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FINANCIAL INSTITUTIONS AND SERVICES
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REAL ESTATE INVESTMENT MARKET
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FINANCIAL INSTITUTIONS AND SERVICES
REAL ESTATE INVESTMENT MARKET
SOFIA M. LOMBARDI
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
EDITOR
Nova Science Publishers, Inc.
New York
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LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA
Real estate investment market / editor, Sofia M. Lombardi.
p. cm.
Includes index.
ISBN: (eBook)
1. Real estate investment. 2. Housing--Finance. 3. Mortgage loans. I.
Lombardi, Sofia M.
HD1382.5.R385 2010
332.63'24--dc22
2010015621
Published by Nova Science Publishers, Inc.
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New York
CONTENTS
Preface
Chapter 1
Chapter 2
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
vii
Addressing the Ongoing Crisis in the Housing
and Financial Markets
Douglas W. Elmendorf
1
Value Versus Growth Real Estate Investment Strategy: Is the Win a
Flash in the Pan?
Kwame Addae-Dapaah, Hin/David Kim Ho, and Yan Fen Tan
31
Restructuring Real Estate Market Information Management
to Facilitate Land-Based Investment Activities in Ghana
Raymond T. Abdulai and Felix N. Hammond
75
Investment Characteristics of Housing Market: Focusing
on the Stickiness of Housing Rent
Chihiro Shimizu
105
Fannie Mae and Freddie Mac: Changes to the Regulation of Their
Mortgage Portfolios
N. Eric Weiss
127
Overview of the Securities Act of 1933 as Applied to Private Label
Mortgage-Backed Securities
Kathleen Ann Ruane
139
Examining the Continuing Crisis in Residential Foreclosures and
the Emerging Commercial Real Estate Crisis: Perspectives from
Atlanta
Jon D. Greenlee
Short Communication: Diversification in Listed Real
Estate Investment Fund Reporting in South Africa
Valmond Ghyoot
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151
161
Contents
vi
Chapter 9
Chapter 10
Should Banking Powers Expand into Real Estate Brokerage
and Management?
Walter W. Eubanks
Emerging Economies and Secondary Mortgage Markets
Raymond T. Abdulai and Frank Gyamfi-Yeboa
169
177
181
Index
183
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Chapter Sources
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Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
PREFACE
The turmoil in the international financial markets since the subprime loan crisis has had a
significant effect on the real-estate investment market around the globe. This suggests that the
real estate investment market is becoming part of the financial market. This book reviews
current data on real-estate investing including topics such as the investment characteristics of
the housing market; real estate markets in developing sub-Saharan Africa; ascertaining
whether the superiority of "value" over "growth" real estate investment is unsustainable;
emerging economies and secondary mortgage markets; a CBO report on the ongoing crisis in
the housing and financial markets; changes to the regulation of Fannie Mae and Freddie Mac;
an overview of the Securities Act of 1933 as it applies to private label mortgage-backed
securities and others.
Chapter 1- Chairman Conrad, Senator Gregg, and Members of the Committee, I welcome
the opportunity to discuss the turmoil in our nation’s housing and financial markets and some
options for additional action by policymakers.
A strong financial sector is a necessary component of a robust economy. Financial
markets and institutions channel funds from savers to borrowers who need the money to build
businesses and hire workers and to buy homes and other goods and services. Indeed, credit is
often required to support the ordinary operations of businesses—for example, to finance their
inventories and to meet payrolls before payments are received. If the customary means of
obtaining credit break down, the disruption to households’ and businesses’ spending can be
severe.
Thus, the ongoing crisis in the U.S. financial system has significantly depressed economic activity during the past year and a half, and it poses a serious threat to the nation’s
ability to quickly return to a path of solid economic growth. Losses on mortgages, on assets
backed by mortgages, and on other loans to consumers and businesses, together with an
associated pullback from risk taking in many credit markets, have raised the cost and reduced
the availability of credit for borrowers whose credit ratings are less than the very highest. To
be sure, among the fundamental causes of the crisis was the provision of too much credit at
too low a price as well as insufficient capital. However, the sudden shift to a much higher
price for risk taking has led to a significant reduction in wealth and borrowing capacity; it has
also forced a number of financial institutions to close and others to be merged with stronger
operations. Those forces, in turn, are weighing heavily on consumption, the demand for
housing, and businesses’ investment.
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Sofia M. Lombardi
Chapter 2- The superiority of the contrarian investment strategy, though well attested in
the finance literature, is being questioned in some quarters on the pretext that the gap between
the performance of value and growth investment narrows over time. If this is proven to be
true, it would imply that value real estate investment may not be advisable given that real
estate is a medium to long term investment. This paper uses empirical real estate investment
return data from 1985Q1 to 2005Q3 for US, and some Asia Pacific cities to ascertain whether
the superiority of “value” over “growth” real estate investment is a “flash” in the pan, i.e.
unsustainable. The office, industrial and retail property investments are examined in the
context of the value-growth paradigm, and complemented with mean reversion and stochastic
dominance tests. In addition to confirming the relative superiority of “value” over “growth”
property investment, the results show that office and industrial property investments exhibit
return reversal. This implies that the “win” is sustainable. Although the returns from retail
property investment display inertia, the results of stochastic dominance test validate the
relative superiority of “value” over “growth” property investment for all the three sectors.
This implies that fund managers who traditionally have been favoring prime (i.e. growth)
property investment may have to reconsider their investment strategy if they want to
maximize their return.
Chapter 3- Real estate is so important a subject that it cannot be left out any serious
macroeconomic deliberation and the collective quest for investment, wealth creation, poverty
alleviation and economic development. This is amply demonstrated by the negative effects
that the current real estate market downturn is having on every facet of the economies of rich
nations. The role played by, especially, private real estate in the economic development of the
advanced world is well documented. The importance of well established real estate markets
that operate efficiently cannot, therefore, be over-emphasised. One area that has and
continues to dominate discussions relates to how real estate market information should be
organized and managed to guide participants in the markets to make efficient purchase, sale
and investment decisions. It is often the responsibility of the state to organize and manage real
estate market information through implementation of land registration programmes. In Ghana,
despite 126 years of unbroken history of implementing land registration programmes, it is
estimated that only 8% of real estate ownership has been registered. It is important to properly
comprehend this problem and its fundamental causes in order to proffer the appropriate
remedies. Using the quantitative research methodology, this study seeks to offer explanations
of the large lag in land registration in Ghana. It has been established that the fundamental root
cause of the problem is the fact that the operation of Ghanaian state agencies that are
responsible for the organization, management and dissemination of real estate market
information is not based on clear economic principles. As a starting point, it is recommended
that a nationwide timed-bound real estate ownership census akin to the survey conducted in
Britain that resulted in the Domesday Book of 1086 be carried out and it should be financed
by the government. From then onwards, it should be in the interest of the state to ensure that
every real estate ownership or transaction is recorded by instituting an incentive package that
would attract people to register; after all, such information would be sold to the public at a
price. In this way a viable real estate ownership information system would be created, which
would enable the real estate market to operate efficiently.
Chapter 4- The turmoil in the international financial market since the subprime loan crisis
has had a significant effect on the real-estate investment market in Japan, particularly the
Japan real-estate investment trust (J-REIT) market. This suggests that the real-estate
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Preface
ix
investment market is becoming part of the financial market. It is necessary to precisely
understand the mechanism of risk generation and cash flow in the real-estate market to
understand the characteristics of the real-estate investment market.
The purpose of this study is to statistically clarify the characteristics of the five problems
that have been recently pointed out as risk factors in the real-estate investment market for
housing. Specifically, we have attempted to clarify the following five intrinsic problems,
which are considered to be characteristics of the housing market: 1) the return problem, 2) the
small-scale investment problem, 3) the risk associated with the adjustment of rent, 4) the key
tenant problem, and 5) the inflation problem, all of which have been pointed out to be
problems in the housing and commercial property markets.
Regarding the risk associated with the adjustment of rent, we investigated the actual
situation in the housing market by considering the decrease in housing rent with the age of the
building and the adjustment of housing rent when a new contract is concluded between a
landlord and a new tenant. The results indicated that the yearly rate of decrease in housing
rent for nontimbered houses is as high as approximately 6% over the first five years after
construction, but decreases to 2.6% over the 5th to 10th years and 2.5% over the 10th to 20th
years, indicating that the long-term rate of decrease in housing rent is small. The probability
of no change in rent was converted to a yearly value of 0.6585, which means that the revenue
from the housing rent of 65% of leasehold properties does not change. This result revealed
that housing rent in the Japanese market is extremely sticky compared with that in the US.
Regarding the risk associated with the adjustment of rent, the probability of downward
adjustment of the housing rent should be considered; however, in most cases, the housing rent
is left unchanged. Even when the housing rent is adjusted downward, decreases of more than
10% comprised only 11.2% of all the adjustments. Also note that the occurrence of rent
adjustment is random with respect to time; the housing rent market is not strongly affected by
the economic environment, in contrast to the market for office buildings; a turnover of
residents occurs because of events such as marriage, childbirth, and relocation, regardless of
the economic cycle, causing the housing rent to change.
Chapter 5- This chapter analyzes the costs and benefits of the Fannie Mae’s and Freddie
Mac’s retained portfolios while they remain under conservatorship.
Increasing numbers of homeowners are threatened with foreclosure because of interest
rate resets on subprime mortgages, combined with stagnant or falling home prices. Congress
responded to this situation by passing the Housing and Economic Recovery Act of 2008
(H.R. 3221, P.L. 110-289), which uses the congressionally chartered, stockholder-owned
government-sponsored enterprises (GSEs), Fannie Mae and Freddie Mac, to lead the market
in providing more affordable mortgages.
The GSEs have retained mortgage portfolios with a combined value of more than $1.4
trillion. The size of the portfolios, past management problems, risks to the financial system,
and potential cost to the taxpayer led, in part, to provisions of the Housing and Economic
Recovery Act that changed the rules governing the activities and regulation of Fannie Mae
and Freddie Mac. The bill created the Federal Housing Finance Agency (FHFA) and
authorized it to regulate the size of the GSEs’ retained mortgage portfolios; it also raised the
conforming loan limit in certain high-cost areas, thereby allowing the GSEs to purchase larger
mortgages in these areas.
Previous regulatory actions have affected the GSEs’ portfolios. In 2006, following
discovery of accounting and management problems, the GSEs agreed to restrictions on their
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Sofia M. Lombardi
retained portfolios. In 2007, the Office of Federal Housing Enterprise Oversight (OFHEO),
now the Federal Housing Finance Agency (FHFA), denied requests from both Fannie and
Freddie to raise or eliminate the caps, but these restrictions were relaxed shortly afterwards.
On September 6, 2008, the GSEs were placed in conservatorship (government management).
One condition of the conservatorship set the portfolio limit to $850 billion as of December
2009, with a 10% yearly decline until the portfolios reach $250 billion.
The GSEs’ portfolios include mortgages and mortgage-backed securities (MBS) that are
subject to financial risks. When these risks are not managed properly, or if market movements
turn dramatically against the GSEs, the government faces two unsatisfactoryalternatives:
eitherlet the GSEs go into default and work to control the financial repercussions, or step in
and assume payments on the GSEs’ debt at a significant cost to taxpayers. The GSEs and
their supporters argue that the profits generated by the investment portfolios enhanced the
GSEs’ ability to support affordable housing programs and reduce mortgage interest rates.
Chapter 6- Mortgage-backed securities that are packaged and issued by private industry
participants are required to comply with the Securities Act of 1933. Issuers of so-called
private label mortgage-backed securities must either register these securities pursuant to the
rules the Securities and Exchange Commission has set forth, or obtain an exemption from
registration. Failure to register or fall under an exemption could result in liability for the
issuer and other parties involved in the offering. Furthermore, material misstatements or
omissions in the offering materials may also result in liability under the Securities Act. This
chapter will provide an overview of the Securities Act of 1933 as it may be applied to
mortgage-backed securities issued by private industry participants.
Chapter 7- Chairman Kucinich, Ranking Member Jordan, and members of the
Subcommittee, I appreciate the opportunity to appear before you today to examine several
issues related to the condition of the banking system. First, I will discuss credit conditions and
bank underwriting standards, with a particular focus on commercial real estate (CRE), and I
will briefly address conditions in the state of Georgia. I will then describe Federal Reserve
activities to enhance liquidity and improve conditions in financial markets. Finally, I will
discuss the ongoing efforts of the Federal Reserve to ensure the overall safety and soundness
of the banking system, as well as actions taken to promote credit availability.
Chapter 8- The study set out to determine the extent to which diversification, as promoted
in the financial literature, is actually implemented by institutional investors in South Africa.
Diversification theory is encapsulated in a conceptual model of potential diversification
strategies. The universe of listed real estate investment trusts in South Africa (Property Unit
Trusts and Property Loan Stock Companies) was evaluated in 2004 and the study was
updated in 2009. Content analysis was used to compare the conceptual model of potential
diversification strategies with the annual reports of the listed real estate investment funds. The
study finds that in 2004 few of the available diversification strategies were reported on. By
2009, reporting was more comprehensive. The study also explores focused strategies as an
alternative to diversification.
Chapter 9- In late 2000, the Federal Reserve and the Treasury proposed to increase
banking powers. They proposed allowing banking companies to engage in real estate
brokerage and management, as activities that are financial in nature. The substantiative issues
are the respective nature of banking and of real estate activities and the potential impact on
consumers. Procedural questions involve the intent of Congress in P.L. 106-102, which
delegated authority to both agencies to issue new regulations. The reintroduced Community
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Preface
xi
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Choice in Real Estate Act, H.R. 111/S. 413, 110th Congress, would permanently remove
these real estate activities from consideration under the marketadaptive powers of the
regulators. In the mean time, Treasury spending bills have forestalled any such regulations for
six fiscal years, most recently in P.L. 110-5.
Chapter 10- Access to long-term credit remains one of the major obstacles to solving the
perennial housing problems in many emerging economies. These countries have been making
serious attempts at developing their mortgage markets in recent times. There is a general
consensus on the need for emerging economies to develop housing finance systems that
would ensure easy, affordable and sustainable accessibility to credit. The exact nature and the
elements of such a system are still subject to debate. In this commentary, we argue for the
institution of secondary mortgage markets but recommend the use of mortgage credit
institutions in the short to medium term.
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In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 1-30
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 1
ADDRESSING THE ONGOING CRISIS IN THE HOUSING
AND FINANCIAL MARKETS
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Douglas W. Elmendorf
Chairman Conrad, Senator Gregg, and Members of the Committee, I welcome the
opportunity to discuss the turmoil in our nation’s housing and financial markets and some
options for additional action by policymakers.
A strong financial sector is a necessary component of a robust economy. Financial
markets and institutions channel funds from savers to borrowers who need the money to build
businesses and hire workers and to buy homes and other goods and services. Indeed, credit is
often required to support the ordinary operations of businesses—for example, to finance their
inventories and to meet payrolls before payments are received. If the customary means of
obtaining credit break down, the disruption to households’ and businesses’ spending can be
severe.
Thus, the ongoing crisis in the U.S. financial system has significantly depressed economic activity during the past year and a half, and it poses a serious threat to the nation’s
ability to quickly return to a path of solid economic growth. Losses on mortgages, on assets
backed by mortgages, and on other loans to consumers and businesses, together with an
associated pullback from risk taking in many credit markets, have raised the cost and reduced
the availability of credit for borrowers whose credit ratings are less than the very highest. To
be sure, among the fundamental causes of the crisis was the provision of too much credit at
too low a price as well as insufficient capital. However, the sudden shift to a much higher
price for risk taking has led to a significant reduction in wealth and borrowing capacity; it has
also forced a number of financial institutions to close and others to be merged with stronger
operations. Those forces, in turn, are weighing heavily on consumption, the demand for
housing, and businesses’ investment.
Policymakers have responded to the turmoil with a set of unprecedented actions. Thus
far, a systemic collapse of the financial system has not occurred, and conditions have
improved noticeably in some financial markets. Nevertheless, according to some analysts,
U.S. banks and thrift institutions could be facing more than $450 billion in additional
estimated losses on their assets—on top of the approximately $500 billion that has already
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2
Douglas W. Elmendorf
been recognized. The scale of those losses suggests that many financial institutions and
markets will remain deeply troubled for some time, which will keep borrowing exceptionally
costly for many borrowers and thereby dampen spending by households and businesses.
Challenging conditions seem likely to persist for some time in the housing and mortgage
markets as well. Housing sales remain weak, and construction activity continues to decline.
With the housing market’s large glut of vacant properties, the prices of homes are likely to
fall considerably further, pushing the value of more borrowers’ homes below the value of
their outstanding mortgages. As more of those “underwater” borrowers experience losses of
income during the current recession, rates of delinquency and foreclosure on residential
mortgage loans are likely to rise further.
A crucial and challenging question for policymakers is, What further actions can be taken
to normalize the financial and housing markets so as to spur economic activity? A separate
but equally important question—though not one considered in this testimony—is, What can
policymakers do to reduce the risk of a financial crisis in the future?
I will make four major points in this testimony:
•
•
•
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•
Turmoil in the housing and financial markets is likely to continue for some time,
even with vigorous policy actions and especially without them.
Most economists think that to generate a strong economic recovery in the next few
years, further actions to restore the health of the housing sector and the financial
system are needed.
An effective policy to ensure the availability of credit for qualified borrowers probably requires a multifaceted strategy that uses a range of tools to address the different
aspects of financial distress.
The costs to federal taxpayers of actions to reduce mortgage foreclosures and
improve financial conditions are highly uncertain and may be large, but the economic
consequences of doing nothing may be even greater.
THE ECONOMY’S CONTINUING FINANCIAL PROBLEMS
The vigorous monetary and financial policy actions of the past year and a half represent a
graduated response to the unfolding crisis.1 When the first signs of financial turmoil emerged,
it was not clear either to policymakers or to most other observers just how serious the crisis
would become. The Federal Reserve first began to supply additional liquidity to credit
markets in August 2007 as pressures from losses on mortgage-related assets unexpectedly
began to mount. In the following year and a half, the central bank greatly increased the funds
it was providing by creating a number of new lending facilities to address emerging problems
among financial institutions and in certain markets (such as those for commercial paper,
money market mutual funds, and mortgages). It also expanded arrangements (known as
currency swaps) to provide U.S. dollars to a number of foreign central banks and slashed the
federal funds rate, which banks charge each other for overnight loans of their monetary
reserves, almost to zero by late last year.
1
Tables 1 through 3 on page 26 describe those actions in more detail.
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Addressing the Ongoing Crisis in the Housing and Financial Markets
3
Policymakers also took a series of significant steps to prevent the problems with solvency
that a number of major financial institutions were experiencing from further destabilizing
markets.
•
•
•
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•
•
•
The Federal Reserve, in consultation with the Department of the Treasury, facilitated
the sale of the investment bank Bear Stearns to the commercial bank JPMorgan
Chase, in March 2008, by lending $29 billion to a newly formed limited liability
company (LLC), Maiden Lane, against a $30 billion portfolio of Bear Stearns’s less
liquid assets. (An LLC, like a corporation, offers protection from personal liability
for debts incurred by a business.)
The Federal Housing Finance Agency (FHFA)—the regulator of Fannie Mae,
Freddie Mac, and the 12 Federal Home Loan Banks—put Fannie Mae and Freddie
Mac into conservatorship, and the Treasury provided an initial pledge to inject up to
$100 billion of capital into each of the institutions by purchasing an equity share, or
ownership interest, in each company.2
The Federal Reserve extended a $60 billion line of credit to the insurance company
American International Group (AIG). Additionally, the Federal Reserve Bank of
New York arranged to lend up to $52.5 billion to two newly formed LLCs to fund
purchases of residential mortgage-backed securities and collateralized debt obligations from AIG’s securities portfolio.
The Emergency Economic Stabilization Act of 2008 (Division A of Public Law 110343) created the $700 billion Troubled Asset Relief Program (TARP), which began
purchasing preferred stock of commercial banks in late October. (Preferred stock
refers to shares of equity that provide a specific dividend to be paid before any
dividends are paid to common stockholders and that take precedence over common
stock in the event of a liquidation.) The law also temporarily raised the ceiling on
deposit insurance from $100,000 to $250,000 per depositor.
The Treasury, the Federal Reserve, and the Federal Deposit Insurance Corporation
(FDIC) jointly announced agreements with Citigroup and Bank of America to
provide each with a package of asset guarantees, access to liquidity, and capital.
The FDIC created the Temporary Liquidity Guarantee Program in October 2008 to
strengthen confidence and encourage liquidity in the banking system. The program
guarantees certain newly issued unsecured debt of banks, thrift institutions, and
certain holding companies and provides full deposit insurance coverage for certain
checking and non-interest-bearing deposit accounts, regardless of dollar amount.
The actions mentioned above have improved conditions in some financial markets and
thus far reduced the risk of a financial meltdown. The interbank market for short-term loans,
which had virtually seized up, has improved markedly in recent months, as indicated by the
spread, or difference, between the interest rates banks pay to borrow from each other and their
expectations about the federal funds rate. (The spread reflects the risk that banks will not
2
Fannie Mae and Freddie Mac were originally created as federally chartered institutions but were privately owned
and operated. Designed to facilitate the flow of investment funds, they pool mortgages purchased from
mortgage lenders and sell them as mortgage-backed securities, collecting annual guarantee fees on the
mortgages they securitize. Conservatorship is the legal process in which an entity is appointed to establish
control and oversight of a company to put it in a sound and solvent condition.
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4
Douglas W. Elmendorf
repay the loan.) That spread can be measured by the difference between the key interbank
lending rate, the three-month Libor (the London interbank offered rate), and the average
expected federal funds rate over the next three months.3 The spread has fallen to about 1
percentage point, which is roughly where it was before the failure of the investment bank
Lehman Brothers (though still well above its historical norm) and well below its peak of 3.6
percentage points in October 2008. Transactions in the interbank market for short-term loans
have picked up, and loans are being extended to somewhat longer terms than those seen
recently, signaling that the crisis of confidence among financial institutions is continuing to
ease.
Conditions have also improved in the market for commercial paper, as indicated by a
smaller spread between the interest rate on commercial paper and the rate on three-month
Treasury bills. (Commercial paper is a kind of short-term borrowing that provides credit to
financial and nonfinancial firms.) The spreads for commercial paper that represents higherquality credit have fallen substantially; in the case of paper with the highest credit rating,
spreads have returned to the levels observed before September 2007—that is, before the
financial crisis began to emerge. Moreover, the amount of commercial paper issued by
financial firms has mostly recovered after a sharp decline last fall (the amount of nonfinancial
commercial paper has changed little during the crisis).Those improvements, however, do not
imply that private lending has returned to normal; rather, the Federal Reserve has provided
extensive financial support to this market, particularly for paper that carries longer maturities,
whose spreads remain elevated. Indeed, the amount of outstanding asset-backed commercial
paper has yet to recover from the sharp drop that occurred in September 2007, and markets
for lower-quality commercial paper no longer extend beyond a 90-day maturity.
Credit difficulties are much more severe for companies with low credit ratings. Firms’
issuance of investment-grade (high-quality) debt was robust in the fourth quarter of 2008, and
the interest rates that AAA-rated firms—those with the highest credit ratings—are paying to
borrow money are 2 percentage points lower than at the height of the crisis, in October. (The
spread of the AAA rate over the interest rate on 10-year Treasury notes nevertheless reached
historic highs at the end of last year, indicating that the convulsions in financial markets and
the recession have affected the cost of credit even for firms with the highest credit rating.)
Conditions are more difficult for firms that have lower credit ratings—there has been little
issuance of below-investment-grade debt. In addition, spreads on junk bonds have widened
since September 2008, in part reflecting the difficulties that continue to beset the economy.
Although some financial conditions have improved significantly since September and
October of last year, the flow of credit from banks remains constricted. A recent study
showed that apart from preexisting lines of credit, bank lending to large borrowers dropped
sharply during the September-to-November period.4 Moreover, the senior loan officer opinion
survey conducted by the Federal Reserve in October 2008 shows that banks continued to
tighten lending standards and terms in the third quarter of 2008. About 80 percent of large
banks tightened lending standards for commercial and industrial loans, an important source of
3
The Federal Reserve attempts to achieve a target value of the federal funds rate in its conduct of monetary policy.
The expected federal funds rate is measured by the overnight index swap contract.
4
Victoria Ivanova and David Scharfstein, “Bank Lending During the Financial Crisis of 2008” (working paper,
Harvard Business School, December 15, 2008).
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5
credit for investment.5 In addition to their applying more rigorous standards for borrowers to
qualify for such loans, more than 90 percent of banks (on net) raised their interest rates on
commercial and industrial loans relative to their cost of funds.
Lending standards for mortgages have tightened as well, with 100 percent of the
respondents in the Federal Reserve’s October survey saying they were applying more
rigorous standards to subprime loans (loans made to borrowers with low credit scores or other
impairments to their credit histories), 90 percent saying they had tightened standards on
nontraditional mortgages (such as alt-A loans, which are riskier than prime loans), and 70
percent reporting having tightened standards for prime borrowers (those considered least at
risk of default). In light of the past excesses in mortgage lending, some tightening in
standards had been expected. Since October, interest rates on jumbo mortgages (generally
loans of more than $417,000) and on conventional 30-year mortgages have fallen, but the
spreads between those rates and the interest rate on 10-year Treasury notes rose. Those
spreads fell in January, however, due in part to the Federal Reserve’s actions to support the
mortgage market (discussed later).
Lenders have also tightened standards and terms for consumer loans. In the third quarter
of 2008, 58 percent of respondents to the Federal Reserve’s survey reported tightening
standards on credit cards, compared with 67 percent reporting such tightening in the second
quarter. Interest rates on credit cards have begun to move down modestly over the past
several months, but given the much lower Libor rates, the interest rate spread has, in fact,
widened.
Tighter standards for lending, declines in employment, and a large drop in consumer
confidence have contributed to a marked slowing in the growth of consumer credit. By
November 2008, the amount of consumer credit had grown by only 2¼ percent relative to the
amount in November 2007, compared with growth of more than 5½ percent in the previous
year. Much of the slowdown in growth in the past year occurred after July 2008, when the
financial turmoil began to intensify.
Continuing declines in house prices and the ongoing recession are likely to worsen the
financial condition of banks. Delinquency rates on residential mortgage loans continued to
rise through the third quarter of last year (the latest available data), and foreclosure rates have
remained high. Delinquency rates on commercial real estate loans and consumer installment
loans at commercial banks have also risen sharply over the same time span. According to the
latest compilation by the Bloomberg financial information network, financial institutions
worldwide have recognized losses of about $1 trillion since the third quarter of 2007,
primarily because they held securities based on residential real estate. Analysts with Goldman
Sachs estimate that banks worldwide are likely to experience about another $1 trillion in
losses on residential mortgages, loans for commercial real estate, credit cards, auto loans,
commercial and industrial loans, and corporate bonds.6
In an attempt to deal with such losses, financial institutions have been reducing their
leverage—that is, their use of borrowed funds—by holding a greater amount of capital in
relation to their assets. In 2007 and early 2008, many banks seemed to have little difficulty in
deleveraging because they could obtain additional capital from private sources through
5
6
Board of Governors of the Federal Reserve System, The October 2008 Senior Loan Officer Opinion Survey on
Bank Lending Practices (November 2008).
See Jan Hatzius and Michael Marschoun, Home Prices and Credit Losses: Projections and Policy Options,
Goldman Sachs Global Economics Paper 177 (New York: Goldman Sachs, January 2009).
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Douglas W. Elmendorf
offerings of common and preferred stock. As the solvency of more and more financial
institutions has been tested, however, those private sources of capital appear to have dried
up.7 (Another way to increase capital would be to cut dividends, but most banks are reluctant
to do so because that could deter new and existing shareholders from holding the stock.) The
interventions by the Treasury and the Federal Reserve in the past several months have been
largely directed toward counteracting the contraction of credit that results from banks’
deleveraging. The Federal Reserve, through its holdings of assets and by direct lending (for
example, in the commercial paper market), has provided credit that private institutions
previously would have provided. In addition, most of the first half of the $700 billion in
TARP funding has been used to supply banks directly with capital.
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THE NEED FOR A MULTIFACETED STRATEGY TO ADDRESS THE
FINANCIAL CRISIS
Economists and financial experts widely agree that the financial markets are likely to
remain severely stressed for some time and that additional action is desirable now to promote
their recovery and hence the economy’s return to more vigorous growth. With the economy
weakening, losses on loans are likely to continue to deplete the capital of financial institutions
for the foreseeable future. Such conditions raise the prospect of a vicious cycle of loan losses,
leading to further reductions in the availability of credit, weaker economic activity, more loan
losses, and so on. Stimulus from fiscal policies can strengthen the economy and, as a result,
complement policies directed specifically at strengthening the financial sector.
Many analysts agree that a broader, clearer strategy is necessary to help return the flow of
credit to a more normal state and support the recovery of overall economic activity. Some
critics of the actions taken to date say those interventions have been confusing to markets and
have given the impression that the government is “playing favorites” (because different forms
and amounts of support have been given to different financial institutions).8 Private investors
are chary of providing capital to banks in part because of uncertainty about banks’ financial
positions and future government actions. Moreover, banks may be postponing actions to
resolve their financial problems in anticipation of receiving additional support from the
government. Therefore, one advantage of a more clearly enunciated strategy would be that
financial markets would be more certain about future policy steps.
7
8
Because new capital would largely help to shore up balance sheets, new investors would expect existing
shareholders to accept a dilution of their ownership. Existing shareholders would rather take the gamble of not
raising new capital than suffer an immediate reduction in wealth. Economists refer to that reluctance of
distressed firms to raise equity capital as a “debt overhang” problem.
See Shadow Financial Regulatory Committee, An Open Letter to President-Elect Obama, Statement No. 264
(Washington, D.C.: American Enterprise Institute, December 8, 2008), available at www.aei.org/
docLib/20081208_StatementNo.264.pdf; and Luigi Zingales, “Yes We Can, Mr. Geithner,” available at
www.voxeu.org/index.php?q=node/2807.
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Principles for Crafting a Strategy
Several principles can be used to craft a sound strategy for further assisting the recovery
of the financial markets:
•
•
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•
•
•
•
•
Effective strategies would have some degree of flexibility so actions can be adjusted
to changing and unexpected circumstances. There is enormous uncertainty not only
about the future course of this crisis but also about its impact on economic activity,
the degree of success that might be expected from different policy actions, and the
amount of resources to devote to those actions. A degree of flexibility would allow
policymakers the leeway to shift gears so as to regain traction in a crisis that might
continue to unfold in unexpected ways. Flexibility that is governed by principles that
are understood by the private sector could reduce uncertainty about the government’s
interventions, which can freeze actions by the private sector.
A sound strategy would determine an appropriate price for the assistance given to
financial institutions. Such pricing should give financial institutions an incentive to
solve their problems on their own if they are in a position to do so and should mean
shuttering institutions that have little prospect of recovery. Underpricing the support
would profit creditors, executives, or workers in the financial system at the expense
of taxpayers. As a result, it would increase the likelihood that they would continue to
take excessive risks in the future or become too large and important an institution to
be allowed to fail (a phenomenon known as moral hazard). However, overcharging
would delay the system’s and the economy’s recovery.
An effective strategy would encourage the participation of private capital. Having a
role for private capital is important both because the government cannot provide
enough money itself and because private market signals regarding the long-term
viability of specific institutions can be valuable. Encouraging private capital means
not only that the strategy must provide clear guidance, but also that it must avoid as
much as possible a lack of clarity and especially incentives that encourage private
capital to sit on the sidelines and wait for government to act.
As the financial system is rebuilt, private creditors will have to take some losses; and
some banks may have to fail: It is neither necessary nor desirable for government to
take on all the losses from bad assets.
An efficient strategy would distort the supply of credit as little as possible. Distortions could arise, for example, if policies picked winners and losers—that is, if they
treated financial institutions in similar circumstances differently or focused on certain
types of credit at the expense of others with similar needs.
A sound strategy would coordinate the activities of government agencies (including
the Federal Reserve, the Treasury, the FDIC, the FHFA, and the Securities and
Exchange Commission) to avoid overlapping actions and initiatives that operate at
cross purposes.
A successful strategy would be implemented quickly to reduce the chances of a
vicious cycle of losses on loans, reductions in the availability of credit, weaker economic activity, more loan losses, and so on.
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Evidence from Other Crises
Previous financial crises in the United States and other countries highlight the risks and
greater costs that come from implementing only partial measures in the hope that time and
economic growth will quickly resolve problems in the banking system.
The savings and loan crisis in the United States in the late 1980s illustrates the costs of
delaying action. The ultimate cost to taxpayers for cleaning up the thrift crisis was estimated
to be about 2 percent of gross domestic product (GDP), and an analysis by the Congressional
Budget Office (CBO) found that delays in closing and resolving insolvent thrifts doubled the
costs to taxpayers.9,10 At the time of the crisis, some regulators thought that the problems
facing the thrift institutions were temporary and that, given time, the institutions could be
restored to solvency through the profits gained in their operations and a recovery in the value
of their assets. In effect, though, that forbearance by regulators led many insolvent institutions
to take greater risks in the hope of becoming solvent, a phenomenon known as “gambling for
resurrection.” Because most of their deposits were federally insured, the institutions could
acquire additional funds to make speculative investments by offering somewhat higher interest rates than solvent institutions had to pay. In the end, the costs to taxpayers spiraled,
eventually resulting in the Financial Institutions Reform, Recovery, and Enforcement Act of
1989 and the creation of the Resolution Trust Company.
Financial conditions in Japan in the 1990s were probably closer to current conditions in
the United States. In the 1980s, Japan experienced both stock market and real estate bubbles
that by 1992 had burst. Initially, Japanese authorities encouraged the formation of private
asset management companies that would purchase troubled assets from banks, but as the
financial problems deepened, public funds were also used to purchase assets. In 1997, a credit
crisis began with the bankruptcy of a major bank and a securities firm. Like the United States,
Japan faced highly elevated interbank lending rates after those events, reflecting a lack of
confidence in its financial institutions. In the midst of the crisis, the government changed the
accounting rules governing banks’ financial statements, allowing banks to choose whether to
value assets at their historical book value or to use “mark-to-market” accounting.11 As a
result, Japanese banks could report earnings using the accounting method that was more
favorable to them. Using the book value of assets also gave Japanese banks an incentive to
offer additional credit to troubled borrowers rather than to healthier firms “to avoid the
realization of losses on their own balance sheets.”12 Furthermore, as was the case during the
U.S. thrift crisis, regulators allowed and even encouraged the practice of forbearance.
By late 2002, Japan had finally begun to address the problems caused by forbearance, and
its regulators were pressuring banks to improve their balance sheets. Japan’s financial sector
improved, but whether the more effective regulation of banks or the global economic boom
9
Timothy Curry and Lynn Shibut, “Costs of the Savings and Loan Crisis: Truth and Consequences,” FDIC Banking
Review (2000).
10
Congressional Budget Office, The Cost of Forbearance During the Thrift Crisis (June 1991). Note that the costs
cited for resolving previous financial crises are generally stated in cash terms.
11
Takeo Hoshi and Anil Kashyap, Will the U.S. Bank Recapitalization Succeed? Lessons From Japan, NBER
Working Paper 14401 (Cambridge, Mass.: National Bureau of Economic Research, December 2008).
12
Joe Peek and Eric Rosengren, “Unnatural Selection: Perverse Incentives and the Misallocation of Credit in
Japan,” American Economic Review, vol. 95 (2005).
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that began in 2003 had the greater impact is difficult to determine. The costs associated with
the rescue of Japan’s financial system have been estimated at about 25 percent of GDP.13
The lack of support provided to the financial system during the Great Depression differed
from the policy of forbearance that characterized the U.S. thrift crisis and Japan’s financial
turmoil, and some economists view that laissez-faire approach to the widespread bank failures
that occurred during the Depression as an example of being too strict. According to Federal
Reserve Chairman Ben Bernanke, policymakers thought at the time that “to weed out weak
banks was a harsh but necessary prerequisite to the recovery of the banking system.”14 By
contrast, in Bernanke’s view, the Federal Reserve should have increased the monetary base
either by supplying more funds to banks or by increasing the currency in circulation to limit
the adverse effects of bank failures on borrowers and depositors. Those funds could then have
been used to pay off depositors and curtail runs on banks.
A more successful outcome emerged from the response of Swedish policymakers to the
financial crisis of 1992, which came on the heels of a speculative bubble in Swedish real
estate. By 1991, the cost of the reunification of Germany had caused interest rates in Europe
to increase sharply. In addition, international growth was slowed by a recession in the United
States, and the combination of those factors led Sweden’s real estate bubble to burst. The
steep decline in the value of real estate in turn impaired the value of the assets held by many
Swedish banks. The crisis was exacerbated by attempts to defend Sweden’s currency:
Sweden’s central bank, the Riksbank, let overnight rates rise as high as 500 percent to prevent
the outflow of the Swedish currency. In that difficult environment, Sweden’s economy fell
into recession, and banks’ losses increased rapidly.
The Swedish government insisted that banks value loans and assets on their balance
sheets using mark-to-market accounting standards.15 Under those rules, the values prevalent
in the financial markets were applied, even though many participants believed that the current
conditions in those markets temporarily understated the values of the assets. That policy led
to large losses for the banks, but authorities considered such a policy necessary to restore
confidence in the financial system. After the banks’ assets were marked to market, banks
identified as having good prospects for surviving were helped, and the rest were either
merged with stronger banks or liquidated.
As was the case in the savings and loan crisis in the United States, Sweden formed assetmanagement companies to deal with the assets from the liquidated banks. No measures were
adopted to support nonfinancial companies, and the number of bankruptcies rose markedly.
Sweden placed limits on the Riksbank in its dealings with the banks, basically allowing the
central bank only to provide liquidity and not to take risks with taxpayers’ funds. Sweden’s
financial sector began to recover about a year after the crisis reached its peak, in late 1993, at
a cost of about 4 percent of its gross national product.
The experiences of previous financial crises highlight the risks to nations’ economies and
the costs to taxpayers when governments delay action to bolster their financial systems in the
13
14
15
See Luc Laeven and Fabian Valencia, Systemic Banking Crises: A New Database, IMF Working Paper
WP/08/224 (Washington, D.C.: International Monetary Fund, November 2008); and Anil Kashyap, “Sorting
Out Japan’s Financial Crisis,” Federal Reserve Bank of Chicago Economic Perspectives, vol. 26 (2002, Fourth
Quarter).
Ben S. Bernanke, “Money, Gold, and the Great Depression” (remarks at the H. Parker Willis Lecture in
Economic Policy, Washington and Lee University, Lexington, Va., March 2, 2004).
See Lars Heikenstein, Deputy Governor, Risbank, “Financial Crisis—Experiences from Sweden” (speech in
Seoul, Korea, July 15, 1998).
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Douglas W. Elmendorf
hope that economic growth will resolve banks’ problems. Of course, some cases could be
cited in which global economic growth has allowed financial systems to recover without
special government action. Yet once the problems of such systems became as severe as in the
United States’ current situation, economists and financial experts generally agreed that
additional action was desirable to promote a system’s recovery. Successful approaches have
entailed forceful action by government authorities to uncover the true financial condition of
each bank, to close banks in the worst shape, and to provide support to banks that appear
viable in the long run.
Possible Elements of a Rescue Strategy
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Several complementary approaches might be used to further assist the recovery of the
financial system. Some extend or continue current interventions; others attack the crisis from
different angles.
Inject more equity into financial institutions
The government could further strengthen the financial system by taking a larger
ownership interest in some financial institutions through the purchase of more equity. That
could be accomplished by continuing the Capital Purchase Program (CPP) under the TARP,
an approach that was widely supported by economists when it began. In the eyes of some
observers, the government’s further purchases of equity in banks would bring the government
closer to nationalizing a major portion of the banking system. However, additional purchases
may be appropriate if conditions in the financial markets worsen.
The main advantage of this approach is that it would provide banks with a greater
capacity to absorb further losses, which would help stabilize the banking system and in so
doing support banks’ lending. Another advantage of injecting equity is that it would maintain
existing channels of borrowing and lending. Such channels cannot be created overnight, and
the use of existing pathways would allow lending to pick up again more quickly.
Some observers have criticized the CPP because they believe that banks that have
received money from the equity purchases have not increased their lending sufficiently. That
criticism is difficult to evaluate because it is very hard to trace the use of particular funds in
large and complex banks, and it is very hard to know what bank lending would have been in
the absence of equity injections by the TARP. In addition, many banks currently have good
reason not to boost lending. To the extent that they need to reduce their own leverage, they
can do that either by lending less or by getting more capital. The government’s capital
injection may thus mean that banks do not have to cut their own lending as much, but that
may not mean they can actually increase lending. Moreover, even without the need to delever,
the slow growth of lending reflects banks’ unwillingness to increase risky lending in the
current recession or a lower demand for borrowing as a result of the slowdown.
A further criticism of the CPP is that it is purchasing equity from banks at very favorable
terms for the banks. The program requires all banks to pay a dividend of 5 percent on the
government’s preferred shares for the first five years—even though banks that have other
outstanding preferred shares currently pay the owners of those shares a higher dividend.
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Moreover, the subsidy that the government’s purchase represents varies by bank, because it
depends on the market’s assessment of the riskiness of investment in the bank.
Injecting more equity into financial institutions raises the risk of propping up banks that
should be allowed to fail. By supporting weak banks, the government may be allowing them
to take excessive risks in hopes of resurrecting themselves. If they are successful, they stay in
business; if they are not successful, their mistakes are paid for by the federal deposit
insurance system or by taxpayers.
Government equity injections are, moreover, unlikely to be sufficient to fill all the capital
needs of banks if they are to provide a level of lending that is sufficient for a growing
economy after the recession ends. Policy therefore needs to be designed to encourage private
investors to supply some of the new capital. A clear, principled policy can reduce the
incentive for private investors to sit on the sidelines, waiting to see how much money the
government will commit and which institutions will be supported.
One possible approach to determining which banks should receive funds and the price
they should pay for them, while at the same time encouraging private participation in
recapitalization, would be to match the government’s equity contributions or loans to private
equity purchases. The involvement of private investors would solve the pricing problem
because they would inject capital into firms only on terms that provided an adequate return on
their investment. Policymakers could require that any injections of public capital be matched
by private investors’ equity purchases and that the dividend rate that banks pay on their new
public capital equal the rate they must pay on the new private capital. In that way, taxpayers
would receive a return on their investment that more closely reflected the risks they were
assuming. However, the management and shareholders of distressed firms are unlikely to
agree to take equity infusions without some federal subsidy because the injection of new
equity capital on market terms usually benefits the firm’s debtholders at the expense of its
shareholders.16
Address troubled assets
The government could facilitate the removal of troubled assets from the balance sheets of
some institutions. Such a removal could clarify the true value of institutions’ balance sheets
by removing the difficult-to-value assets from some institutions and by establishing a market
price that other institutions could use in their own valuations. That step might improve the
solvency of some institutions by establishing a price for troubled assets that exceeds both the
value of those assets on the institutions’ books and the price that investors are currently
willing to pay for them. That would leave those institutions in a better position to raise capital
and make new loans. At the same time, establishing a market price could force some institutions to recognize losses, because of the accounting rule that most assets held for sale must
be marked to market. Moreover, removing troubled assets would allow the managers of
financial institutions to focus on new lending rather than on cleaning up previous mistakes.
One approach that is currently much under discussion is to set up an “aggregator bank”
that would purchase risky assets that are not actively traded from troubled institutions and
then dispose of them, leaving the balance sheets of the banks clean so that they could then
return to lending. That is similar to Sweden’s approach, described earlier.
16
That phenomenon is termed the “debt overhang” problem (see, for example, S. Myers, “Determinants of
Corporate Borrowing,” Journal of Financial Economics, vol. 5 (1977).
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The first problem to be encountered is how much to pay for those assets. Because they
are not actively traded, the assets do not have readily observable market values. Paying too
much would help recapitalize the banks, but it would reward risky behavior and leave
taxpayers with a large bill from the losses on the assets. Erring on the low side— such as by
buying assets at fire-sale prices—would run the risk of forcing banks to mark down the assets
to unrealistically low prices, making more banks insolvent than perhaps needed to be.
As with capital injections, the government could partner with private investors to
determine a market price for asset purchases. In that case, the government could partially
finance (through loans) the purchase of troubled assets by private investors. Because the
investors’ profits would depend on accurate pricing, they would help determine the assets’
fair market prices. The government would not finance the full cost of the purchases so that
private investors would have to put up—in essence, risk—some of their own money for the
transaction. The government could help protect taxpayers’ money in a number of ways: by
requiring that the private investors take losses before the government, by holding the
purchased assets as collateral, or by using recourse arrangements for the loans (essentially
collateralizing the loan with the investors’ other assets). However, without some federal
subsidy, private investors might find few willing sellers of such assets.
Alternatively, instead of buying assets, the government could guarantee portfolios of
assets; that is, provide insurance against some losses on the assets. An asset guarantee would
shift the risk of loss from the financial institutions to the federal government, just as if the
government had taken direct ownership of the troubled assets. With guarantees in place,
financial institutions would more easily borrow and raise capital. Determining the price of the
guarantee would not be easy, and the government could experience large losses if the price
was too low, or fail to attract participation if the price was too high.
Yet another approach, known as “good bank/bad bank,” tries to isolate troubled assets in
a different way. An existing bank that has a large amount of troubled assets is split into two
new banks—one (a “good” bank) with all of the good assets and lending operations and the
other (a “bad” bank) with all of the bad assets.17 Mellon Bank used that approach to deal with
its soured energy and real estate loans (without government support) in 1988, and the Swiss
government used it last year to deal with the problems of the bank UBS. This approach
essentially forces the stockholders and creditors of the bank to absorb the losses from the bad
assets while creating a new bank with a clean, transparent balance sheet that should be able to
borrow and lend in a normal way. In principle, that approach does not require government
funds, although as a practical matter, such funds may well be necessary.
Dividing assets and putting them into separate entities has the advantage of providing
greater clarity and less uncertainty about the financial health of the new good banks than are
offered by the more subtle approaches of guarantees or selective asset purchases.
Consequently, the good banks would be more willing to lend to each other (although there
might be some reluctance if the existing management team remained in place) and more able
to raise new capital from private investors to support new lending. Because this approach
would effectively quarantine the bad bank away from the greater financial system, the
17
Chairman Ben S. Bernanke, “The Crisis and the Policy Response” (the Stamp Lecture, London School of
Economics, January 13, 2009). See also Zingales, “Yes We Can, Mr. Geithner.” Zingales also proposes a
prepackaged bankruptcy option that would allow banks to restructure their debt and restart lending. He
describes that option in fuller detail in “Plan B,” The Economists’ Voice (October 2008), available at
www.bepress.com/ev.
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approach would also allow for a more orderly liquidation of the bad bank’s assets. Such a
process would probably obtain higher prices for the assets than those achievable through a
fire-sale liquidation.
However, even though Mellon Bank managed to split itself into a good and a bad bank in
1988, many securities lawyers are skeptical that similar splits could be accomplished now
without government support or perhaps legislation, because of the competing interests of debt
and equity holders. Those competing interests could come into play because some ways of
accomplishing the split could favor stockholders over creditors by allowing stockholders a
share of profits in the new good bank.
Approaches that inject capital and purchase troubled assets could be used together. The
government could pay market prices for the assets and then help banks cover their losses
through a program of capital injections. In that way, the prices of the assets would not be
distorted, but the banks would receive some assistance. That approach, however, has the
disadvantage of potentially providing the most government capital to the banks that made the
worst business decisions and therefore have the greatest volumes of toxic assets on their
balance sheets.
Provide credit directly
The government could increase its direct lending to consumers, homeowners, and
businesses by expanding existing programs or starting new ones. That approach would
increase the availability and lower the cost of credit for those borrowers.
For example, the Federal Reserve could expand its Term Asset-Backed Securities Loan
Facility (TALF). The TALF is designed to help participants in the market meet the credit
needs of households and small businesses by supporting the issuance of asset-backed
securities that are collateralized by student loans, auto loans, credit card loans, and loans
guaranteed by the Small Business Administration. The TALF is expected to begin lending in
February 2009; at that point, the Federal Reserve Bank of New York will lend up to $200
billion on a nonrecourse basis to holders of certain AAA-rated securities that are backed by
newly and recently originated consumer and small business loans. The Federal Reserve Bank
of New York will lend an amount that is less than the market value of the securities; the loans
will be secured at all times by those securities. The Treasury—under the TARP—will provide
$20 billion of credit protection to the Federal Reserve Bank of New York in connection with
the TALF. The Federal Reserve could expand the TALF by buying securities backed by other
types of assets, such as mortgages on commercial properties.
The Federal Reserve also could expand its Commercial Paper Funding Facility (CPFF),
which is designed to provide a liquidity “backstop” to U.S. issuers of commercial paper. The
CPFF is intended to improve liquidity in short-term funding markets and thereby contribute to
greater availability of credit for businesses and households. Under the CPFF, the Federal
Reserve Bank of New York finances the purchase of highly rated unsecured and asset-backed
commercial paper from eligible issuers via primary dealers.18 In expanding the facility, the
Federal Reserve could purchase more paper from eligible issuers and expand the program to
include lower-rated paper.
18
Primary dealers are firms that trade in U.S. government securities with the Federal Reserve System. There are
currently 17 primary dealers.
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Another alternative would be for the government to attempt to broadly lower the cost of
mortgage loans. In lowering the cost of borrowing, such a program would raise the demand
for houses, but it would be unlikely to boost house prices significantly, given the large
overhang of vacant houses. Programs of that kind would also help reduce unnecessary
foreclosures by increasing opportunities to refinance unaffordable loans.
Several programs are already in place to lower the cost of prime conforming loans (loans
of up to $417,000—higher in high-cost areas—that are eligible to be purchased by Fannie
Mae and Freddie Mac).
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•
The Housing and Economic Recovery Act of 2008 authorized the Department of the
Treasury to buy obligations and securities issued by Fannie Mae and Freddie Mac.
About $70 billion of residential mortgage-backed securities had been purchased as of
December 31, 2008.
Over the next several quarters, the Federal Reserve, through competitive auctions,
will purchase up to $100 billion in debt issued by the three government-sponsored
enterprises for housing—Fannie Mae, Freddie Mac, and the Federal Home Loan
Bank System.19
Over the next several quarters, the Federal Reserve will purchase up to $500 billion
in mortgage-backed securities issued by Fannie Mae, Freddie Mac, and the Government National Mortgage Association (Ginnie Mae).20
The government could begin a similar program to help thaw the market for jumbo
mortgages and stimulate originations of jumbo loans. Under such an approach, the
government could purchase securities that are backed by jumbo loans either directly or
through Fannie Mae and Freddie Mac.
Policymakers have also worked to improve the supply of student loans. In May 2008,
lawmakers enacted Public Law 110-227, the Ensuring Continued Access to Student Loans
Act, which allowed the Department of Education to offer buyer- and lender-oflast-resort
programs to lenders in the Family Federal Education Loan Program (or FFELP). Lenders in
the FFELP program, who finance the loans they make to students in private capital markets
(with federal assistance), have seen their financing costs increase sharply since the financial
market turmoil began.Under the new programs, which apply to loans issued before July 1,
2010, lenders may obtain temporary financing from the Education Department at attractive
borrowing terms (that is, at financing rates higher than those that might be considered normal
but lower than the rates they could get in the current credit markets), or they may sell their
loans to the department (at close to face value). Without the additional federal assistance,
those higher funding costs would have forced lenders to cut back on their lending in the 20082009 school year and beyond.
To date, the actions of the department have been successful in ensuring the continued
availability of student loans. The Department of Education has provided temporary financing
of $8.7 billion, which covers almost half of the loans originated in the 2008–2009 school
19
20
Unlike Fannie Mae and Freddie Mac, the Federal Home Loan Bank System, which provides lowcost loans to
home mortgage lenders, has not been taken over by the government.
Ginnie Mae, a government-owned corporation, guarantees securities backed by federally insured or guaranteed
loans, mainly loans insured by the Federal Housing Administration or guaranteed by the Department of
Veterans Affairs.
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Addressing the Ongoing Crisis in the Housing and Financial Markets
15
year. Loans worth approximately $62 million have thus far been sold to the department under
the purchase programs. In November, the Department of Education announced new programs
that broadened eligibility for funding and purchases of loans to those originated before 2008.
(Before that announcement, only loans that were originated in the 2008–2009 and 2009–2010
school years were eligible for purchase or financing.) Lenders may also be eligible to finance
their student loans under the Federal Reserve’s TALF.
Assist troubled businesses and governments
As part of a broader strategy to support the overall economy rather than just the financial
sector, the government could assist nonfinancial industries, as it has started to do with some
of the major U.S. automobile manufacturers, whose possible failure appears likely to worsen
the ongoing recession. Policymakers used some of the funds provided through the TARP to
support General Motors and Chrysler and their financial arms. However, other industries have
also sought assistance, putting policymakers in the position of picking winners and losers in
the current economic downturn. That situation raises issues of fairness, prompting questions
about why some workers and firms receive assistance but others do not. It also raises issues of
economic efficiency because assisting troubled businesses could keep labor and capital from
moving to other businesses and industries that might better be able to use them. That problem
may not seem severe during a recession, when there are unused resources. But to the extent
that businesses that would otherwise have failed are still around, and failing to thrive, after
the recession, resources will be misallocated and the productivity engine of the economy will
be compromised.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Promoting Actions to Lessen the Number of Mortgage Foreclosures
The government could help mortgage borrowers and lenders, and improve conditions in
the housing market, by more vigorously supporting efforts to reduce the number of avoidable
foreclosures. In 2007, about 1.6 million foreclosures were initiated; the first nine months of
2008 alone saw 1.7 million foreclosures. Moreover, with house prices likely to continue to
fall and with the recession pushing down family incomes, analysts expect the number of
foreclosures to remain high during the next two years. (CBO expects that the prices of houses
will decline by another 14 percent, and some forecasters in the private sector are looking for
even bigger slides.) Some analysts are now suggesting that the prices of houses in some
markets are back to or near their fundamental values; however, another possibility is that
prices could overshoot on the downside by 10 percent or more.21 Many of the coming
foreclosures are unavoidable because the borrowers cannot afford a refinanced loan that
would also be profitable for lenders (that is, the profits from the modified loan are less than
the amount that the lender would earn through foreclosure). However, some of those
foreclosures might be avoided if distressed borrowers were given the opportunity to refinance
their loans on more favorable terms. If government policies do not address the foreclosure
problem, the additional excess supply of houses could further push up expected mortgage
losses, which already exceed $1 trillion, according to some analysts.
21
For example, see Hatzius and Marschoun, Home Prices and Credit Losses.
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Douglas W. Elmendorf
The benefits of preventing unnecessary foreclosures are considerable, not only for lenders
and borrowers but also for the economy. The cost of a foreclosure may range from 30 percent
to 60 percent of the value of a property, and by contributing to neighborhood blight,
foreclosures have additional negative spillover effects on local economies. A reduction in the
number of avoidable foreclosures would complement other actions to strengthen the financial
sector because it would shore up the values of mortgage loans on lenders’ books—although
not by enough to resolve the problems with solvency in the financial system. A smaller
number of foreclosures would also provide some support for house prices, but probably not
enough to reverse their ongoing decline.
Modifications of mortgage loans have increased in the past year, but the approach has
met with limited success—in part because a large percentage of loans that had been modified
have subsequently redefaulted.22 However, the streamlined modification plan used by the
FDIC to modify loans made by the failed lender IndyMac may have more success because it
targets a substantial reduction in the borrower’s monthly payments and repayment burden.23
Efforts to reduce foreclosures face a number of obstacles. Lenders are afraid that if they
modify some loans, borrowers who otherwise might meet their contractual mortgage
payments will ask for loan modifications as well. Lenders also may be waiting to see what
mitigation strategies the government settles on. Further complicating modifications of loans
in many instances are second mortgages and home-equity loans and lines of credit. When first
liens are underwater—the value of the house is less than the balance on the mortgage—any
second liens are almost valueless. In that circumstance, modifying the first lien—especially
reducing the principal on an underwater loan— may do the borrower no good if it simply
increases the value of the second mortgage. Thus, meaningful loan modification may require
the cooperation of second lien holders, which can be difficult to arrange.
Modifications of loans held in “pools” that back securities face additional obstacles.
(Rates of foreclosure on loans that have been held by the lenders and not securitized are about
20 percent to 30 percent lower than the rates experienced by third-party servicers.) Thirdparty servicers have little or no financial incentive to modify mortgages because they will not
be adequately compensated for their costs. In addition, legal constraints and uncertainties in
the pooling and servicing agreements for mortgage-backed securities may inhibit
modifications. Servicers may be prohibited from performing modifications that improve the
net returns to all investors collectively if some investors (typically those holding the lowestpriority claims on the securities’ returns) are made worse off by the modification.
Consequently, a number of proposals have been advanced to overcome those obstacles.
To align the incentives of servicers more closely with those of investors and borrowers,
servicers could be paid a fee for each successful loan modification. Alternatively, investors
could be given an incentive to be more receptive to loan modifications. Under some
22
More than 37 percent of the loans modified in the first quarter of 2008 were more than 30 days delinquent after
three months, and 55 percent were more than 30 days delinquent six months later. See Office of the
Comptroller of the Currency and the Office of Thrift Supervision, OCC and OTS Mortgage Metrics Report,
Third Quarter 2008 (December 2008). Also see the remarks of John Dugan, Comptroller of the Currency, at
the Office of Thrift Supervision’s Third Annual National Housing Forum, Washington, D.C., December 8,
2008.
23
The FDIC contends that systematic loan modifications can still make good business sense even with a default rate
of 40 percent. See the statement of John F. Bovenzi, Deputy to the Chairman and Chief Operating Officer,
Federal Deposit Insurance Corporation, before the House Committee on Financial Services, January 13, 2009.
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17
proposals, those fees would be subsidized by the government. Currently, Fannie Mae and
Freddie Mac are increasing the payments they make for loan modifications.
Alternatively, a number of proposals would change the legal constraints that inhibit loan
modifications on securitized loans. For example, one would eliminate explicit restraints on
modifications and create a “safe harbor” from lawsuits in the case of modifications that raise
the overall net returns to investors.24 However, such proposals might raise constitutional
issues unless compensation was provided to some of the parties who lost out. And that kind of
approach would require using taxpayers’ money to compensate the holders of the riskiest
“slices” of mortgage-backed securities.
Another important obstacle to actions to promote loan modifications is that a large
number of distressed borrowers have “negative home equity”—that is, balances on their loans
that exceed the homes’ value. By the middle of last year, an estimated 10.5 million borrowers
had a total of about $850 billion in negative home equity with an average amount of more
than $75,000.25 Those borrowers do not have the necessary equity to qualify for a refinanced
loan with a private lender.26 To address that problem, policymakers created the Hope for
Homeowners program under the Federal Housing Administration (FHA) to encourage private
lenders to refinance loans of borrowers with negative home equity. The FHA will guarantee
new 30-year fixed-rate mortgages under the plan if the loans meet a number of criteria. One
criterion, that the new loan be between 90 percent and 97 percent of the home’s current
appraised value, has limited lenders’ interest in the program because it requires them, in some
cases, to “recognize” (record on their balance sheets) a substantial loss on the original loan.
To date, no modifications have been completed under this program, and the number of
applications has been minimal. Reducing the size of that write-down or subsidizing it (or
both) would encourage more lenders to participate, but it would also shift more costs to the
government.
Other proposals for limiting foreclosures would shift more costs to taxpayers either
through federal loan guarantees or direct purchases of loans and their modification by the
government. For example, a proposal by the FDIC would result in the government’s
guaranteeing modified loans. Under the proposal’s streamlined approach to modifications,
modified mortgages would include a reduction in interest rates, an extension of loan terms to
40 years, and forbearance on repayment of the principal, all of which would be designed to
reduce a borrower’s monthly cost for housing to as low as 31 percent of his or her monthly
income. If the loans subsequently redefaulted, lenders would recover up to 50 percent of the
loan from the government, subject to some restrictions.
A proposal modeled after the approach taken by the Depression-era Home Owners Loan
Corporation (HOLC) would have the government purchase and then refinance mortgages that
were in or near default. A new agency would be created that would buy mortgages from
lenders at some discount to the mortgages’ book values (the values for the loans that lenders
24
25
26
Statement of Christopher J. Mayer, professor, Columbia Business School, before the House Committee on
Financial Services, January 13, 2009.
Christopher Mayer and R. Glenn Hubbard, “Home Prices, Interest Rates, and the Mortgage Market Meltdown”
(working paper, Columbia University Business School, October 2008), available at www2.gsb.columbia
.edu/faculty/cMayer/Papers/Mayer_Hubbard_BEP_10_2008_v7.pdf.
Private lenders have generally avoided writing down the principal of mortgage loans in favor of either
forbearance on payment of the principal or reductions in interest rates. In part, they fear that write-downs will
encourage borrowers to behave strategically to qualify; in part, they also hope that housing prices will recover
in the future.
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18
Douglas W. Elmendorf
carry on their balance sheets) and then refinance them at interest rates tied to the
government’s borrowing rates (that is, the rates on Treasury securities). Buying up all of the
troubled loans, however, would require hundreds of billions of dollars, and determining prices
that would provide enough protection to taxpayers would be particularly challenging. Dangers
include the likelihood that the worst mortgages would be sold to the government and that a
lack of funding would allow only some borrowers to be helped. Although the HOLC returned
a small amount of funds to the Treasury when it was liquidated, the program was not costless
to taxpayers, who were not compensated for the risks they bore. Another concern is how to
target any subsidies that are offered and avoid the problem that some borrowers might be
helped a great deal and others only slightly. (Tying the subsidy to the size of the mortgage,
for example, would provide greater help to those with bigger mortgages.) Given the aggregate
amount of negative equity, such proposals could cost the government hundreds of billions of
dollars, even with the private sector absorbing a good portion of the losses.
A different approach to encouraging loan modifications would be to change federal
bankruptcy laws. Bankruptcy judges could be allowed to restructure certain mortgages on
principal residences under Chapter 13—for instance, by limiting a mortgage to the current
value of a home (known as “cram down”) or by changing the terms of a loan. Under current
law, Chapter 13 halts foreclosure proceedings by lenders, giving homeowners an opportunity
to restructure their financial arrangements. Although Chapter 13 currently gives courts the
leeway to adjust many financial obligations, it does not generally allow the terms of a
mortgage on a principal residence to be modified.27 Changing that provision of Chapter 13
would allow bankruptcy courts to treat mortgages on a primary residence in the same way
they treat secured debts on other items, such as motor vehicles, vacation homes, investment
properties, and personal businesses. (In practice, bankruptcy judges seldom restructure
mortgages on vacation or investment properties.)
Allowing a bankruptcy court to modify the amount or terms of a mortgage changes
incentives for both borrowers and lenders. It gives borrowers an incentive to file for
bankruptcy as a way to lower their mortgage payments and avert foreclosure. Consequently,
lenders would have a greater incentive to restructure loans voluntarily. Lenders would also
have a stronger incentive to be more prudent in making loans, which could help avoid future
excesses in the mortgage markets. In doing so, lenders might raise mortgage rates,
particularly for high-risk borrowers, to offset any expected additional losses from loan
modifications in bankruptcy. However, some research indicates that in the past, the terms and
availability of mortgages that could be modified in bankruptcy were not too different from
those that bankruptcy did not cover.28 The increase in mortgage rates might be limited in part
27
11 U.S.C. §1322(b)(2). Furthermore, the Supreme Court has held that even when the value of the debt exceeds
the value of the property—a partially secured debt—courts may not modify that debt. See Nobleman v. Am.
Savings Bank, 508 U.S. 324 (1993). Conversely, when a second (or third) mortgage is wholly unsecured
because the value of the property is insufficient to satisfy the first mortgage, such subordinated debt may be
discharged. Tanner v. FirstPlus Fin. Inc. (In re Tanner), 217 F. 3d 1357 (11th Cir. 2000) announced what has
become the dominant view among the circuitcourts of appeals. Hence, the claims of partially secured creditors
are protected by bankruptcy law, but the claims of unsecured creditors are not.
28
See Adam J. Levitin and Joshua Goodman, The Effect of Bankruptcy Strip-Down on Mortgage Markets, Business
Economics and Regulatory Policy Working Paper No. 1087816 (Washington, D.C.: Georgetown University
Law Center, February 6, 2008). See also Michelle J. White, Bankruptcy: Past Puzzles, Recent Reforms, and
the Mortgage Crisis, Working Paper No. 14549 (Cambridge, Mass.: National Bureau of Economic Research,
December 2008).
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19
because lenders might also change other lending terms to reduce their exposure to losses.
Changing the bankruptcy law could also add to the caseload of the bankruptcy court system.
THE BUDGETARY COSTS OF THE FINANCIAL RESCUE
The ultimate costs of the actions taken in response to the turmoil in the financial markets
are uncertain, but they could be quite large. Those costs derive from the policy actions of
various parts of the government—the Federal Reserve, the Treasury, and other federal
agencies. Many of the actions involve the purchase of assets or loans by the government; as a
result, some portion of the current funding being directed toward the crisis (perhaps most of
it) is likely to be recouped in the future. However, given the fragility of the financial sector
and the riskiness of the assets being purchased or guaranteed—as well as the social purposes
underlying the policy responses—the federal government can expect some net losses from its
transactions. (Tables 1 to 3 contain details of those actions.)
Costs to the Taxpayer
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Most of the policy actions taken in response to the financial turmoil have been more like
investments than like cash outlays. Both the Federal Reserve and the Treasury have been
purchasing financial instruments (for example, mortgage-backed securities) in an effort to
boost liquidity in the market; at some point in the future, many of those instruments will be
redeemed by their issuers or sold to other buyers. Because such investments were not made
purely with the goal of making a profit, they could reasonably be expected to result in some
losses.
The Federal Reserve
Activities of the Federal Reserve are not directly recorded in the federal budget. Rather,
each year its net earnings—generated by interest on its holdings of securities; income from
foreign currency holdings; fees received for services provided to institutions that accept
monetary deposits from consumers (such as check clearing, funds transfers, and automated
clearinghouse operations); and interest on loans to such institutions—are remitted to the
Treasury and recorded in the budget as revenues. That income is typically in the range of $20
billion to $30 billion a year.29
Thus, recent actions by the Federal Reserve to address the turmoil in the markets may
affect federal revenues through their impact on the amount of earnings that the central bank
remits to the Treasury. Those earnings will be diminished by any losses that resulted from
creditors being unable to repay loans or from assets that the Federal Reserve acquired proving
to be worth less than the cost to acquire them. The central bank has committed nearly $2.3
29
The Federal Reserve is now paying interest on required reserves and excess balances held on behalf of financial
institutions. The interest rate paid on those deposits is currently set at 0.25 percent; CBO estimates that the
Federal Reserve will incur interest costs of less than $5 billion in 2009. Authorization to pay interest on such
reserves came from the Emergency Economic Stabilization Act of 2008, which advanced the effective date of
a provision of the Financial Services Regulatory Relief Act of 2006 that was slated to take effect in 2011.
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Douglas W. Elmendorf
trillion to its programs, but the assets purchased through those programs are backed by
collateral. Still, CBO estimates that the Federal Reserve will incur modest losses, although it
is expected to eventually recoup nearly all of its investments. Nevertheless, losses are
possible; for example, the Federal Reserve has already written down—by about $2 billion—
the value of the assets it acquired in the takeover of Bear Stearns.
The Troubled Asset Relief Program
CBO records spending for the TARP on a risk-adjusted discounted-present-value basis
rather than on a cash basis.30 That is, CBO accounts for the costs resulting from interest
subsidies, potential defaults on lending, and other factors. As is the case with the Federal
Reserve’s transactions, the principal of most of the assets acquired under the TARP should be
repaid over time. Of the $700 billion that the TARP is expected to disburse before the end of
December of this year, CBO anticipates that the subsidy cost (after adjusting for market risk)
will be about $200 billion.
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Purchases of Mortgage-Backed Securities
The Treasury is also purchasing mortgage-backed securities in the private market. Again,
those transactions are basically an exchange of assets—the Treasury has used cash to buy the
securities and will receive cash upon the sale of the asset or at its maturity. Because there is
no statutory provision for an alternate treatment, the cost of purchases of mortgage-backed
securities is computed using standard credit reform procedures.31 To date, the net cost of
those purchases is close to zero.
Fannie Mae and Freddie Mac
In CBO’s baseline projections of the federal budget, most of the cost recorded in 2009 for
Fannie Mae and Freddie Mac stems from the existing assets and liabilities of the two GSEs at
the time of their takeover. CBO estimates that the value of the GSEs’ mortgage loans and
guaranteed assets falls short of their liabilities by about $200 billion (on a present-value
basis); that amount is included in CBO’s estimate of the deficit as calculated for 2009. Nearly
$40 billion in 2009 and smaller annual amounts thereafter represent the estimated annual
subsidy costs (on a net-present-value basis) associated with the GSEs’ new business after the
takeover. The decline in the annual subsidy reflects CBO’s forecast that the housing and
mortgage markets will stabilize over the next several years.
CBO has long held that the federal government has subsidized the operation of Fannie
Mae and Freddie Mac by providing what some have called an “implicit guarantee” of the
GSEs’ debt.32 However, the federal government has never recognized the cost of the subsidy
in its budget. The value of that guarantee (the existence of which has now been demonstrated
30
The Administration is accounting for capital purchases made under the TARP on a cash basis rather than the
present-value basis adjusted for market risk that was specified in the Emergency Economic Stabilization Act
of 2008. (Present value is the value on a given date of a future payment or series of future payments,
discounted using an appropriate interest rate to reflect the risk and term to maturity of the underlying asset.)
The Administration’s treatment will show more outlays than will CBO’s treatment for the TARP this year and
then will show cash receipts in future years.
31
For an explanation of credit reform, see Congressional Budget Office, Policy Options for the Housing and
Financial Markets, Box 3-2 (April 2008).
32
See Congressional Budget Office, Assessing the Public Costs and Benefits of Fannie Mae and Freddie Mac (May
1996), and Federal Subsidies and the Housing GSEs (May 2001).
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21
by the Treasury) is a large component of the estimated cost of the GSEs’ operations that CBO
has included in its baseline budget projections.
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Other Agencies
A few other agencies have also taken actions in response to the turmoil in the markets,
either through existing authority or on the basis of recent legislation. The FDIC has
temporarily raised the limit on insurance coverage—from $100,000 to $250,000 per
depositor—and has established a program to enhance liquidity by guaranteeing debt issued by
banks as well as deposits in checking accounts and other non-interest-bearing accounts. The
FDIC will also provide assistance to Citigroup in conjunction with the TARP and the Federal
Reserve.
Financial turmoil has also affected credit unions. As a result, the National Credit Union
Administration, or NCUA (the federal agency that charters and supervises federal credit
unions and insures deposits) has created programs to ensure the liquidity of its member
institutions. The costs incurred by the FDIC and NCUA are treated in the budget on a cash
basis.
The Department of Housing and Urban Development (HUD) has established several
programs in an attempt to reduce foreclosures and address other issues in the housing market.
Many of those programs were created by the Housing and Economic Recovery Act of 2008,
but HUD has also used existing authority to create the FHA Secure program. HUD’s
programs are also treated in the budget on a cash basis.
Differences between CBO and the Administration in the Treatment of Policy Actions in
the Budget
By this point, two major differences have arisen between CBO and the Administration in
their treatment of policy actions taken in response to the financial crisis. One involves the
recording of the budgetary costs of the TARP, and the other deals with the costs related to the
conservatorship of Fannie Mae and Freddie Mac.
The Troubled Asset Relief Program
Section 123 of the Emergency Economic Stabilization Act of 2008 states that the federal
budget should display the costs of purchasing or insuring troubled assets by using procedures
similar to those specified in the Federal Credit Reform Act but with an adjustment to account
for market risk. Under that procedure, the federal budget would not record the gross cash
disbursement for the purchase of a troubled asset (or the cash receipt for its eventual sale) but
instead would reflect the market value of the asset or an estimate of the government’s net cost
(on a present-value basis) for the purchase. Broadly speaking, the net cost is the purchase cost
minus the present value—calculated using an appropriate discount factor that reflects the
riskiness of the underlying cash flows associated with the asset—of any estimated future
earnings from holding the asset and the proceeds from its eventual sale.
Following that directive, CBO has estimated that the net costs of the TARP’s activities
through January 22, 2009 (with $293 billion disbursed), total $94 billion. That calculation
implies a subsidy rate of 32 percent—that is, the net subsidy (in 2009 dollars) amounts to an
estimated 32 percent of the government’s initial expenditures. CBO and the Administration’s
Office of Management and Budget do not differ significantly in their assessments of the
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Douglas W. Elmendorf
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subsidy cost of those transactions but vary in their judgment as to how the transactions should
be reported for budgetary purposes.
OMB submitted its first report to the Congress on the costs of the Treasury’s purchases
and guarantees of troubled assets on December 5, 2008;33 at the time that the report was
compiled (November 6, 2008), the TARP had disbursed $115 billion to eight large banks in
exchange for preferred stock and warrants (securities that entitle the holder to buy stock of the
company that issued them at a specified price). OMB maintains that the Federal Credit
Reform Act applies only to direct loans and loan guarantees and that the reference in the
Emergency Economic Stabilization Act does not require the use of credit reform procedures
for other types of transactions. As a result, it budgeted for those initial TARP disbursements
on a cash basis rather than by reporting the estimated subsidy cost.
In its December report on the TARP, however, OMB also provided two alternative
estimates of the subsidy cost of that first set of disbursements. One such estimate was valued
using procedures similar to those specified in the Federal Credit Reform Act (discounting
future cash flows using a risk-free rate), and the other estimate was calculated using an
approach similar to the way CBO treats the TARP (discounting future cash flows while
adjusting for estimated market risk). OMB’s second alternative calculation is comparable to
CBO’s assessment of the cost of the first $115 billion of transactions. Using a modified credit
reform basis (that is, adjusting for risk), OMB estimated those costs to be $25.5 billion, or a
subsidy rate of 22 percent, and CBO arrived at a cost of $20.5 billion, or a subsidy rate of 18
percent. Most of that difference is probably explained by such factors as the discount rate
used (which is affected by when the estimates were made) and the volatility of stock prices
(which affects the potential value of the warrants).
Fannie Mae and Freddie Mac
CBO has concluded that because of the extraordinary degree of management and
financial control that the government has exercised, Fannie Mae and Freddie Mac should now
be considered federal operations. Although the GSEs are not legally government agencies and
their employees are not civil servants, CBO believes it is appropriate and useful to
policymakers to account for and display the GSEs’ financial transactions alongside all other
federal activities in the budget.
However, the Administration continues to treat the two organizations as separate from the
government. As a result, it has so far recorded the cash infusion that the Treasury provided to
Freddie Mac ($13.4 billion) as an outlay. By contrast, CBO considers such payments as
intragovernmental transfers that have no net effect on the budget.
33
Office of Management and Budget, “OMB Report Under the Emergency Economic Stabilization Act, Section
202,” letter to the Honorable Nancy Pelosi (December 5, 2008), available at www.whitehouse.gov/
omb/legislative/eesa_120508.pdf.
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Addressing the Ongoing Crisis in the Housing and Financial Markets
Table 1. Actions Taken by the Federal Reserve in Support of the
Housing and Financial Markets as of January 22, 2009
(Billions of dollars)
Action
Reductions in
Interest Rates
Funding
Committed
Potentiala
to Date
n.a.
n.a.
Loans to Financial Institutions
Primary and
63
Secondary Credit
Programs
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Term Auction
Facility
416
Takeover of Bear Stearns
Backed assets to
29
facilitate takeover
of Bear Stearns by
JP Morgan Chase
Unknown
600
29
Description
The target for the federal funds
rate (the interest banks charge on
loans to other banks) was reduced
10 times between September 2007
and December 2008, falling from
5.25 percent to between zero and
0.25 percent.
Through the primary and secondary credit programs, the Federal
Reserve disburses short-term loans
to banks and other institutions that
are legally allowed to accept
monetary deposits from
consumers. The term of the loan
may be as long as 90 days.
The Term Auction Facility (TAF)
allows banks and other financial
institutions to pledge collateral in
exchange for a loan from the
Federal Reserve. The interest rate
on the loan is determined by
auction; such auctions are conducted biweekly for loans with a
maturity of either 28or 84 days.
The maximum size of each auction
is $150 billion, although accepted
bids for most recent auctions have
been considerably smaller.
The Federal Reserve created
Maiden Lane I, a limited liability
company (LLC), to acquire certain
assets of Bear Stearns at a cost of
$29 billion. (An LLC offers
protection from personal liability
for business debts, just like a
corporation. The profits and losses
of the business pass through to its
owners, as they would if the
business was a partnership or sole
proprietorship.) The LLC will
manage those assets to
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23
24
Douglas W. Elmendorf
Action
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Support for AIG
Acquired control
of nearly 80
percent of the
insurance
company
Table 1. (Continued)
Funding
Description
Committed
Potentiala
to Date
maximize the likelihood that the
investment is repaid and to
minimize disruption to financial
markets. The current value of the
portfolio on the Federal Reserve’s
balance sheet is $27 billion.
82
113
Support for Short-Term Corporate Borrowing
Commercial
351
1,800
Paper Funding
Facility
The Federal Reserve agreed to
loan AIG $60 billion and acquired
control of nearly 80 percent of the
company. In addition, the Federal
Reserve Bank of New York
bought $19.5 billion of residential
mortgage-backed securities from
AIG’s portfolio through an LLC
and another $24.5 billion of
collateral-lized debt obligations
(CD-Os) on which AIG wrote
contracts for credit default swaps
through another LLC. (CDOs) are
complex financial instruments that
repackage assets such as mortgage
bonds, loans for leveraged
buyouts, and other debt—
including other CDOs— into new
securities. A credit default swap is
a type of insurance arrangement in
which the buyer pays a premium at
periodic intervals in exchange for
a contingent payment in the event
that a third party defaults. The size
of the premium paid relative to the
contingent payment generally
increases with the likelihood of
default.)
The Commercial Paper Funding
Facility (CPFF) finances the
purchase of commercial paper
(securities sold by large banks and
corporations to obtain funding to
meet short-term borrowing needs,
such as payroll) directly from
eligible issuers. Securities
purchased under this program may
be backed by assets or unsecured;
they must be highly rated,
denominated in U.S. dollars, and
have a maturity of three months.
The program is in effect through
April 30, 2009.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Addressing the Ongoing Crisis in the Housing and Financial Markets
Committed
Potentiala
to Date
Support for Money Market Mutual Funds
Money Market
0
540
Investor Funding
Facility
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Asset-Backed
Commercial
Paper Money
Market Mutual
Fund Liquidity
Facility
15
Support for Primary Dealers
Term Securities
133
Lending Facility
and TSLF Options
Program
Primary Dealer
Credit Facility
33
Unknown
200
Unknown
The Money Market Investor
Funding Facility (MMIFF) is
designed to restore liquidity to
money markets by purchasing
certificates of deposit, bank notes,
and commercial paper from money
market mutual funds and other
similar investors. The authority to
purchase assets is in effect through
April 30, 2009.
The Asset-Backed Commercial
Paper Money Market Mutual Fund
Liquidity Facility (AMLF)
provides funding to U.S.
depository institutions and bank
holding companies to finance their
purchases of high-quality assetbacked commercial paper (ABCP)
from money market mutual funds
under certain conditions. The
program is intended to assist
money market funds that hold such
paper in meeting demands for
redemptions by investors and to
foster liquidity in the ABCP
market specifically and money
markets generally. The program is
in effect through April 30, 2009.
The Term Securities Lending
Facility (TSLF) offers to lend
Treasury securities held by the
Federal Reserve for a one-month
term in exchange for other types of
securities held by the 17 financial
institutions, known as primary
dealers, that trade directly with the
Federal Reserve. The TS-LF
Options Program (TOP) offers
options on short-term TSLF loans
that will be made on a future date.
(An option is a contract written by
a seller that conveys to the buyer
the right—but not the obligation
—to buy or sell a particular asset.)
The Primary Dealer Credit Facility
(PDCF) provides overnight loans
in exchange for eligible collateral
to fina-ncial institutions that trade
directly with the Federal Reserve.
The program is in effect through
April 30, 2009.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
25
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
26
Douglas W. Elmendorf
Table 1. (Continued)
Funding
Action
Description
Committed
Potentiala
to Date
Support for the Mortgage Market
Purchases of the
23
100
The Federal Reserve will purchase
debt of the
up to $100 billion in debt issued
housing-related
by three government-sponsored
governmententerprises GSEs)—Fannie Mae,
sponsored
(Freddie Mac, and the Federal
enterprises
Home Loan Banks—through
competitive auctions over the next
several quarters.
Purchases of
53
500
Over the next several quarters, the
mortgage-backed
Federal Reserve will purchase up
securities
to $500 billion in mortgagebacked securities (MBSs) issued
by GSEs and the Government
National Mortgage Association
(Ginnie Mae).
Support for Consumer and Small Business Lending
Term Asset0
200
Through the Term Asset-Backed
Backed Securities
Securities Loan Facility (TALF),
Loan Facility
the Federal Reserve Bank of New
York will lend up to $200 billion
to holders of certain AAA-rated
asset-backed securities (consumer
and small business loans), and the
Troubled Asset Relief Program
will provide $20 billion of credit
protection (protection against
debtors that do not pay because of
insolvency or protracted default)
for those loans. The TALF is
expected to begin lending in
February 2009; the authority
expires on December 31, 2009.
0
234
The Federal Reserve will absorb
Assistance to
90 percent of any losses resulting
Citigroup
from the federal government’s
guarantee of a pool of Citigroup’s
assets after payouts have been
made by Citigroup, the Troubled
Asset Relief Program, and the
Federal Deposit Insurance
Corporation.
0
87
The Federal Reserve will absorb
Assistance to
90 percent of any losses resulting
Bank of America
from the federal government’s
guarantee of a pool of Bank of
America’s assets after payouts
have been made by Bank of
America, the Troubled Asset
Relief Program, and the Federal
Deposit Insurance Corporation.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Addressing the Ongoing Crisis in the Housing and Financial Markets
Currency Swaps
Committed
to Date
At least 500
27
Potentiala
Unlimited
In response to strong demand for
dollars from abroad, the Federal
Reserve has contracted with 14
foreign central banks to make U.S.
dollars available temporarily.
After a specified period of time,
the original amounts of dollars will
be returned in exchange for the
foreign currency.
Source: Congressional Budget Office based on information from the Federal Reserve.
Note: n.a. = not applicable.
“Potential funding” refers to the size of the market or the maximum amount of lending under the
program.
Table 2. Actions Taken by the Treasury in Support of the Housing and Financial
Markets as of January 22, 2009
(Billions of dollars)
Disbursements
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Action
Subsidy
b
To
Date
Potential
Troubled
Asset Relief
Program
293
700
(Credit
basis)
94
HousingRelated Tax
Provisions
0
12
n.a.
a
Description
The Emergency Economic Stabilization Act of 2008 (Division A of
P.L. 110-343) granted authority to
the Treasury to purchase $700
billion in assets through a new
program, the Troubled Asset
Relief Program (TARP). The
second $350 billion will become
available on January 27, 2009.
As of January 22, the program had
disbursed $293 billion. The
subsidy cost estimated by the
Congressional Budget Office—
about $94 billion to date—is
computed using the modified
credit reform procedure (that is,
accounting for market risk)
specified in P.L. 110-343.
The Housing and Economic
Recovery Act of 2008 (P.L. 110289) authorized a refundable tax
credit for first-time home buyers
(to be repaid, without interest,
over
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
28
Douglas W. Elmendorf
Table 2. (Continued)
Disbursements
Subsidy
b
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Action
To
Date
Potential
a
Description
(Credit
basis)
Purchases
of
Obligations
and Securities Issued
by Fannie
Mae and
Freddie
Mac
71
Unlimite
d
-1
Conservator
ship for
Fannie Mae
and Freddie
Mac
14
200
n.a.
Temporary
Guarantee
Program for
Money
Mar-ket
Funds
Unk
now
n
3,000
n.a.
a 15-year period) and contained
other housing-related tax
provisions.
The Housing and Economic
Recovery Act of 2008 authorized
the Department of the Treasury to
buy obligations and securities
issued by Fannie Mae and Freddie
Mac. About $71 billion of
residential mortgage-backed
securities (securities whose value
is derived from an underlying pool
of mortgages) had been purchased
as of December 31, 2008.
Authority to make such market
purchases expires on December
31, 2009. The subsidy cost
recorded in the budget is
computed using standard credit
reform procedures.
The Treasury received senior
preferred equity shares and
warrants in exchange for any
future contributions necessary to
keep the two entities solvent.
(Preferred equity shares provide a
specific dividend to be paid before
any dividends are paid to common
stockholders and take precedence
over common stock in the event of
a liquidation; a warrant is a
security that entitles the holder to
buy stock of the company that
issued it at a specified price.)
The Treasury will gu-arantee
investors’ shares as of September
19, 2008. The guarantee is in
effect through April 30, 2009, but
can be extended through
September 18, 2009. Participating
funds pay a fee of 1.5 or 2.2 basis
points times the number of shares
outstanding. (A basis point is onehundredth of a percentage point.)
The Treasury is borrowing from
the public to assist the Federal
Reserve.
Supplement 175
Unlimite
n.a.
d
-ary
Financing
Program
Source: Congressional Budget Office based on information from the Department of the Treasury.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Addressing the Ongoing Crisis in the Housing and Financial Markets
29
Note: n.a. = not applicable.
a. “Potential disbursements” refers to the maximum amount of spending under the program or the maximum
amount of outstanding assets available for guarantee.
b. “Subsidy,” broadly speaking, refers to the purchase cost minus the present value of any estimated future earnings
from holding those assets and the proceeds from the eventual sale of them.
Table 3. Actions Taken by Other Agencies in Support of the Housing and Financial
Markets as of January 22, 2009
(Billions of dollars)
Disbursements
To Date
Potentiala
Federal Deposit Insurance Corporation
n.a.
700
Temporarily Raised the
Basic Limit on Insurance
Coverage from $100,000
to $250,000 per
Depositor
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Action
Temporary Liquidity
Guarantee
Program
n.a.
1,450
Assistance to Citigroup
0
10
Description
The Emergency Economic Stabilization Act of
2008 (Division A of P.L. 110-343) temporarily
raised the limit on deposit insurance through
December 31, 2009. That action is estimated to
increase the amount of insured deposits by
about $700 billion, or 15 percent.
The Temporary Liquidity Guarantee Program
has two components. The first—the debt
guarantee program—aims to enable
participating institutions to borrow and lend
money more readily. It fully protects certain
newly issued senior unsecured debt (securities
that are not backed by collateral and have
priority over all other debt in rank-ing for
payment in the event of default) with a
maturity of more than 30 days, including
promissory notes,
commercial paper (securities sold by large
banks and corporations to meet short-term
needs, such as payroll), and interbank funding.
The guarantee applies to debt that is issued by
June 30, 2009, and matures no later than June
30, 2012. Participating institutions pay fees
based on the maturity of the debt. To date, the
Federal Deposit Insurance Corporation (FDIC)
has guaranteed $238 billion of new debt;
potential guarantees could total $1 trillion.
The second component provides full
guarantees for certain checking and other noninterest-bearing accounts through December
31, 2009. Participat-ing institutions also pay
fees for this guarantee, which could total $450
billion.
The FDIC may absorb up to $10 billion in
losses resulting from the federal government’s
guarantee of a pool of Citigroup’s assets after
payouts have been made by Citigroup and the
Trou-bled Asset Relief Program. As a fee for
the guarantee, the FDIC will receive $3 billion
in preferred stock (shares of equity that
provide a specific dividend to be paid before
any dividends are paid to common
stockholders and that take precedence over
common stock in the event of a liquidation).
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
30
Douglas W. Elmendorf
Table 3. Continued
Department of Housing and Urban Development
0
4
Redevelopment of
Abandoned and
Foreclosed Homes
HOPE for Homeowners
Program
0
1
FHA Secure
n.a.
1
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
National Credit Union Administration
Streamlined Modification
Unknown
Program
Unknown
Credit Union
Homeowners
Affordability Relief
Program and Credit
Union System Investment
Program
5
41
Temporary Corporate
Credit Union
Liquidity Guarantee
Program
n.a.
Unknown
The Housing and Economic Recovery Act of
2008 (P.L. 110-289) provided $4 billion in
funding to state and local governments to
purchase and rehabilitate foreclosed and
abandoned homes.
The HOPE for Homeowners program permits
home mortgages to be refinanced through
private lenders with a guarantee from the
Federal Housing
Administration. The new loans must have a
loan-to-value ratio that is no greater than 90
percent of the property’s appraised value.
FHA Secure was a temporary initiative to
permit lenders to refinance non-FHA (Federal
Housing Administration) adjust-table-rate
mortgages. The pro-gram has made about
4,000 loans since fall 2007
The Streamlined Modification Program is
intended to avoid foreclosures by creating a
fast-track method for reducing monthly
payments on mortgages. The program will
restrict payments to 38 percent of a
household’s gross monthly income by reducing
the interest rate, extending the life of the loan,
or deferring principal. That policy applies to
loans held by Fannie Mae and Freddie Mac
and was launched on December 15, 2008.
These two loan programs are operated through
the National Credit Union Administration’s
Central Liquidity Facility and are financed by
borrowing from the Federal Financing Bank.
The Credit Union Homeowners Affordability
Relief Program (CU HARP) will provide
subsidized funding intended to help credit
unions modify mortgages. The Credit Union
System Investment Program (CU SIP) seeks to
facilitate lending by shoring up corporate credit
unions (which primarily provide financial
resources and services to other credit unions).
The Temporary Corporate Credit Union
Liquidity Guarantee Pro-gram guarantees
certain unsec-ured debt of participating corporate credit unions that was or will be issued
between October 16, 2008, and June 30, 2009.
Such debt must mature by June 30, 2012.
Participating institutions pay annualized fees
for the guarantees. To date, this program has
guaranteed $5 billion in debt.
Source: Congressional Budget Office based on information from the Federal Deposit Insurance
Corporation, the Department of Housing and Urban Development, the Federal Housing Finance
Agency, and the National Credit Union Administration.
Note: n.a. = not applicable.
a. “Potential disbursements” refers to the maximum amount of spending under the program or the
maximum amount of outstanding assets available for guarantee.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 31-74
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 2
VALUE VERSUS GROWTH REAL
ESTATE INVESTMENT STRATEGY:
IS THE WIN A FLASH IN THE PAN?
Kwame Addae-Dapaah*, Hin/David Kim Ho, and Yan Fen Tan
ABSTRACT
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
The superiority of the contrarian investment strategy, though well attested in the
finance literature, is being questioned in some quarters on the pretext that the gap
between the performance of value and growth investment narrows over time. If this is
proven to be true, it would imply that value real estate investment may not be advisable
given that real estate is a medium to long term investment. This paper uses empirical real
estate investment return data from 1985Q1 to 2005Q3 for US, and some Asia Pacific
cities to ascertain whether the superiority of “value” over “growth” real estate investment
is a “flash” in the pan, i.e. unsustainable. The office, industrial and retail property
investments are examined in the context of the value-growth paradigm, and
complemented with mean reversion and stochastic dominance tests. In addition to
confirming the relative superiority of “value” over “growth” property investment, the
results show that office and industrial property investments exhibit return reversal. This
implies that the “win” is sustainable. Although the returns from retail property investment
display inertia, the results of stochastic dominance test validate the relative superiority of
“value” over “growth” property investment for all the three sectors. This implies that
fund managers who traditionally have been favoring prime (i.e. growth) property
investment may have to reconsider their investment strategy if they want to maximize
their return.
*
Corresponding author: E-mail: rstka@nus.edu.sg, Department of Real Estate, School of Design & Environment,
National University of Singapore 4 Architecture Drive Singapore 117566 Telephone: ++ 65 6516 3417 Fax: ++
65 6774 8684.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
32
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
INTRODUCTION
The choice of an investment strategy is an important step in the decision-making process
of fund managers and large institutional investors. In view of this, growth stock investment
strategy and value stock investment strategy have received considerable attention in the
finance literature. The growth stock investment strategy is frequently associated with
investments in “glamour” stocks that have relatively high price-to-earnings ratios (i.e. high
gross income multiplier in real estate terms). On the other hand, value stock investment
strategy usually involves investing in “gloomy” stocks that characteristically have relatively
low market prices in relation to earnings per share (EPS), cash flow per share, book value
per share, or dividend per share (i.e. low gross income multiplier). They are often less
popular stocks that have recently experienced low or negative growth rates in corporate
earnings. Notwithstanding their relative unpopularity with investors, studies have shown that
investments in value stocks, commonly known as contrarian investment strategy, have
outperformed growth stocks in major markets (see for example, Fama and French [1993,
1995, 1996, 1998], Capual et al. [1993], Lakonishok et al. [1994], Haugen [1995],
Arshanapali et al. [1998], Levis and Liodakis [2001], Badrinath and Omesh [2001] and Chan
and Lakonishok [2004]).
However, Jones (1993) reports that the profitability of contrarian portfolios is a pre-WW
II phenomenon that has since largely disappeared. Furthermore, Kryzanowski and Zhang
(1992) find that the Canadian stock market exhibits significant price inertia, which negates
the relative superiority of contrarian investments. These contrary findings have been refuted
(see for example, Bauman and Miller [1997]).
In view of the overwhelming evidence in support of the superior performance of
contrarian investment in the finance literature, there appears to be a prima facie case for
expecting contrarian real estate investment to do likewise (Addae-Dapaah et al. (2002)).
Growth stock is analogous to prime properties as both have relatively low earnings-toprice ratio (i.e. low initial yield) and investors in both investment media pin their hopes on a
relatively high potential price or capital appreciation. Similarly, value stock that provides high
income is comparable to high income-producing properties such as lower grade properties
and properties in secondary locations. In relation to real property, the contrarian strategy
implies that value properties with high running yield could outperform growth properties with
low running yield. Thus, the objectives of the study are:
i)
to ascertain the comparative advantage(s), in terms of performance, of contrarian real
estate investment;
ii) to evaluate the relative riskiness of value properties and growth properties;
iii) to establish whether excessive extrapolation and expectational errors characterize
growth and value strategies; and
iv) to ascertain the sustainability of the relative superiority (the “win”) of contrarian real
estate investment if such superiority is established.
In view of this, the next section provides a brief review of the finance literature on the
contrarian investment strategy after which, a specific set of research hypotheses are
formulated. This is followed by a discussion on data management and sourcing, and the
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
33
contrarian strategy model. The next section is devoted to the empirical model estimation
which is followed by a post-model estimation. The last section deals with concluding
remarks.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
LITERATURE REVIEW
According to Dreman (1982) a contrarian investor is an investor who goes against the
“grain”. Thus, contrarian investment strategy simply refers to investment in securities which
have lost favor with investors. It covers various investment strategies based on buying/selling
stocks that are priced low/high relative to accounting measures of performance – earnings-toprice ratios (E/P), cash flow-to-price ratio (C/P) and book value-to-price ratio (B/P) – as well
strategies based on low/high measures of earning per share (EPS) growth (Capual, 1993). In
simple terms, the contrarian investment strategy refers to the value/growth stock paradigm.
While there is substantial empirical evidence supporting the efficient market hypothesis
that security prices provide unbiased estimates of the underlying values, many still question
its validity. Smidt (1968) argues that one potential source of market inefficiency is
inappropriate market responses to information. The inappropriate responses to information
implicit in Price-Earnings (P/E) ratios may be indicators of future investment performance of
a security. Proponents of this price-ratio hypothesis claim that low P/E securities tend to
outperform high P/E stocks (Williamson, 1970). Basu (1977), Jaffe et al. (1989), Fama and
French (1992, 1998), Davis (1994), Lakonishok et al. (1994), Bauman et al. (1998),
Badrinath and Omesh (2001) and Chan and Lakonishok (2004) show a positive relationship
between earnings yield and equity returns. However, as a result of the noisy nature of
earnings (i.e. the category of stocks with low E/P include also stocks that have temporarily
depressed earnings), value strategies based on E/P give narrower spreads compared to other
simple value strategies (Chan and Lakonishok [2004]). Furthermore, in view of the noise in
reported earnings that results from Japanese accounting standards (i.e. distortions in the
earnings induced by accelerated depreciation allowances), Chan et al. (1991) find no evidence
of a strong positive earnings yield effect after controlling for the other fundamental variables.
Rosenberg et al. (1985) show that stocks with high Book Value relative to Market Value
of equity (BV/MV) outperform the market. Further studies, e.g. Chan et al. (1991) and Fama
and French (1992), confirm and extend these results. In view of the highly influential paper
by Fama and French (1992), academics (e.g. Capaul et al., 1993; Davis, 1994; Lakonishok et
al., 1994; La Porta et al., 1997; Fama and French, 1998; Bauman et al., 1998 and 2001; Chan
et al., 2000; and Chan and Lakonishok, 2004) have shifted their attention to the ratio of
BV/MV as one of the leading explanatory variables for the cross-section of average stock
returns.
Although BV/MV has gained much credence as an indicator of value-growth orientation,
it is by no means an ideal measure (Chan and Lakonishok (2004)). BV/MV is not a ‘clean’
variable uniquely associated with economically interpretable characteristics of the firm
(Lakonishok et al. (1994)). Many different factors are reflected in this ratio. For a example,
low BV/MV may describe a company with several intangible assets that are not reflected in
accounting book value. A low BV/MV can also describe a company with attractive growth
opportunities that do not enter the computation of book value but do enter the market price. A
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
34
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
stock whose risk is low and future cash flows are discounted at a low rate would have a low
BV/MV as well. Finally, a low BV/MV may be reminiscent of an overvalued glamour stock.
The shortcomings of accounting earnings have motivated a number of researchers to
explore the relationship between cash flow yields and stock returns. High Cash Flow to Price
(CF/P) stocks are identified as value stocks because their prices are low per dollar of cash
flow, or the growth rate of their cash flows is expected to be low. Chan et al. (1991), Davis
(1994), Lakonishok et al. (1994), Bauman et al. (1998), Fama and French (1998), and Chan
and Lakonishok (2004) show that a high ratio of CF/P predicts higher returns. This is
consistent with the idea that measuring the market’s expectations of future growth more
directly gives rise to better value strategies (La Porta (1996)).
Fama and French (1998) and Bauman et al. (1998) use the ratio of Dividends to Price
(D/P) as a proxy for the market’s expectations of future growth. Firms with higher ratios have
lower expected growth and are considered to be value stocks. They show that the
performance of the value stocks based on dividend yields is quantitatively similar to the
performance based on the prior categorizations (i.e. P/E, BV/MV and CF/P). Finally, instead
of using expectations of future growth to operationalize the notions of glamour and value,
Davis (1994) and Lakonishok et al. (1994) use past growth to classify stocks. Davis (1994)
and Lakonishok et al. (1994) measure past growth by Growth in Sales (GS) to conclude that
the spread in abnormal returns is sizeable.
To the extent that the different valuation indicators of value-growth orientation are not
highly correlated, a strategy based on information from several valuation measures may
enhance portfolio performance. Lakonishok et al. (1994) explore sophisticated twodimensional versions of simple value strategies. According to the two-way classification,
value stocks are defined as those that have shown poor growth in sales, earnings and cash
flow in the past, and are expected by the market to continue growing slowly. Expected
performance is measured by multiples of price to current earnings and cash flow. La Porta et
al. (1997) form portfolios on the basis of a two-way classification based on past GS and CF/P
introduced by Lakonishok et al. (1994). Using robust regression methods, Chan and
Lakonishok (2004) estimate cross-sectional models that predicted future yearly returns from
beginning-year values of the BV/MV, CF/P, E/P and the sales to price ratio. The use of the
multiple measures in the composite indicators boosts the performance of the value strategy
(see Gregory et al. [2003]).
In contrast to the above findings, Jones (1993) reports that the profitability of contrarian
portfolios is a pre-WW II phenomenon that has since largely disappeared. However, this has
been refuted by later studies which include post-war data. Also, Kryzanowski and Zhang
(1992) suggest that positive profits resulting from the use of the contrarian investment
strategy are limited to the U.S. stock market. When applied to the Canadian stock market, the
DeBondt and Thaler (1985) do not produce favorable results. Instead of finding significant
price reversals, Kryzanowski and Zhang (1992) find that the Canadian stock market exhibits
significant price continuation behavior, which does not support contrarian investments. This
is also refuted by later studies that conclude mean-reversion tendency (see for example,
Bauman and Miller [1997]).
In view of the accumulated weight of the evidence from past studies, the finance
academic fraternity agrees that value investment strategies, on average, outperform growth
investment strategies. The only polemical issue about the contrarian strategy is the rationale
for its superior performance.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
35
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Rationale for Superior Performance of Contrarian Strategies
Competing explanations for contrarian supremacy include risk premiums (Fama and
French, 1993, 1995, 1996; Petkova and Zhang, 2004), systematic errors in investors’
expectations and analysts’ forecasts – i.e. naïve investor expectations of future growth and
research design induced bias (see for example, La Porta et al., 1997; Bauman & Miller, 1997;
La Porta, 1996; Dechow & Sloan, 1997; Lakonishok et al., 1994; Lo and MacKinlay, 1990;
Kothari et al., 1995) and the existence of market frictions (Amihud and Mendelson, 1986)
.The traditional view, led by Fama and French (1993, 1995, 1996), is that the superior
performance is a function of contrarian investment being relatively risky (see also Chan,
1988; Ball and Kothari, 1989; Kothari and Shanken, 1992.). However, Lakonishok et al.
(1994), MacKinley (1995), La Porta et al. (1995, 1997), Daniel and Titman (1996) have
found that risk-based explanations do not provide a credible rationale for the observed return
behaviour (see Jaffe et al., 1989; Chan et al., 1991; Chopra et al., 1992; Capaul et al., 1993;
Dreman and Lufkin, 1997; Bauman et al., 1998, 2001; Nam et al., 2001; Gomes et al., 2003
and Chan and Lakonishok (2004)).
The behavioral finance paradigm recognizes psychological influences on human
decision-making in which experts (in this case, investors) tend to focus on, and overuse,
predictors of limited validity (i.e., earnings trend in the recent past) in making forecasts (see
Covel and Shumway, 2005). In view of systematic errors in investors’ expectations and
analysts’ forecasts, it has been argued that a significant portion of value stocks’ superior
performance is attributable to earning surprises (see De Bondt and Thaler, 1985; Lakonishok
et al., 1994; La Porta, 1996; Chan et al., 2000, 2003; Chan and Lakonishok, 2004; Jegadeesh
et al., 2004). According to Dreman and Berry (1995) and Levis and Liodakis (2001), positive
and negative earnings surprises have an asymmetrical effect on the returns of value and
growth stocks. Positive earning surprises have a disproportionately large positive impact on
value stocks while negative surprises have a relatively benign effect on such stocks (see also
Bauman and Miller, 1997).
Furthermore, analysts and institutional investors may have their own reasons for
gravitating toward growth stocks. Analysts have self-interest in recommending successful
stocks to generate trading commissions and more investment banking business. Moreover,
growth stocks are typically in ‘promising’ industries, and are thus easier to promote in terms
of analyst reports and media coverage (Bhushan, 1989; and Jegadeesh et al., 2004). These
considerations play into the career concerns of institutional money managers (Lakonishok et
al., 1994). Another important factor is that most investors have shorter time horizons than are
required for value strategies to consistently pay off (De Long et al., 1990; Shleifer and
Vishny, 1990). In addition, institutional investors act in a fiduciary capacity. Pension fund
trustees, in particular, are expected to behave as an “ordinary man of prudence”. This implies
that they must go with the crowd (i.e. opt for glamour stocks. The result of all these
considerations is that value stocks/glamour stocks become under-priced/overpriced relative to
their fundamentals. Due to the limits of arbitrage (Shleifer and Vishny (1997)), the mispricing
patterns can persist over long periods of time.
A third hypothesis that has been postulated for the superiority of the contrarian strategy is
that the reported cross-sectional return differences is an artifact of the research design and the
database used to conduct the study (Black, 1993; Kothari et al., 1995). Thus, the abnormal
returns would be reduced or vanish if different methodology and data were used. Such
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
36
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
researchers argue that the superior returns are the result of survivor biases in the selection of
firms (Banz and Breen, 1986), look-ahead bias (Banz and Breen, 1986), and a collective datasnooping exercise by many researchers sifting through the same data (Lo and MacKinlay,
1990). Finally, the database is limited to a relatively short sample period (Davis, 1994). The
data-snooping explanation has been controverted by Lakonishok et al. (1994), Davis (1994,
1996), Fama and French (1998), Bauman and Conover (1999), Bauman et al., (2001), and
Chan and Lakonishok (2004) who used databases that are free of survivorship bias and/or
fresh data that previously have not been used for such analysis to confirm the superior
performance of value strategy.
Furthermore, two features of value investing distinguish it from other possible anomalies.
According to Chan and Lakonishok (2004), many apparent violations of the efficient market
hypothesis, such as day-of-the-week patterns in stock returns, lack a convincing logical basis
and the anomalous pattern is merely a statistical fluke that has been uncovered through data
mining. The value premium, however, can be tied to ingrained patterns of investor behavior
or the incentives of professional investment managers.
In view of the analogy between value stock and high income producing property
(henceforth called value property), the features of the contrarian investment strategy may
apply to property investment. Therefore, it is hypothesized that:
a).
b).
c).
d).
value properties generate higher returns than growth properties;
value property investment is riskier than growth property investment;
investors naively extrapolate past performance into future expectations; and
the returns of value and growth properties are mean-reverting.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
These hypotheses will be operationalized through statistical tests, and where possible,
stochastic dominance test.
DATA SOURCING AND MANAGEMENT
A growth real estate investor prefers properties with a low initial yield to properties with
high initial yield. The investor chooses to exchange immediate cash flows for higher future
cash flows (in the form of potential capital appreciation and/or rental growth) that are worth
more at the date of the purchase, depending on the investor’s opportunity cost of capital. On
the other hand, a value property investor prefers to receive a high initial yield rather than to
wait for future income or uncertain capital growth. The paper uses the Jones Lang Lasalle
Real Estate Intelligence Service-Asia (JLL REIS-Asia), the Property Council of New
Zealand, the Property Council of Australia and NCREIF property databases to classify 73
office property sub-markets, 52 industrial property sub-markets and 48 retail property submarkets into value/growth sub-markets on the bases of yields (see Appendix A), i.e. E/P ratio.
The data for the office and industrial property markets are from 1985Q1 to 2005Q3 while the
retail property market data are from 1992Q1 to 2005Q3.
JLL REIS-Asia dataset consists of ex post quarterly (since 1994) and ex-ante annual
(forecasts for the next 4 years) capital and rental values of prime commercial properties for 16
Asia real estate market sectors (i.e. eight retail sectors and eight office sectors). The capital
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
37
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
and rental values of commercial real estate assets (office and retail) in the eight cities are
based on a basket of 30 prime commercial buildings per sector in each city. Rental values are
based on actual rents while the capital values are based on transactions and estimated
valuations. The JLL REIS-Asia ex ante data are derived from JLL’s proprietary quantitative
forecasting and the consensus views of the JLL network of branch offices in Asian cities,
namely: Singapore (the Raffles Place CBD), Beijing, Shanghai, Hong Kong (the Central &
major business districts), Bangkok, Manila (Makati CBD), Kuala Lumpur and Jakarta3. The
criteria for selecting investment grade offices for the dataset are the same for all the markets
in the sample. Thus, the dataset provides a basis for comparing like with like. Similarly, the
data from the Property Council of New Zealand and the Property Council of Australia are
based on market rentals and valuations. The quality of these data is attested by the fact that
they have been subsumed by the IPD. All the datasets are extensively used by researchers.
The only caveat about the use of different datasets is that one cannot guarantee that the
quality of all the datasets is the same. However, the datasets are of very good quality to
provide credible results.
The initial yields are measured in U.S. dollars. Decile portfolios are formed on the basis
of the end-of-previous-quarter’s initial yield. The top decile of the sample with the highest
initial yield is classified as value property (Vp) portfolio while the bottom decile with the
lowest initial yield is classified as growth property (Gp). Each decile is treated as a portfolio
composed of equally weighted properties. The portfolios are reformulated only at the end of
each holding period. This system of classification is consistent with the finance literature (see
for example, Chan et al. [1991] and Bauman et al. [1998, 2001]).
The classification of the property sub-markets into Vp and Gp portfolios is followed by an
examination of the relative performances of the portfolios. If there is evidence of a value
premium in any of the sampled property sector markets, the underlying reasons behind the
relative superiority of Vp will be discussed.
THE CONTRARIAN STRATEGY MODEL
The performances of both the value and growth properties for the office and industrial
sectors are compared on a 5-year, 10-year, 15-year and entire holding-period (of up to 83
quarters) horizons while those for the retail sector are compared on 5-year, 10-year and entire
holding-period (of up to 55 quarters). Medium and long term investment horizons are the
focus of analyses as real estate investors usually invest long (Ball, 1998). Periodic (i.e.
quarter-by-quarter) return measure is used in the evaluation of the relative superiority of the
performance of Vp and Gp portfolios. The periodic returns are quantified as simple holding
period returns. Thus, the simple holding period returns are calculated for each quarter and
compounded to obtain the multi-year holding-period (e.g. 5-year investment horizon) returns
as defined in equation (1).
rt = [(1 + r1 )(1 + r2 )...(1 + rm )] − 1 (Levy, 1999),
where
r1, r2…rm = return for each quarter of the period m.
m
= number of quarters for the holding period.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
(1)
38
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Compared to simply adding the returns for all quarters of a given period, equation (1) is
more accurate (Sharpe et al., 1998). The periodic quartile returns for each holding- period
horizon are averaged across the full period of study to determine the time-weighted average
return. Arithmetic mean is most widely used in forecasts of future expectations and in
portfolio analysis (Geltner and Miller, 2001). Each value-growth spread (i.e. value premium)
is then computed by subtracting the mean return on a Gp portfolio from that on the
corresponding Vp portfolio.
The pooled-variance t test and separate-variance t test are then used to determine whether
there is a significant difference between the means of the Vp and Gp portfolios. If the p-value
is smaller than the conventional levels of significance (i.e. 0.05 and 0.10), the null hypothesis
that the two means are equal will be rejected:
H 0 :μ value = μ growth
H 1 : μ value ≠ μ growth
The next step is to determine whether any difference in returns is a function of variation
in risk, using a more direct evaluation of the risk-based explanation that focuses on the
performance of the value and growth properties in ‘bad’ states of the world. Traditional
measures of risk such as standard deviation of returns, risk-to-return ratio (i.e. coefficient of
variation – CV) and return-to-risk ratio will be utilized.
The Levene’s Test is used to test the equality of the variances for the value and growth
properties:
H 0 : σ 2 value = σ 2 growth
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
H 1 : σ 2 value ≠ σ 2 growth
Performance in ‘Bad’ States of the World
According to Lakonishok et al. (1994), value strategies would be fundamentally riskier
than glamour strategies if:
i) they under-perform glamour strategies in some states of the world; and
ii) those are on average ‘bad’ states of the world, in which the marginal utility of wealth
is high, making value strategies unattractive to risk-averse investors.
Periods of severe stock market declines are used as a proxy for ‘bad’ states of the world.
This is because they generally correspond to periods when aggregate wealth is low and thus
the utility of an extra dollar is high. The approach of examining property performance during
down markets also corresponds to the notion of downside risk that has gained popularity in
the investment community (Chan and Lakonishok, 2004). If the above tests confirm the
superiority of value properties, stochastic dominance will be used to ascertain the optimality
of the value property investment strategy.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
39
STOCHASTIC DOMINANCE
The most widely known and applied efficiency criterion for evaluating investments is the
mean-variance model. An alternative approach is the stochastic dominance (SD) analysis,
which has been employed in various areas of economics, finance and statistics (Levy, 1992;
Al-khazali, 2002; Kjetsaa and Kieff, 2003). The efficacy and applicability of SD analysis, and
its relative advantages over the mean-variance approach have been discussed and proven by
several researchers including Hanoch and Levy (1969), Hadar and Russell (1969), Rothschild
and Stiglitz (1970), Whitmore, 1970, Levy (1992), Al-khazali (2002) and Barrett and Donald
(2003). According to Taylor and Yodder (1999), SD is a theoretically unimpeachable general
model of portfolio choice that maximizes expected utility. It uses the entire probability
density function rather than simply summarizing a distribution’s features as given by its
statistical moments.
Stochastic Dominance Criteria
The SD rules are normally specified as first, second, and third degree SD criteria denoted
by FSD, SSD, and TSD respectively (see Levy, 1992; Barrett and Donald, 2003; Barucci,
2003). There is also the nth degree SD. Given that F and G are the cumulative distribution
functions of two mutually exclusive risky options X and Y, F dominates G (FDG) by FSD,
SSD, and TSD, denoted by FD1G, FD2G, and FD3G, respectively, if and only if,
F ( X ) ≤ G ( X ) for all X (FSD)
(2)
∫ [G(t ) − F (t )]dt ≥ 0 for all X (SSD)
(3)
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
x
−∞
∫ ∫ [G(t ) − F (t )]dtdυ ≥ 0 for all X, and
x
υ
−∞ −∞
E F ( X ) ≥ E G ( X )(TSD )
(4)
The FSD (also referred to as the General Efficiency Criterion – Levy and Sarnat, 1972)
assumes that all investors prefer more wealth to less regardless of their attitude towards risk.
The SSD is based on the economic notion that investors are risk averse while the TSD posits
that investors exhibit decreasing absolute risk aversion (Kjetsaa and Kieff, 2003). A higher
degree SD is required only if the preceding lower degree SD does not conclusively resolve the
optimal choice problem. Thus, if FD1G, then for all values of x, F(x) ≤ G(x) or G(x) - F(x) ≥
0. Since the expression cannot be negative, it follows that for all values of x, the following
must also hold:
∫ [G(t ) − F (t )]dt ≥ 0 ; that is, FD G (Levy and Sarnat, 1972; Levy, 1998)
x
−∞
2
Furthermore, the SD rules and the relevant class of preferences Ui are related in the
following way:
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
40
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
FSD: F ( X ) ≤ G ( X ) ∀X ⇐⇒ E F U ( X ) ≥ EGU ( X )
SSD:
∫
TSD:
x
∀u ∈ U 1 ,
F (t )dt ≥ ∫ G (t ) dt ∀X ⇐⇒ E F U ( X ) ≥ EGU ( X ) ∀u ∈ U 2 ,
x
−∞
x
−∞
υ
∫ ∫
−∞ −∞
F (t ) dtdυ ≥ ∫
x
∫
x
−∞ −∞
(5)
(6)
G (t ) dtdυ ∀X ⇐⇒ E F U ( X ) ≥ EGU ( X )
∀u ∈ U 3 , and E F ( X ) ≥ EG ( X ) ,
(7)
where U i = utility function class (i =1, 2, 3)
U 1 includes all u with u '≥ 0 ;
U 2 includes all u with u '≥ 0 and u ' ' ≤ 0 ; and
U 3 includes all u with u '≥ 0 , u ' ' ≤ 0 and u ' ' ' ≥ 0 .
In other words, a lower degree SD is embedded in a higher degree SD. The economic
interpretation of the above rules for the family of all concave utility functions is that their
fulfilment implies that E F U ( x ) > E GU ( x ) and E F ( x ) > E G ( x ) ; i.e. the expected utility
and return of the preferred option must be greater than the expected utility and return of the
dominated option.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
EMPIRICAL MODEL ESTIMATION – A
TEST OF THE EXTRAPOLATION MODEL
Following the evaluation of the risk characteristics of the Vp and Gp portfolios, the next
task is to investigate the relationship between the past, the forecasted, and the actual future
growth rates. This relationship is largely consistent with the predictions of the extrapolation
model. The essence of extrapolation is that investors are excessively optimistic about growth
properties and excessively pessimistic about value properties. A direct test of extrapolation
(Lakonishok et al. (1994)), then, is to look directly at the actual future rental income and
capital growth rates of value and growth properties, and compare them to:
a) past growth rates and
b) expected growth rates as implied by the initial yields.
If naïve extrapolation is established, the variance ratio test will be used to show that naïve
extrapolation is a credible explanation to the relative superiority of the contrarian strategy.
Variance Ratio Test
The variance ratio, which measures the randomness of a return series, is calculated by
dividing the variance of longer intervals’ returns by the variance of shorter intervals’ returns
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
41
(for the same measurement period. The result is normalized to 1 by dividing it by the ratio of
the longer to the shorter interval. The test assumes that if a return series follows a random
walk, the variance of its k-differences should be k times the variance of its first difference
(Poterba and Summers, 1988).
Assuming that yt denotes a time series consisting of T observations, the variance ratio of
the k-th difference is calculated as follows (see Lo and MacKinlay, 1988; Poterba and
Summers, 1988; Belaire-Franch and Oppong, 2005):
VR(k ) =
σ 2 (k )
,
σ 2 (1)
(8)
where
VR(k): is the variance ratio of the series k-th difference
σ 2 (k ) : is the unbiased estimator of 1/k of the variance of the series k-th difference
σ 2 (1) : is the variance of the first–differenced return series
k: is the number of the days of the base observations interval, or the difference interval.
The estimator of the k-period difference,
σ 2 (k ) , is computed as:
σ 2 (k ) = 1 ∑ ( y t + ... + y t − k +1 − kμ̂ )
T
T
(9)
t =k
where
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
μ̂ =
1 T
∑ yt ; while the unbiased estimator of variance of the first difference,
T t =1
σ 2 (1) = 1 ∑ ( yt − μˆ ) 2
T
T
σ 2 (1) , is:
(10)
t =1
A variance ratio greater than 1 suggests that the shorter-interval returns trend within the
duration of the longer interval (i.e. the return series is positively serially correlated).
Conversely, a variance ratio less than 1 implies that the return series is negatively serially
correlated (i.e. the shorter-interval returns are mean reverting within the duration of the longer
interval.
Performance of the Contrarian Strategy
Exhibits 1 to 4 clearly demonstrate the superiority of the contrarian strategy in each of the
holding periods under consideration. The value portfolio for each property sector
outperformed the corresponding growth portfolio. The value industrial property portfolio, in
particular, recorded 100% positive value-growth spread for all the investment formation
horizons (Exhibits 1-4a). In other words, the value industrial property portfolio outperformed
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
42
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
its growth counterpart in every holding period. The mean value/growth industrial portfolio
returns for the 5, 10, 15 and more than 15 years holding periods are 163.59%/40.77%,
405.55%/107.46%, 1023.36%/187.18% and 1992.29%/258.69% respectively (Exhibit 5 – full
details are obtainable from authors). This implies that an investor who adopted the contrarian
strategy over the more than 15-year holding period would have earned, on average, 1733.6%
more on each dollar invested than the one who invested in glamour industrial properties over
the same period.
Similarly, the value retail property portfolio had spectacular performance by registering
100% value-growth spread for the 10 and more than 10 years holding periods (Exhibits 2 and
4a). Over the 5-year investment formation horizons, however, the value retail property
portfolio outperformed its glamour counterpart in 35 of the 36 holding periods (Exhibit 1).
The mean value/growth retail property portfolio returns for the 5, 10 and more than 10 years
holding period are 201.54%/65.62%, 810.85%/143.7% and 980.84%/203.76% respectively
(Exhibit 5 – full details are obtainable from authors).
Exhibit 1: Value-Growth Spread (5-Year Holding Period
600.00
500.00
Cumulative Spread
300.00
200.00
100.00
0.00
-100.00
-200.00
85
q1
-8
9q
85
4
q4
-9
0q
86
q3 3
-9
1q
87
2
q2
-9
2q
88
q1 1
-9
2q
88
q4 4
-9
3q
89
3
q3
-9
4q
90
q3 2
-9
5q
91
2
q2
-9
6q
92
q1 1
-9
6q
92
4
q4
-9
7q
93
q3 3
-9
8q
94
2
q2
-9
9q
95
q1 1
-9
9q
95
4
q4
-0
0q
96
q3 3
-0
1q
97
2
q2
-0
2q
98
q1 1
-0
2q
98
4
q4
-0
3q
99
q3 3
-0
4q
00
2
q2
-0
5q
1
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
400.00
Period
Industrial Sector
Office Sector
Exhibit.1.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Retail Sector
Value Versus Growth Real Estate Investment Strategy
43
Exhibit 2: Value-Growth Spread (10-Year Holding Period)
1200
Cumulative Spread
1000
800
600
400
200
0
85
Q1
-94
Q4
85
Q3
-95
Q2
86
Q1
-95
Q4
86
Q3
-96
Q2
87
Q1
-96
Q4
87
Q3
-97
Q2
88
Q1
-97
Q4
88
Q3
-98
Q2
89
Q1
-98
Q4
89
Q3
-99
Q2
90
Q1
-99
Q4
90
Q3
-00
Q2
91
Q1
-00
Q
91
4
Q3
-01
Q2
92
Q1
-01
Q4
92
Q3
-02
Q2
93
Q1
-02
Q4
93
Q3
-03
Q2
94
Q1
-03
Q4
94
Q3
-04
Q2
95
Q1
-04
Q4
95
Q3
-05
Q2
-200
Period
Industrial Sector
Office Sector
Retail Sector
Exhibit 2.
Exhibit 3: Value-Growth Spread (15-Year Holding Period)
1400
Cumulative Spread
1000
800
600
400
200
Industrial Sector
Exhibit 3.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Office Sector
30
5Q
2
4Q
4
90
Q
10
4Q
2
Period
90
Q
30
3Q
4
89
Q
10
3Q
2
89
Q
30
2Q
4
88
Q
30
10
88
Q
10
30
2Q
2
1Q
4
87
Q
1Q
2
87
Q
10
30
0Q
4
86
Q
86
Q
85
Q
19
9Q
4
0Q
2
0
85
Q
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
1200
44
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Exhibit 4a: Value-Growth Spread (Holding Period Exceeding 15
Years)
4500
Cumulative Spread
4000
3500
3000
2500
2000
1500
1000
500
85
Q1
85 04Q4
Q2
-04
Q4
85
Q3
-04
85
Q4
Q4
86 04Q4
Q1
-04
Q4
86
Q2
86 04Q4
Q3
-04
Q4
86
Q4
-04
87
Q 1 Q4
87 04Q4
Q2
-04
Q4
87
Q3
87 04Q4
Q4
-04
Q4
88
Q1
-04
88
Q 2 Q4
88 04Q4
Q3
-04
Q4
88
Q4
-04
89
Q4
Q1
-04
Q4
89
Q2
-04
89
Q 3 Q4
89 04Q4
Q4
-04
Q4
0
Period
Industrial Sector
Office Sector
1400
1200
1000
800
600
400
200
0
92
Q1
-04
Q4
92
Q2
-04
Q4
92
Q3
-04
Q
4
92
Q4
-04
Q4
93
Q1
-04
Q4
93
Q2
-04
Q4
93
Q3
-04
Q4
93
Q4
-04
Q4
94
Q1
-04
Q4
94
Q2
-04
Q4
94
Q3
-04
Q4
94
Q4
-04
Q4
95
Q1
-04
Q4
Cumulative Spread
Exhibit 4b: Retail Sector Value-Growth Spread
(Holding Period Exceeding 10 Years)
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Period
Retail
Exhibit 4.
Glamour office property portfolio did better than its industrial and retail counterparts.
However, the better performance was nothing compared to the value office property portfolio.
The value office property portfolio outperformed the growth office property portfolio in 39
out of the 61 five-year holding periods. In other words, the growth portfolio outperformed the
value portfolio in 22 (out of the 61) investment formation periods between 1994Q1 and
1999Q2 (Exhibit 1 – full details are obtainable from authors). However, the superiority of the
contrarian strategy is evident over the longer investment horizons (Exhibits 2-4). Over the 10year investment horizon, the value office portfolio outperformed its growth counterpart in 36
of the 41 formation periods (Exhibit 2). Furthermore, the superior performance of the
contrarian strategy is attested by the 100% positive value-growth spread for the 15 and more
than 15 years formation periods (Exhibits 3 and 4). The mean return value/growth office
property portfolio returns for 5, 10, 15 and more than 15 years holding period are
102.6%/35.29%, 275.12%/66.05%, 944.65%/96.26% and 1929.81%/125.75% respectively
(Exhibit 5). Thus, a dollar invested in value office property portfolio over the entire
investment horizon, would have earned, on average, 1804.06% more than a dollar invested in
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
45
growth office property portfolio. It is worth noting that the differences between the mean
returns for both portfolios (i.e. the value premium) are statistically significant at both the 0.01
and 0.05 levels (Exhibit 6a).
Exhibits 7-9 clearly demonstrates that VpD1Gp for all the holding periods under
consideration – i.e. the value portfolios are the most efficient (and therefore the optimal)
choice. This implies that value portfolios stochastically dominate growth portfolios in the
first, second and third order. In other words, the value portfolios statistically prognosticated a
higher probability of success than the growth portfolios. For example, Exhibit 8b shows that
there was about 95% and 60% probability that the 10-year holding period return for value and
growth office portfolios respectively was greater than or equal to 40%. Thus, value portfolio
investment should have been preferable to both risk averters and risk lovers (Kjetsaa and
Kieff, 2003; Levy and Sarnat, 1972).
Exhibit 5. Exhibit 5: Descriptive Return Statistics.
Mean Return (%)
Value
Growth
Office
Quarterly
5.88
0.65
5 Years
102.6
35.29
10 Years
275.12
66.05
15 Years
944.65
96.26
> 15 Years 1929.81 125.75
Industrial Quarterly
6.03
0.98
5 Years
163.59
40.77
10 Years
405.55
107.46
15 Years
1023.36 187.18
> 15 Years 1992.29 258.69
Retail
Quarterly
4.05
1.88
5 Years
201.54
65.62
10 Years
810.85
143.7
> 10 Years 980.84
203.76
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Sectors
Holding
Period
Standard Deviation
Value
Growth
7.62
3.67
109.72 58.12
191.53 65.39
327.07 37.55
1267.04 45.51
5.27
2.29
119.34 33.88
178.5
61.13
233.14 43.55
964.00 88.99
4.96
2.00
82.76
34.16
419.92 53.41
268.42 20.51
Coefficient of Variation
Value Growth
1.3
5.60
1.07
1.65
0.7
0.99
0.35
0.39
0.66
0.36
0.87
2.32
0.73
0.83
0.44
0.57
0.22
0.23
0.48
0.34
1.23
1.06
0.41
0.52
0.52
0.37
0.27
0.10
Exhibit 6a: Equality of Means Test.
Sectors
Holding
Period
ValueGrowth
Spread (%)
Office
Quarterly
5 Years
10 Years
15 Years
5.23
67.32
209.08
848.4
t-test
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Test statistic t
p-value
 
 
5.63
0.000
Reject
Reject
5.63
0.000
Reject
Reject
4.18
0.000
Reject
Reject
4.18
0.000
Reject
Reject
6.53
0.000
Reject
Reject
6.53
0.000
Reject
Reject
11.54
0.000
Reject
Reject
11.54
0.000
Reject
Reject
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
46
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
> 15 Years
1804.06
Industrial
Quarterly
5 Years
10 Years
15 Years
> 15 Years
5.04
122.82
298.1
836.18
1733.60
Retail
Quarterly
5 Years
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
10 Years
> 10 Years
2.16
135.91
667.15
777.08
Exhibit 6a (Continued)
Pooled6.52
variance
Separate6.52
variance
Pooled8.00
variance
Separate8.00
variance
Pooled7.92
variance
Separate7.92
variance
Pooled10.48
variance
Separate10.48
variance
Pooled17.27
variance
Separate17.27
variance
Pooled8.21
variance
Separate8.21
variance
Pooled3.00
variance
Separate3.00
variance
Pooled9.11
variance
Separate9.11
variance
Pooled6.30
variance
Separate6.30
variance
Pooled10.41
variance
Separate10.41
variance
Exhibit 6b: Equality of Variance Test
Standard Deviation
Holding
Sectors
Period
Value
Growth
Office
Quarterly
7.62
3.67
5 Years
109.72
58.12
10 Years
191.53
65.39
15 Years
327.07
37.55
> 15 Years
1267.04 45.51
Industrial Quarterly
5.27
2.29
5 Years
119.34
33.88
10 Years
178.50
61.13
15 Years
233.14
43.55
> 15 Years
964.00
88.99
Retail
Quarterly
4.96
2.00
5 Years
82.76
34.16
10 Years
419.92
53.41
> 10 Years
268.42
20.51
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.002
Reject
Reject
0.002
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
0.000
Reject
Reject
F-test
p-value
statistics
4.33
3.56
8.58
101.39
775.15
5.30
0.08
8.53
28.65
117.35
6.144
5.87
61.81
171.34
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
 
 
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Reject
Value Versus Growth Real Estate Investment Strategy
47
The relative superiority of the value portfolios is confirmed by the results of stochastic
dominance test presented in Exhibits 7-9
Cumulative Probability
Exhibit7a: Stochastic Dominance Analysis for (Industrial Sector) 5Year Holding Period
1.00
0.80
0.60
0.40
0.20
0.00
-8 2. 5. 12 30 42 57 61 68 76 87 10 14 19 21 35
.0 46 88 .8
.
.
.
.
.
.
.
5
4
5
7
0
0
8 95 77 65 33 59 75 67 .04 .27 .00 .62 .02
Return (%)
Value
Growth
1.00
0.80
0.60
0.40
0.20
0.00
-1
.3
1
44
.
25
73
7
4
3
3
3
2
2
2
1
1
8
1
.5 3.3 05. 36. 66. 10. 60. 80. 08. 37. 90. 85. 46.
6
10 26 19 80 58 14 91 51 77 50 13
2
Return (%)
Value
Growth
Exhibit 7c: Stochastic Dominance Analysis (Industrial
Sector) for 15-Year Holding Period
Cumulative Probability
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Cumulative Probability
Exhibit 7b: Stochastic Dominance Analysis for (Industrial
Sector) 10-Year Holding Period
1.00
0.80
0.60
0.40
0.20
0.00
10 14 17 18 19 19 22 24 32 82 88 91 10 11 12 12
4. 4. 0. 3. 0. 4. 1. 6. 3. 7. 7. 7. 80 88 46 78
57 37 09 01 08 31 61 90 47 23 31 63 .2 .4 .4 .5
1
0
6
4
Return (%)
Value
Growth
Exhibit 7. Contiuned
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
48
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Cumulative Probability
Exhibit 7d: Stochastic Dominance Analysis (Industrial
Sector) for Investment Horizon Exceeding 15 Years
1.00
0.80
0.60
0.40
0.20
0.00
18 20 21 23 29 50 97 14 18 27 34
4
2
9
0
2
2
1
3
6
4.
6
20 .88 .10 .90 .56 .99 .69 3.0 0.3 9.3 9.3
0
3
2
9
Return (%)
Value
Growth
Exhibit 7.
C u m u lative Prob ab ility
Exhibit 8a: Stochastic Dominance Analysis (Office Sector) for 5-Year Holding
Period
1.000
0.800
0.600
0.400
0.200
0.000
-3
4 .0
8
-2
1.5
5
-1
1 .0
3
7 .6
8
22
.81
39
.8 8
48
57
.6 5
.60
87
.8 2
10
5.9
8
12
2 .1
Value
Growth
Exhibit 8b: Stochastic Dominance Analysis (Office Sector) for
10-Year Holding Period
1.000
Cumulative
Probability
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Return (%)
0.800
0.600
0.400
0.200
0.000
57
47
30
19
13
11
94
80
51
40
-3
4. 5.6
6.
7.
4.
7.
8.
3.
.2
.7
.5
.4
1
65
41
24
16
49
03
80
3
2
7
7
Return (%)
Value
Growth
Exhibit 8. Continued
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
0
15
8 .9
3
Value Versus Growth Real Estate Investment Strategy
49
Cumulative Probability
Exhibit 8c: Stochastic Dominance Analysis (Office
Sector) for 15-Year Holding Period
1.000
0.800
0.600
0.400
0.200
0.000
10 11 12 13
35 64 72 10 11 12 12 50 58 94
0
1
9
6
.0
.
.
1.
1
1
5
6
4
2
2 45 15
07 .11 .42 .33 .43 .77 .81 6.6 2.5 3.3 6.6
7
7
2
6
Return (%)
Value
Growth
Exhibit 8d: Stochastic Dominance Analysis (Office Sector) for Investment
Horizon Exceeding 15 Years
1.000
Cumulative Probability
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
52
.05
11
8 .4
0
14
4 .5
58
2 .5
5
7
17
81
.62
43
15
.95
Return (%)
Value
Growth
Exhibit 8.
Is The Superior Performance of Contrarian Strategy a Compensation for
Higher Risk?
According to the traditional school of thought (see literature review), the superiority of
the contrarian strategy is a compensation for higher systematic risk (i.e. higher return is a
reward for higher risk). If the value strategy is fundamentally riskier, it should under-perform
the growth strategy during undesirable/bad states of the world – i.e. times of severe market
decline when the marginal utility of consumption is high (Lakonishok et al., 1994). This
section is therefore aimed at ascertaining if there is any synchrony between “value”
underperformance and “bad” state of the world. Furthermore, traditional measures of risk (i.e.
standard deviation) and risk-adjusted performance indicator (i.e. coefficient of variation) are
used to compare “value” and growth strategies.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
50
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Exhibit 9a: Stochastic Dominance Analysis (Retail Sector)
for 5-Year Holding Period
Cumulative
Probability
1.000
0.800
0.600
0.400
0.200
0.000
-1 -4 -1 0. 1. 1. 1.6 1.7 1.9 2.3 2.8 3.2 3.5 3.7 3.8
3. .7 .5 18 12 48 3 8 8
6
2 4 0 8 0
98 5 4
Return (%)
Value
Growth
Cumulative
Probability
Exhibit 9b: Stochastic Dom inance Analysis (Retail Sector) for 10Year Holding Period
1.000
0.800
0.600
0.400
0.200
0.000
72
94
11
25
17
40
15
37
1
11
10
.8
.7
8.
9.
4.
7.
3.
0.
77 295
9
5
09
42
2 1 90 .
30
51
20
.
.8
53
10
8
Return (%)
Grow th
Exhibit 9c: Stochastic Dominance Analysis (Retail Sector) for
Investment Horizon Exceeding 10 Years
1.000
0.800
0.600
0.400
0.200
0.000
17
7.4
2
18
6.3
4
18
9.2
9
19
5.8
9
21
9.1
9
22
5.4
5
23
7.9
2
74
2.4
1
79
7.3
7
12
08
.03
89
1.1
5
12
76
.47
13
83
.57
Cumulative Probability
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Value
Return (%)
Value
Growth
Exhibit 9.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
51
Exhibit 10: Pacific Basin Real Estate Stock Market
2500
Index (1973=100)
2000
1500
1000
500
0
19
85
Q1
19
87
Q1
19
89
Q1
19
91
Q1
19
93
Q1
19
95
Q1
19
97
Q1
19
99
Q1
20
01
Q1
20
03
Q1
20
05
Q1
Period
Exhibit 10
Exhibit 11. Performance of Portfolios
in Different States of the World.
Tests for equality of Means
Office
Mean
value
Worst period 9.70
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Next worst
Period
Next best
Period
Best Period
Industrial
9.61
2.52
2.05
Worst period 4.41
Next worst
Period
Next best
Period
Best Period
5.69
6.17
8.05
Mean
Mean
Growth Spread
-0.74
-1.59
2.44
2.80
-0.23
-0.32
2.14
2.33
10.44
11.21
0.08
-0.75
4.64
6.01
4.04
5.72
t-test
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Test statistic t
p-value
5.38
0.000
Reject
5.38
0.000
Reject
5.24
0.000
Reject
5.24
0.000
0.06
0.475
0.06
0.475
-0.83
0.206
-0.83
0.207
Reject
Do not
Reject
Do not
Reject
Do not
Reject
Do not
Reject
4.31
0.000
Reject
4.31
0.000
Reject
4.55
0.000
Reject
4.55
0.000
Reject
4.84
0.000
Reject
4.84
0.000
Reject
3.68
0.000
Reject
3.68
0.001
Reject
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
 
52
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Exhibit 11 (Continued)
Retail
Worst period 5.37
Next worst
Period
Next best
Period
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Best Period
3.44
6.38
0.77
0.96
2.50
1.03
3.14
4.41
0.94
5.35
-2.37
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
Pooledvariance
Separatevariance
3.43
0.001
Reject
3.43
0.001
0.56
0.290
0.56
0.290
Reject
Do not
Reject
Do not
Reject
8.28
0.000
Reject
8.28
0.000
-1.55
0.067
-1.55
0.072
Reject
Do not
Reject
Do not
Reject
Exhibits 1-4 show that the value strategy (industrial and retail sectors) virtually never
under-performed the growth strategy in any holding period. It is the value office portfolio that
underperformed “growth” between 1994Q1 and 1999Q2 (5-year holding period), and
1991Q1and 1992Q3 (10-year holding period). Apart from 1997-1999 (the period of SouthEast Asian economic crisis), the periods of “value” underperformance do not coincide with
severe market declines. As far as the industrial and retail sectors are concerned, there is no
underperformance of the value portfolios to be associated with severe market declines as
defined by some pay-off relevant factor.
The performance of the value and growth properties in four states of the world (i.e.
Worst, Next Worst, Next Best, and Best 20 quarters) based on Datastream Indices for the
Pacific Basin Real Estate Stock Market from 1985Q1 to 2005Q3 (Exhibit 10) is presented in
Exhibit 11. After matching the quarterly returns for the growth and value portfolios with the
changes in the real estate stock market return, the mean value-growth spread in each state is
reported together with the corresponding t-statistics for the test that the difference in returns is
equal to zero (Exhibit 11), i.e.
H o : μvalue − μ growth = 0
H o : μvalue − μ growth ≠ 0
Exhibits 10 & 11
Exhibit 11 shows that the value strategy did notably better than the growth strategy in all
the 4 states of the world (industrial sector) except the best state of the world (office and retail
sectors). However, these “value” underperformances are not statistically significant. The null
hypothesis is rejected for all 4 states of the world (industrial sector), the “Worst” and “Next
Worst” (office), and “Worst” and “Next Best” (Retail) states of the world to conclude that
there is statistical difference between the means of the two populations. It is evident from
Exhibit 11 that the superior performance of the value strategy was skewed towards negative
market return months rather than positive market return months. The evidence indicates that
there are no significant traces of a conventional asset pricing equilibrium in which the higher
returns on the value strategy are compensation for higher systematic risk.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
53
The volatility of the portfolios’ returns during the period of study is presented in Exhibit
5. The results show that value portfolios recorded higher standard deviation of returns than
growth portfolios for all the holding periods and for the three property sectors. The results
presented in Exhibit 6b indicate that the higher value portfolio standard deviations are
significantly different, at the 0.01 level, from those of the growth properties. However, since
the mean returns and variances of the two portfolios are different, the coefficient of variation
(CV) is a more appropriate risk measure for comparison. The CVs in Exhibit 5 imply that the
industrial and office sectors value portfolios were safer than the growth portfolios for all the
holding periods except the more than 15-year holding period. Furthermore, the retail value
portfolio was safer (based on CV) than its growth counterpart in only the 5-year holding
periods – It was riskier than the retail growth portfolio in the remaining two holding periods
(Exhibit 5). However, since value portfolios stochastically dominate growth portfolios in all
the holding periods (exhibits 7-9), the latter is riskier than the former (Biswas, 1997). Hence,
a risk model based on differences in standard deviation alone may not be a credible
explanation for the superior performance of value properties.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
POST-MODEL ESTIMATION – A TEST OF THE EXTRAPOLATION
MODEL
The paper provides empirical evidence to verify whether excessive extrapolation and
expectational errors characterize growth and value strategies. First, the study period is divided
into two: past (pre-portfolio formation) and future (post-formation) performances (see Panels
B and C respectively of Exhibits 12-14). Exhibits 12-14 present some descriptive
characteristics of the growth and value portfolios with respect to their initial yields, past
growth rates, and future growth rates. Panel A of Exhibits 12-14 reveals that the value
portfolios had higher initial yields than growth portfolios. This is supposed to portend lower
expected growth rates for value properties. Panel B shows that, using several measures of past
growth, including rental income and capital value, the growth portfolio performance for each
sector (in relation to rental income) and for the industrial and retail sectors (relative to capital
value) grew faster than the value portfolios over the pre- portfolio formulation period.
Exhibit 12. Initial Yields, Past and Future Performances
of Value and Growth Properties (Industrial Sector).
Panel A: Initial Yields
1994 Q3
Initial Yield
1994 Q4
Portfolio
Composition
Value
5.16
Ford Lauderdale
Orlando
Tampa
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Growth
2.05
Memphis
Sydney
Brisbane
AuckLand
54
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Year
1985Q1
1985Q2
1985Q3
1985Q4
1986Q1
1986Q2
1986Q3
1986Q4
1987Q1
1987Q2
1987Q3
1987Q4
1988Q1
1988Q2
1988Q3
1988Q4
1989Q1
1989Q2
1989Q3
1989Q4
1990Q1
1990Q2
1990Q3
1990Q4
1991Q1
1991Q2
1991Q3
1991Q4
1992Q1
1992Q2
1992Q3
1992Q4
1993Q1
1993Q2
1993Q3
1993Q4
Panel B: Past Performances of Industrial Properties
Value
Growth
Capital Growth
Rental Growth
Capital Growth
(%)
(%)
(%)
3.49
-10.04
0.33
9.78
-2.77
-0.30
10.42
-10.74
1.11
8.07
-13.11
2.33
14.48
34.09
-0.10
8.26
14.43
4.34
7.47
-13.38
-3.22
-2.62
-8.56
-2.13
-3.56
-0.41
-1.16
5.50
3.74
-1.80
4.99
-1.61
-1.68
19.25
-6.02
-5.43
9.96
-12.62
-0.30
2.03
-28.59
0.55
3.50
-10.86
-0.31
0.71
-13.76
-1.30
-4.77
-14.75
0.07
-7.09
-9.42
0.01
-7.01
-2.32
0.23
-15.02
-8.35
-1.73
-23.97
-3.15
-4.31
-79.20
-17.69
-0.23
-81.46
39.75
-2.43
19.05
26.34
-4.04
13.41
21.41
-1.19
6.68
-9.09
-2.47
8.19
9.93
-2.51
-5.01
7.03
-7.32
-6.58
1.42
-0.89
-10.22
0.80
-3.10
-17.40
-16.83
-2.54
5.87
2.57
-4.30
1.43
16.89
-3.06
-5.41
-1.78
-2.42
-5.58
-18.94
-2.46
-19.91
-1.10
-3.14
Rental Growth
(%)
28.09
17.08
53.13
75.05
1.96
133.92
57.14
25.23
25.14
12.58
-2.32
-2.21
10.18
2.81
14.89
10.39
44.75
6.44
-9.88
85.05
-0.15
0.91
46.27
15.10
49.70
1.62
6.49
43.55
4.04
20.29
-0.33
20.04
6.91
-0.14
7.45
33.85
1994Q1
-45.04
-8.95
-2.73
20.38
1994Q2
73.58
50.95
8.40
8.40
1994Q3
19.71
1.65
2.02
15.55
1994Q4
-6.31
3.17
2.06
32.13
Geometric
Average
Growth Rate
-7.45
-1.47
-1.21
20.43
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
Panel C: Future Performances of Industrial Properties
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Value
Growth
Year
Capital
Growth (%)
Rental
Growth (%)
Capital Growth
(%)
Rental Growth (%)
1995Q1
-9.86
12.95
1.30
6.14
1995Q2
3.55
1.24
1.16
-15.97
1995Q3
-2.22
10.48
-0.04
-14.62
1995Q4
0.49
-1.63
-0.72
35.19
1996Q1
3.43
6.28
2.83
49.37
1996Q2
-0.68
-1.06
0.36
-7.04
1996Q3
-3.21
-0.52
0.55
-11.06
1996Q4
11.81
5.63
0.56
35.99
1997Q1
-1.85
0.48
2.57
8.30
1997Q2
-1.07
-0.81
0.78
-10.67
1997Q3
9.08
1.93
1.59
4.66
1997Q4
1.92
-10.39
2.17
-5.20
1998Q1
-5.03
15.17
0.57
2.19
1998Q2
-1.20
-4.76
4.46
-4.61
1998Q3
-17.24
4.44
1.97
-22.83
1998Q4
-9.80
2.70
0.26
-17.75
1999Q1
-12.91
8.69
0.38
-5.93
1999Q2
15.41
0.36
1.01
0.30
1999Q3
7.36
-1.30
0.80
-8.00
1999Q4
-8.96
5.52
2.19
21.93
2000Q1
-23.09
-1.66
1.29
1.93
2000Q2
-18.13
2.17
1.10
6.85
2000Q3
-109.70
3.38
0.14
-19.98
2000Q4
-51.56
1.09
0.27
19.42
2001Q1
-167.77
2.84
1.02
14.03
2001Q2
401.62
0.71
0.78
6.18
2001Q3
23.38
0.94
1.34
7.47
2001Q4
22.95
-4.79
-0.58
72.33
2002Q1
-21.36
-0.31
-0.63
-2.24
2002Q2
22.64
0.25
-0.29
-4.13
2002Q3
60.71
-4.14
0.84
-10.59
2002Q4
-1.06
8.12
0.40
-6.92
2003Q1
37.16
-0.14
-9.98
23.37
2003Q2
-0.20
-4.44
-0.18
-0.63
2003Q3
12.40
-1.43
-5.64
-21.03
2003Q4
6.83
2.50
-1.56
-3.94
2004Q1
-6.19
-4.01
-11.51
13.44
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
55
56
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Panel C (Continued)
.
2004Q2
-0.26
2.82
2.71
5.39
2004 Q3
30.22
1.22
1.56
-10.12
Geometric
Average Growth
Rate
4.88
1.43
0.10
1.76
Panel C shows that over the subsequent post-formulation years, the relative growth of
rental income and capital value for growth properties was generally quite below expectation.
The figures in Panels B and C represent the incremental growth in performances between
the returns for the preceding and successive quarters’ portfolios, since the analysis is based on
the assumption that portfolios are reformulated at the beginning of each quarter. Thus, the
401.62% capital growth for the value industrial portfolio in 2001Q2 reflects the growth in the
performance of the 2001Q2 portfolio, relative to that of the 2001Q1 portfolio. These
assumptions, which are in consonance with the finance literature, are merely to test the
plausibility of naïve extrapolation being a credible explanation for the value superiority. They
certainly are not intended in any way to imply/suggest that real estate investors do/should
reformulate their portfolio quarterly.
Exhibit 13. Initial Yields, Past and Future Performances of Value and Growth
Properties (Office Sector).
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Panel A: Initial Yields
1994 Q2
Initial Yield
1994 Q3
Portfolio
Composition
Value
8.57
Austin
Bethesda
Houston
San Francisco
Panel B: Past Performances of Office Properties
Value
Capital
Rental
Year
Growth (%)
Growth (%)
1985Q1
0.43
-13.02
1985Q2
12.86
-0.39
1985Q3
24.85
0.02
1985Q4
8.57
-0.26
1986Q1
11.04
-0.40
1986Q2
-6.44
-0.83
1986Q3
-7.64
-0.84
1986Q4
5.82
0.32
1987Q1
5.06
0.32
1987Q2
117.96
0.20
1987Q3
24.25
0.19
Growth
1.69
Melbourne (non-CBD)
Auckland (non-CBD)
Hong Kong (Central)
Hong Kong (Wan Chai)
Shanghai (Puxi)
Growth
Capital
Growth (%)
-0.91
0.39
1.49
0.49
-0.17
-0.59
-2.51
-4.44
-2.38
-18.20
-1.43
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Rental Growth
(%)
17.37
35.80
25.45
11.44
14.22
-11.69
4.50
6.86
16.24
7.55
76.78
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Value Versus Growth Real Estate Investment Strategy
1987Q4
1988Q1
1988Q2
1988Q3
1988Q4
1989Q1
1989Q2
1989Q3
1989Q4
1990Q1
1990Q2
1990Q3
1990Q4
1991Q1
1991Q2
1991Q3
1991Q4
1992Q1
1992Q2
1992Q3
1992Q4
1993Q1
1993Q2
1993Q3
1993Q4
1994Q1
1994Q2
1994Q3
Geometric
Average
Growth
Rate
22.63
15.33
-4.93
-5.23
-0.84
-2.51
11.53
10.11
-3.00
-6.81
-32.37
-123.71
-5.34
-23.60
-30.87
-365.00
24.41
6.29
5.71
6.27
-3.58
4.94
24.32
23.81
12.86
14.16
-26.68
171.69
-0.95
-0.98
-2.75
-2.83
-1.01
0.21
0.98
0.96
0.18
-2.00
-1.69
-1.75
0.28
-1.88
-1.04
-1.20
-2.22
-0.66
-1.04
1.78
-14.67
8.56
5.00
4.60
4.41
4.47
3.04
2.87
-6.17
1.55
-0.87
-0.75
-2.06
0.80
-1.67
1.58
1.45
0.78
-1.84
-0.51
-3.65
-3.08
-0.30
-5.48
-6.24
-2.92
-3.59
-2.09
-5.93
-1.13
-7.79
1.60
-6.90
-0.71
2.66
1.10
7.09
54.31
-2.50
27.26
30.32
3.07
-4.66
8.70
26.72
30.64
43.05
27.99
15.76
16.45
0.12
6.68
32.65
-51.90
0.97
6.47
21.52
-1.54
64.61
21.86
-6.94
23.80
22.93
16.13
6.99
-0.56
-2.14
14.36
57
Recall that the Gordon’s formula (Gordon and Shapiro (1956)) can be rewritten as
⎛ I⎞
k p ⎜ ≡ ⎟ = R N − g p = d , where
⎝ P⎠
k p is the initial yield for property, I is the current rental
income, P is the market price, R N is the required nominal return, and
(gp −d )
is the
rental growth for actual, depreciating properties. These formulae literally imply that, holding
discount rates constant, the differences in expected rental growth rates can be directly
calculated from differences in initial yields. Since the assumptions behind these simple
formulae are restrictive (e.g. constant growth rates, etc.), the paper does not calculate exact
estimates of the differences in expected rental growth rates between value and growth
portfolios. Instead, the paper seeks to ascertain whether the large differences in initial yields
between value and growth properties can be justified by the differences in future rental
growth rates.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
58
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Panel C: Future Performances of Office Properties
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Value
Growth
Year
Capital
Growth (%)
Rental
Growth (%)
Capital
Growth (%)
Rental
Growth (%)
1994Q4
-0.13
9.23
0.00
1.67
1995Q1
1.32
8.39
-6.71
-4.71
1995Q2
1.15
27.02
1.89
-4.24
1995Q3
-0.52
-0.01
0.21
-4.54
1995Q4
0.24
-15.71
-3.50
-4.16
1996Q1
1.09
40.04
2.46
-0.36
1996Q2
1.04
10.40
4.33
53.02
1996Q3
1.40
-10.90
204.54
-0.55
1996Q4
4.02
15.21
16.62
-0.67
1997Q1
0.74
8.91
-7.23
-0.38
1997Q2
2.20
-22.28
-1.03
-0.91
1997Q3
2.75
2.37
-27.62
-0.94
1997Q4
10.79
10.91
38.40
-1.64
1998Q1
6.89
18.48
35.21
-7.33
1998Q2
5.21
6.91
-0.13
-2.97
1998Q3
1.62
17.80
-66.39
-2.73
1998Q4
4.24
1.15
-3.95
-11.83
1999Q1
1.44
34.66
-0.71
-4.29
1999Q2
0.85
2.82
-16.90
-5.25
1999Q3
2.38
-2.91
-27.68
-6.32
1999Q4
1.74
26.21
20.91
0.08
2000Q1
1.74
15.40
-10.90
6.48
2000Q2
3.82
2.66
100.67
6.58
2000Q3
1.88
-7.32
-69.73
7.74
2000Q4
3.37
28.93
9.54
6.16
2001Q1
0.86
38.04
-0.64
-0.41
2001Q2
2.31
-7.03
3.90
-0.62
2001Q3
0.41
1.56
-2.02
-1.44
2001Q4
-1.66
5.59
-56.00
-1.79
2002Q1
-0.94
-0.17
3.76
-3.20
2002Q2
-1.26
-4.00
-11.25
-2.72
2002Q3
-2.25
-5.37
-27.10
-3.20
2002Q4
-5.48
9.78
3.36
-4.41
2003Q1
0.63
4.22
26.10
1.19
2003Q2
-1.05
3.39
-13.64
-5.74
2003Q3
-1.64
-7.55
-1.12
-2.51
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
2003Q4
-1.36
0.78
14.43
1.98
2004Q1
-0.65
9.32
80.07
-0.03
2004Q2
-0.47
-9.12
-5.76
2.80
Geometric
A
-2.41
-0.51
1.21
5.96
Exhibit 14. Initial Yields, Past and Future Performances of
Value and Growth Properties (Retail Sector).
Panel A: Initial Yields
1997 Q4
Initial Yield
1998 Q1
Portfolio Composition
Value
Growth
12.22
1.75
Phoenix
Bangkok
San Diego
Jakarta
New South Wales
Shanghai
Panel B: Past Performances of Retail Properties
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Value
Growth
Year
Capital
Growth (%)
Rental
Growth (%)
Capital
Growth (%)
Rental
Growth (%)
1992Q1
-32.98
-1.00
-0.20
-10.84
1992Q2
26.62
-1.03
0.45
42.40
1992Q3
15.59
-1.14
-3.76
7.81
1992Q4
11.56
-1.17
-5.35
0.04
1993Q1
0.09
-1.35
-0.26
18.47
1993Q2
4.91
-0.79
-0.07
51.52
1993Q3
0.06
-1.94
0.60
5.63
1993Q4
0.03
-1.39
-1.24
34.80
1994Q1
16.87
0.95
0.34
63.78
1994Q2
8.27
1.20
-1.04
6.09
1994Q3
8.22
1.17
0.31
11.37
1994Q4
8.17
1.14
-2.35
25.66
1995Q1
6.64
1.34
-1.47
26.05
1995Q2
-2.90
1.00
0.11
14.02
1995Q3
-3.53
0.97
-0.80
9.27
1995Q4
0.23
0.81
-3.78
28.87
1996Q1
-1.89
-1.10
0.16
12.70
1996Q2
-1.96
-1.26
9.71
39.45
1996Q3
-2.05
-1.42
1.32
4.26
1996Q4
-2.14
-1.60
0.94
11.56
1997Q1
-5.09
-5.75
2.19
18.03
1997Q2
-4.78
-5.26
91.16
0.64
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
59
60
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Panel B (Continued)
1997Q3
-7.28
-7.75
2.00
34.45
1997Q4
-11.81
-11.25
-2.32
5.89
1998Q1
-9.30
-8.63
4.84
3.55
Geometric
A
0.21
-1.87
2.60
17.40
Panel B of Exhibits 12-14 reveal that the average quarterly growth rate for rental income
for the glamour portfolio was 20.43% compared to -1.47% (industrial), 14.36% compared to
0.56% (office) and 17.40% compared to -1.87% (retail) for the value portfolio over the preportfolio formation period.
Panel C: Future Performances of Retail Properties
1998Q2
Value
Capital
Growth (%)
-11.01
Rental
Growth (%)
-10.10
Growth
Capital
Growth (%)
12.39
Rental
Growth (%)
-0.48
1998Q3
-13.75
-12.35
-3.69
8.97
1998Q4
1999Q1
-18.94
0.74
-16.31
3.02
5.96
4.75
45.16
1.64
1999Q2
0.60
2.62
-2.98
3.60
1999Q3
1999Q4
0.47
0.34
2.27
1.95
0.13
12.45
6.54
9.49
2000Q1
2.60
1.83
7.93
-5.70
2000Q2
2000Q3
2.43
2.29
1.73
1.64
4.21
4.16
5.26
3.07
2000Q4
2.16
1.56
-2.67
0.37
2001Q1
2001Q2
2.47
3.22
1.93
2.97
-8.35
-4.43
2.09
-9.58
2001Q3
3.54
3.49
-0.27
4.73
2001Q4
2002Q1
3.13
1.77
2.82
2.24
-7.78
-8.66
3.40
13.37
2002Q2
2.75
3.24
-2.16
-26.49
2002Q3
2002Q4
0.11
1.94
1.01
2.36
6.88
25.46
37.22
1.19
2003Q1
0.69
0.68
28.96
-0.37
2003Q2
2003Q3
3.81
3.66
3.89
2.67
16.96
1.62
3.13
4.92
2003Q4
0.73
0.79
-0.08
1.55
2004Q1
2004Q2
Geometric
4.09
0.15
1.00
-3.24
-6.79
-7.08
-1.23
-1.35
0.71
0.01
2.27
3.65
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Year
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
61
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Every dollar invested in the value portfolio in 1994Q2 (office), 1994Q3 (industrial) and
1997Q4 (retail) had a claim to 8.57, 5.16 and 12.22 cents of the then existing corresponding
rental income while a dollar invested in the growth portfolio was a claim to 1.69, 2.05 and
1.75 cents of the rental income (Panel A of Exhibits 12-14). Ignoring any difference in
required rates of return, the large differences in initial yields have to be justified by an
expectation of higher rental growth rates for glamour than value portfolios over a period of
time. Thus, the expected rental income for the growth portfolio must be higher than the value
portfolio at some future date. Accordingly, investors would like to know the number of
quarters it would take for the rental income per dollar invested in the growth portfolios
(0.0169, 0.0205 and 0.0175) to equate the rental income of the value portfolio (0.0857,
0.0516 and 0.1222), assuming that the differences in past rental income growth rates would
persist. It would take approximately 26 quarters (office), 5 quarters (industrial) and 21
quarters (retail) for such equalization to occur (see Exhibit 15). Note that this equality is
based on a flow basis, not on a present-value basis which would require an even longer time
period over which glamour properties should experience superior growth.
Unfortunately, a comparison of Panels B and C (Exhibits 12-14) show that the relatively
higher expected future growth (implied by the higher growth rate in the pre-formation period)
in the glamour portfolios during the post-formation period was a far cry from reality. The
actual post-formation rental growth rate for glamour portfolios plummeted by 58.49% from
14.36% to 5.96% (office), 91.39% from 20.43% to 1.76% (industrial), and 79.02% from
17.4% to 3.65% (retail) per quarter. Alternatively, the post-formation rental growth rate for
the value portfolios increased by 8.93% from -0.56% to -0.51% (office), 197.28% from 1.47% to 1.43% (industrial) and 100.53% from -1.87% to 0.01%. These results are consistent
with the extrapolation model. Contrarian/glamour investors were pleasantly/unpleasantly
surprised by the post formation portfolio results. Rental is, however, a portion of portfolio
performance. Capital value is an important portion of a portfolios performance and thus, must
be analyzed in relation to the extrapolation model.
Exhibit 15. a: Growth of Industrial Sector’s Rental
Income Per Dollar Invested (4th Quarter 1994)
Quarter
0
1
2
3
Value
Portfolio
5.16
5.08
5.01
4.94
Growth
Portfolio
2.05
2.47
2.97
3.58
Quarter
4
5
Value
Portfolio
4.86
4.78
Growth
Portfolio
4.31
5.19
During the pre-formation period, the capital value growth rates for the glamour
portfolios, -1.21% (industrial) and 2.6% (retail) were higher than those for value portfolios, 7.45% (industrial) and 0.21% (retail). The capital value growth rate for the office glamour
portfolio (-2.14%), in contradistinction, was lower than office value portfolio (6.99%) during
the pre-formation period (Exhibits 12-14). Exhibit 15b: Growth of Office Sector’s Rental
Income Per Dollar Invested (3rd Quarter 1994)
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
62
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Quarter
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Value
Portfolio
8.57
8.52
8.47
8.43
8.43
8.38
8.33
8.28
8.23
8.19
8.14
8.09
8.04
7.99
Growth
Portfolio
1.69
1.93
2.18
2.42
2.66
2.90
3.15
3.39
3.63
3.87
4.12
4.12
4.12
4.36
Quarter
14
15
16
17
18
19
20
21
22
23
24
25
26
Value
Portfolio
7.95
7.90
7.85
7.80
7.75
7.71
7.66
7.61
7.56
7.51
7.47
7.42
7.37
Growth
Portfolio
4.60
4.84
5.09
5.33
5.57
5.81
6.06
6.30
6.54
6.78
7.03
7.27
7.51
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
The results in Exhibits 12-14 reveal that while the capital value growth rate for the
glamour industrial portfolio increased by 108.26% from -1.21% to 0.10%, that for the value
industrial portfolio increased by 165.5% from -7.45% to 4.88% per quarter during the postformation period. Moreover, the capital value growth rate for the retail glamour portfolio
declined by 12.69% from 2.6% to 2.27% while that for the value portfolio increased by
238.1% from 0.21% to 0.71% per quarter in the post-formation period. Once again, the results
are consistent with the extrapolation model.
Exhibit 15c: Growth of Retail Sector’s Rental Income
Per Dollar Invested (3rd Quarter 1994)
Quarter
0
1
2
3
4
5
6
7
8
9
10
Value
Portfolio
12.22
11.99
11.76
11.53
11.31
11.08
10.85
10.62
10.39
10.16
10.16
Growth
Portfolio
1.75
2.05
2.36
2.66
2.97
3.27
3.58
3.88
4.18
4.49
4.49
Quarter
11
12
13
14
15
16
17
18
19
20
21
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value
Portfolio
9.93
9.71
9.48
9.25
9.02
8.79
8.56
8.34
8.11
7.88
7.65
Growth
Portfolio
4.79
5.10
5.40
5.71
6.01
6.31
6.62
6.92
7.23
7.53
7.84
Value Versus Growth Real Estate Investment Strategy
63
Exhibit 16: Variance Ratio Test.
Sector
Office
Investment Horizon
Value Portfolio
Growth Portfolio
4 Quarters
20 Quarters
34.975
0.183
0.806
4.832
40 Quarters
0.190
1.671
60 Quarters
0.144
0.080
0.945
0.586
4 Quarters
20 Quarters
40 Quarters
5.896
0.712
0.298
0.523
0.712
0.659
60 Quarters
0.254
0.479
80 Quarters
4 Quarters
20 Quarters
0.050
2.481
0.354
4.207
0.979
0.165
40 Quarters
1.331
0.722
0.095
0.069
80 Quarters
Industrial
Retail
55 Quarters
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Variance Ratio
However, the results for the office portfolio are inconsistent with the extrapolation model.
The capital value growth rate for the glamour office portfolio increased by 156.54% from 2.14% to 1.21% while that of the corresponding value portfolio declined by 134.48% from
6.99% to -2.41% per quarter. The pertinent question that needs to be addressed at this
juncture is whether, given the post-formation performance of capital value growth rates for
the industrial and office portfolios, the glamour portfolios can outperform the value portfolios
at some time in the future. This is addressed via a mean reversion analysis.
Variance Ratio Test
The results of the variance ratio tests are presented in Exhibit 16. The returns for both
glamour and value portfolios for the three property sectors display mean reversion at long
horizons. However, the office glamour portfolio returns exhibit positive serial correlation
over investment horizons of up to 10 years (40 quarters) while the office value portfolio
display negative serial correlation virtually over all the holding periods. This explains why the
office glamour portfolio outperformed its value counterpart in 22 of the 61 5-year holding
periods (Exhibit 1) as well as 5 of the 41 10-year holding periods (Exhibit 2). On the average,
however, the value strategy outperformed the glamour strategy over the 5 and 10-year holding
periods when the glamour portfolio displayed return inertia.
As far as the industrial sector is concerned, both portfolios displayed mean reversion in
all the holding periods under consideration. This is also true of the retail sector except that the
retail value portfolio exhibited positive serial correlation for holding periods between 5 and
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
64
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
10 years. These results imply that the superior performance of the contrarian strategy is not a
flash in the pan – It will persist in future years.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
CONCLUSION
The paper set out to investigate the comparative advantage(s) of the value and growth
investment strategies to ascertain the sustainability of the superior performance (if any) of the
contrarian strategy. The results of the study indicate that value portfolios for all three property
sectors out-performed (in both absolute, and in most cases, risk-adjusted bases) growth
portfolios over all the holding periods under consideration. A dollar invested in the value
portfolio over 10 years, on the average, earned 209.07% (office), 298.09% (industrial) and
647.15% (retail) more than a dollar invested in the corresponding growth portfolios.
Similarly, a dollar invested in the value portfolio over the entire period of study earned, on
average, 1804.06% (office), 1733.61% (industrial) and 771.08% (retail) more than a similar
investment in the growth portfolio. The difference between the performances of the value
and the growth portfolios are statistically significant at the 0.01 level. Thus, the null
hypothesis that there is no difference between the mean returns for the two portfolios is
rejected.
Furthermore, the superior performances of value portfolios occurred in almost all the four
“states of the world”. The superior performance is not a compensation for higher risk as
measured by the coefficient of variation (CV) for investment horizons of up to 5 years (retail)
and 15 years (office and industrial). These findings are consistent with the contrarian strategy
in finance. It must be noted, however, that the superior performance of the contrarian strategy
for investment horizons of more than 5 years (retail) and 15 years (office and industrial) could
be a compensation for higher risk as measured by the CV. Notwithstanding this caveat, the
relative superiority of the value portfolio for each sector and holding period is confirmed by
stochastic dominance test, which indicates that the value strategy is the optimal choice for
both risk averters and risk lovers. In addition, the variance ratio test reveals that returns for
both value and growth property portfolios exhibit mean reversion at long horizons. This
means that the superior performance of the contrarian strategy is sustainable. The above
results are consistent with the finance literature.
This consistency cannot be attributed to data snooping as the studies in the finance
literature are based on different data. The findings imply that high initial yield office,
industrial and retail portfolios in the sample outperformed their low yield counterparts during
the period under investigation. If the results can be generalized in any way, one may safely
conclude that property investors should seriously consider contrarian real estate investment if
they want to improve the performance of their portfolios.
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Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Appendix A: Countries in the Three Sectors’ Portfolios
Appendix A-1a: Countries in the Office Portfolio
Code Country
Code
Country
1
Altanta
38
San Jose
2
Austin
39
Santa Ana
3
Baltimore
40
Seattle
4
Bethesda
41
Tampa
5
Birmingham
42
Warren
6
Boston
43
Wasington
7
Bridgeport
44
West Palm Beach
8
Cambridge
45
Sydney (CBD)
9
Charlotte
46
Melbourne (CBD)
10
Chicago
47
Brisbane (CBD)
11
Cincinnati
48
Perth (CBD)
12
Columbas
49
Canberra Region Office
13
Dallas
50
Sydney (non-CBD)
14
Denver
51
Melbourne (non-CBD)
15
Edison
52
Auckland (CBD)
16
Ford Lauder
53
Wellington(CBD)
17
Houston
54
Auckland (non-CBD)
18
Kansas City
55
Beijing
19
Lake County
56
Bangkok
20
Los Angeles
57
Hong kong(central)
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Miami
Milwankee
Minneapolis
Newark
new york
Oakland
Orlando
Philadelphia
Phoenix
Pittsburgh
Portland
Raleigh
Sacramento
St Louis
San Antonio
San Diego
San Francisco
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
hong kong(wan chai)
hong kong(tst)
hong kong east
Jarkarta
KL(KLCC)
KL (DC)
Makati
Singapore (Raffles Place)
Singapore (Shenton)
Singapore (Marina)
Shanghai (Puxi)
Shanghai (Pudong)
Seoul(Yoido)
Seoul (Gangnam)
seoul (CBD)
Tokyo
Appendix A-1b: Composition of Value and Growth office Portfolios.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Year
1985Q1
1985Q2
1985Q3
1985Q4
1986Q1
1986Q2
1986Q3
1986Q4
1987Q1
1987Q2
1987Q3
1987Q4
1988Q1
1988Q2
1988Q3
1988Q4
1989Q1
1989Q2
1989Q3
1989Q4
1990Q1
1990Q2
1990Q3
1990Q4
1991Q1
1991Q2
1991Q3
1991Q4
Country Code
Growth
Value
Properties Properties
13
26
13,17,39
49,50
13,43
45,49,50
13,14,17
45,49,50
13,17,37
49,51
13,17,37
49,51
8,13,17
49,51
13,17,26
49,51
13,17,18
49,51
13,17,26
49,51
1,13,18
49,51
13,17,18
49,51
10,13,18
49,51
13,17,37
49,51
10,13,37
49,51
6,34,37
49,51
13,34,37
49,51,57
13,17,26,37 49,51,57
17,22,37
49,51,57
6,17,37
49,51,57
6,17,20,37 49,51,57
17,20,22,23 49,51,57
1,17,22,37 49,51,57
1,6,17,20
49,51,57
1,20,28,37 49,57,65
10,20,26,28 49,57,65
1,6,20,28
49,57,65
6,17,26,28 49,57,65
Year
1995Q3
1995Q4
1996Q1
1996Q2
1996Q3
1996Q4
1997Q1
1997Q2
1997Q3
1997Q4
1998Q1
1998Q2
1998Q3
1998Q4
1999Q1
1999Q2
1999Q3
1999Q4
2000Q1
2000Q2
2000Q3
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
Country Code
Growth
Properties
4,16,45,47,48
23,41,45,47,48
2,17,35,45,47
17,45,46,47,48
11,18,45,47,48
6,12,28,45,48
18,28,45,47,48
6,10,37,45,46,47
11,29,45,46,47,48
24,29,35,45,47,48
2,37,38,39,45,47
2,12,25,29,37,45,47
2,12,18,37,45,47,48
11,13,18,24,37,48
6,11,12,18,25,37,45
8,12,24,26,37,47,48
1,8,11,21,24,26,45
1,12,21,24,26,48,51
12,24,25,26,32,37,46
5,6,10,45,46,47,48
5,24,25,45,46,47,48
4,37,39,42,45,46,47,48,
6,19,45,46,47,48,52
26,27,39,45,46,47,48
5,16,39,42,45,46,48
16,24,26,39,45,47,48
6,11,16,45,46,47,48
5,7,39,45,46,47,48
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value
Properties
54,56,57,61,68
54,56,57,61,68
56,57,61,68,69
56,57,61,68,69
56,57,61,68,69
53,56,61,68,69
56,61,64,68,69
54,56,61,64,68,69
54,56,61,64,68,69
56,57,61,64,68,69
56,61,62,64,68,69
55,56,61,64,68,69
55,56,61,64,68,69
55,56,61,62,64,68
55,56,61,62,64,68
55,56,61,62,64,68
56,61,62,64,70,71,72
56,61,62,64,70,71,72
56,61,62,64,70,71,72
56,61,62,64,70,71,72
55,56,61,64,70,71,72
55,56,61,64,70,71,72
55,56,61,64,70,71,72
55,56,61,64,70,71,72
55,56,61,64,70,71,72
55,56,61,64,70,71,72
55,56,61,64,70,71,72
55,56,61,69,70,71,72
69
70
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
1992Q1
1992Q2
1992Q3
1992Q4
1993Q1
1993Q2
1993Q3
1993Q4
1994Q1
1994Q2
1994Q3
1994Q4
1995Q1
1995Q2
6,14,20,28
1,7,20,28
14,20,28,37
20,23,28,37
10.14,20,28
14,20,28,37
2,18,28,38
17,20,28,40
2,20,38,40
2,17,28,40
2,4,17,37
Growth
Properties
2,17,38,40
2,17,25,33
2,25,38,42
Appendix (Continued)
57,58,59,65
2002Q3 18,21,22,34,39,45,48
57,58,59,65
2002Q4 5,16,21,29,40,45,47,48
49,57,58,59
2003Q1 5,12,17,24,34,35,45,48
7,57,58,59
2003Q2 5,12,21,35,39,45,48
51,57,58,59
2003Q3 4,20,25,34,35,39,45,48
51,54,57,59
2003Q4 9,14,20,29,35,37,39,45
51,54,57,59
2004Q1 4,7,10,34,35,39,44,45
51,54,57,59
2004Q2 4,10,20,21,39,41,44,51
51,54,57,58,68 2004Q3 15,20,25,37,39,44,45,46
51,54,57,58,68 2004Q4 15,16,20,37,39,44
51,54,57,58,68 2005Q1 12,15,28,35,40
Value
Growth
Properties
Properties
49,51,54,57,68 2005Q2 10,12,15,20,37
49,56,57,61,68 2005Q3 1,12,25,27,37
49,56,57,61,68
55,56,61,69,70,71,72
55,56,61,69,70,71,72
55,56,61,69,70,71,72
55,56,61,69,70,71,72
55,56,61,69,70,71,72
55,56,61,69,70,71,72
55,61,68,69,70,71,72
55,61,68,69,70,71,72
55,61,68,69,70,71,72
2,5,17,49,53
22,38,53,54
Value
Properties
3,52,53,54
3,5,53,32
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Appendix B-1: Countries in the Industrial Portfolio.
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Country
Altanta
Austin
Baltimore
Boston
Cambridge
Camden
Charlotte
Chicago
Cincinnati
Columbus
Dallas
Denver
Edison
Fort Lauderdale
Fort Worth
Houston
Indianapolis
Kansas City
Lake County
Los Angeles
Louisville
Memphis
Miami
Minneapolis
New York
Oakland
Code
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Country
Oklahoma City
Orlando
Oxnard
Philadelphia
Phoenix
Portland
Reno
Riverside
Sacramento
St. Louis
Salt Lake City
San Diego
San Francisco
San Jose
Santa Ana
Seattle
Tacoma
Tampa
Warren
Washington
Wilmington
Worcester
sydney
Melbourne
Brisbane
Auckland(nz)
Value Versus Growth Real Estate Investment Strategy
Appendix A-2b: Composition of Value and Growth Industrial Portfolio.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Year
Country Code
Growth
Properties
Value
Properties
1985Q1
16,31
3
1985Q2
16,40
1985Q3
3,36
1985Q4
Year
Country Code
Growth
Properties
Value
Properties
1995Q3
12,15,44
22,31,51,52
15,49
1995Q4
13,14,34
10,22,50,52
22,49
1996Q1
3,44,51
2,42,50,52
3,31
22,49
1996Q2
2,12,46
32,40,44,52
1986Q1
16,31
46,49
1996Q3
3,14,23
6,25,44,52
1986Q2
3,31
15,49
1996Q4
14,17,23
32,44,50,52
1986Q3
16,36
3,49
1997Q1
15,34,35
17,32,46,52
1986Q4
16,28,31
22,49
1997Q2
14,17,35
16,24,44,52
1987Q1
11,16,28
22,49
1997Q3
14,34,35
2,17,49,52
1987Q2
16,31,38
15,22,49
1997Q4
17,26,34
24,37,50,52
1987Q3
16,26,40
10,22,49
1998Q1
3,14,34
16,25,50,52
1987Q4
11,16,40
22,49
1998Q2
17,26,42
12,25,50,52
1988Q1
6,11,16
3,49
1998Q3
3,13,26
14,16,28,52
1988Q2
6,16,28
15,49
1998Q4
3,26,40,44
8,9,27,52
1988Q3
6,16,28
22,49
1999Q1
10.17,26,37
18,25,27,52
1988Q4
14,16,28
3,49
1999Q2
12,14,26,37
27,33,51,52
1989Q1
6,16,20
1,49
1999Q3
10,26,40,44
9,25,30,52
1989Q2
6,16,34
22,49
1999Q4
14,26,34,44
5,30,35,52
1989Q3
15,28,31
9,49
2000Q1
1,17,26,35
5,9,13,52
1989Q4
14,15,28
8,49
2000Q2
14,17,29,48
6,15,24,52
1990Q1
9,20,28
44,49
2000Q3
13,17,31,48
9,40,44,52
1990Q2
6,20,31
16,49
2000Q4
13,14,29,31
18,27,35,52
1990Q3
10,15,28
22,49
2001Q1
13,17,25,33
7,15,27,51,52
1990Q4
1,6,10
17,49
2001Q2
5,13,25,39,42
2,15,30,33,51
1991Q1
10,20,46
15,49
2001Q3
13,25,27,33,44
2,3,16,29,30
1991Q2
10,20,44
4,22,49
2001Q4
1,13,14,39,44
6,29,33,51,52
1991Q3
9,20,46
36,49
2002Q1
15,16,25,33,39
2,30,36,44,52
1991Q4
4,17,44
22,49
2002Q2
8,23,25,33,39
30,36,45,48,50
1992Q1
4,34,35
20,44,49
2002Q3
6,13,25,33,39
24,29,36,45,48
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
71
72
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
Appendix A-2b. (Continued)
Country
Code
Year
Growth
Properties
Value
Properties
1992Q2
4,22,35
13,14,49
1992Q3
4,35,41
13,16,49
1992Q4
4,28,35
1993Q1
13,35
1993Q2
17,34,35
22,49,52
1993Q3
4,35,41
22,49,52
1993Q4
22,28,35
44,49,52
1994Q1
4,6,35
1994Q2
1994Q3
Year
Country
Code
Growth
Properties
Value
Properties
2002Q4
13,14,25,43,44
2,12,18,29,52
2003Q1
12,14,16,27,33
2,37,45,50,51
22,49
2003Q2
10,12,16,33,38
30,43,45,50,52
22,28,49
2003Q3
23,33,36,38,43
7,10,26,30,45
2003Q4
6,27,33,35,44
19,23,30,45,52
2004Q1
6,23,35,36,44
10,26,30,45,52
2004Q2
5,14,23,41,43
10,30,33,45,52
44,49,52
2004Q3
6,14,23,36,47
10,30,45,51,52
3,4,35
22,49,51,52
2004Q4
4,14,23,28,50
2,30,33,51,52
8,14,35
22,49,51,52
2005Q1
4,5,6,23,47
44,45,46,52
1994Q4
14,28,44
22,49,51,52
2005Q2
5,15,30,32,35
10,18,,27,44
1995Q1
14,28,46
22,49,51,52
2005Q3
5,25,30,34,40
21,22,37,52
1995Q2
6,14,46
49,50,51,52
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Appendix A-3a: Countries in the Retail Portfolio
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Country
Altanta
Austin
Baltimore
Bethesda
Chicago
Columbus
Dallas
Denver
Fort Lauderdale
Fort Worth
Houston
Jacksonville
Los Angeles
Miami
Minneapolis
New York
Oakland
Orlando
Philadelphia
Phoenix
Portland
Raleigh
Sacramento
San Antonio
Code
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Country
San Diego
San Francisco
San Jose
Santa Ana
Seattle
Washington
West Palm Beach
New South Wales Retail
Victorian Retail
Queensland Retail
Western Australian Retail
new zealand retail
Beijing
Bangkok
Hong kong(prime)
hong kong (suburban)
Jarkarta
KL(CC)
KL(suburban)
Makati
Singapore (prime)
Singapore (Suburban)
Singapore (Marina)
Shanghai
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Value Versus Growth Real Estate Investment Strategy
73
Appendix A-3b: Composition of Value and Growth Retail Portfolio.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Year
1992Q1
1992Q2
1992Q3
1992Q4
1993Q1
1993Q2
1993Q3
1993Q4
1994Q1
1994Q2
1994Q3
1994Q4
1995Q1
1995Q2
1995Q3
1995Q4
1996Q1
1996Q2
1996Q3
1996Q4
1997Q1
1997Q2
1997Q3
1997Q4
1998Q1
1998Q2
1998Q3
1998Q4
Country Code
Growth
Properties
18,20,30
20,28,30
7,28,30
18,28,30
18,20,30
18,19,30
17,18,30
7,18,30
7,20,30
7,18,30
18,29,30
2,9,30
9,18,30
18,23,30
19,25,30
13,16,25,30
10,15,28,30
1,15,30,33
13,15,25,30
15,29,30,33
15,20,25
30,32,33
9,20,25,27
20,25,34
20,25,32
19,25,33,34
15,19,20,26
14,19,25
Year
Value
Properties
35,45
35,45
35,45
34,45
35,45
34,35,45
35,45
32,34,45
34,41,45
36,41,45
36,41,45
36,41,45
36,41,45
36,41,45
36,41,45
36,41,45
38,41,48
38,41,48
38,41,48
38,41,48
38,41,45,48
38,41,45,48
38,41,45,48
38,41,48
38,41,48
38,41,48
38,41,48
38,41,48
1999Q1
1999Q2
1999Q3
1999Q4
2000Q1
2000Q2
2000Q3
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
2002Q3
2002Q4
2003Q1
2003Q2
2003Q3
2003Q4
2004Q1
2004Q2
2004Q3
2004Q4
2005Q1
2005Q2
2005Q3
Country Code
Growth
Properties
9,17,19,26
14,18,19,33
10,16,18,19
8,10,14,25
8,10,14,18,19
3,8,18,27,32
8,18,19,27,35
14,24,27,29,35
5,14,27,33,34
14,26,27,33,34
19,27,31,34,35
5,26,31,34,35
5,10,31,34,35
18,31,32,34,35
14,32,33,34,35
14,31,33,34,35
11,14,16,31,32
14,16,25,26,31
13,14,31,32,33
7,14,19,26,33
8,11,14,15,27
14,16,22,26,27
4,14,17,26,27
12,14,17,22
6,9,16,22
14,18,24,27
14,17,18,27
Value
Properties
37,38,41,48
37,38,41,48
37,38,41,48
37,38,41,48
37,38,41,48
37,38,41,48
37,38,41,48
37,38,41,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,43,48
37,38,41,48
37,38,41,43,48
37,38,41,48
37,38,41,48
37,38,41,48
6,9,36
19,21,36
6,19,36
7,21,36
Appendix B: States of the World.
Office and Industrial Portfolios
Year
State
Year
1985Q1 W
1990Q2
1985Q2 W
1990Q3
1985Q3 W
1990Q4
1985Q4 W
1991Q1
1986Q1 W
1991Q2
1986Q2 W
1991Q3
1986Q3 W
1991Q4
1986Q4 W
1992Q1
1987Q1 W
1992Q2
1987Q2 NW
1992Q3
State
W
NW
W
W
W
W
NW
NW
W
W
Year
1995Q3
1995Q4
1996Q1
1996Q2
1996Q3
1996Q4
1997Q1
1997Q2
1997Q3
1997Q4
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
State
NB
B
B
B
B
B
B
B
B
B
Year
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
2002Q3
2002Q4
2003Q1
State
NB
NB
NB
NB
NW
NB
NB
NB
NW
NW
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
74
Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan
1987Q3
1987Q4
1988Q1
1988Q2
1988Q3
1988Q4
1989Q1
1989Q2
1989Q3
1989Q4
NW
NW
W
NW
Worst
NW
NW
NW
NW
NW
Appendix B (Continued)
1992Q4 W
1998Q1 NB
1993Q1 W
1998Q2 NB
1993Q2 NW
1998Q3 NW
1993Q3 NW
1998Q4 W
1993Q4 NB
1999Q1 NB
1994Q1 B
1999Q2 NB
1994Q2 NB
1999Q3 B
1994Q3 NB
1999Q4 NB
1994Q4 B
2000Q1 B
1995Q1 NB
2000Q2 NB
1990Q1
NW
1995Q2
Retail Portfolio
Year
State
1992Q1 W
1992Q2 W
1992Q3 W
1992Q4 W
1993Q1 W
1993Q2 W
1993Q3 W
1993Q4 NW
1994Q1 NB
1994Q2 NB
1994Q3 NB
1994Q4 NB
1995Q1 NW
1995Q2 NW
Year
1995Q3
1995Q4
1996Q1
1996Q2
1996Q3
1996Q4
1997Q1
1997Q2
1997Q3
1997Q4
1998Q1
1998Q2
1998Q3
1998Q4
NB
2000Q3
State
NB
NB
NB
B
B
B
B
B
B
B
NW
NW
Worst
Worst
Year
1999Q1
1999Q2
1999Q3
1999Q4
2000Q1
2000Q2
2000Q3
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
W=Worst , NW=Next Worst , NB= Next Best , B=Best
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
2003Q2
2003Q3
2003Q4
2004Q1
2004Q2
2004Q3
2004Q4
2005Q1
2005Q2
2005Q3
NW
NW
NB
B
B
B
B
B
B
B
Year
2002Q3
2002Q4
2003Q1
2003Q2
2003Q3
2003Q4
2004Q1
2004Q2
2004Q3
2004Q4
2005Q1
2005Q2
2005Q3
State
NW
W
W
W
W
NB
NB
B
B
B
B
B
Best
NB
State
NW
NW
NB
NW
NB
NB
NW
NB
NB
NW
NW
W
NW
NW
In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 75-103
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 3
RESTRUCTURING REAL ESTATE MARKET
INFORMATION MANAGEMENT TO FACILITATE LANDBASED INVESTMENT
ACTIVITIES IN GHANA
Raymond T. Abdulai* and Felix N. Hammond
ABSTRACT
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Real estate is so important a subject that it cannot be left out any serious
macroeconomic deliberation and the collective quest for investment, wealth creation,
poverty alleviation and economic development. This is amply demonstrated by the
negative effects that the current real estate market downturn is having on every facet of
the economies of rich nations. The role played by, especially, private real estate in the
economic development of the advanced world is well documented. The importance of
well established real estate markets that operate efficiently cannot, therefore, be overemphasised. One area that has and continues to dominate discussions relates to how real
estate market information should be organized and managed to guide participants in the
markets to make efficient purchase, sale and investment decisions. It is often
the responsibility of the state to organize and manage real estate market information
through implementation of land registration programmes. In Ghana, despite 126 years of
unbroken history of implementing land registration programmes, it is estimated that only
8% of real estate ownership has been registered. It is important to properly comprehend
this problem and its fundamental causes in order to proffer the appropriate remedies.
Using the quantitative research methodology, this study seeks to offer explanations of the
large lag in land registration in Ghana. It has been established that the fundamental root
cause of the problem is the fact that the operation of Ghanaian state agencies that are
responsible for the organization, management and dissemination of real estate market
information is not based on clear economic principles. As a starting point, it is
recommended that a nationwide timed-bound real estate ownership census akin to the
survey conducted in Britain that resulted in the Domesday Book of 1086 be carried out
*
Corresponding author: Email: R. Abdulai@ljmu.ac.uk Tel: +44 (0)151 321 2573.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
76
Raymond T. Abdulai and Felix N. Hammond
and it should be financed by the government. From then onwards, it should be in the
interest of the state to ensure that every real estate ownership or transaction is recorded
by instituting an incentive package that would attract people to register; after all, such
information would be sold to the public at a price. In this way a viable real estate
ownership information system would be created, which would enable the real estate
market to operate efficiently.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
INTRODUCTION
That real estate is so important a subject to be left out of consideration in any serious
macroeconomic deliberation and in the collective quest for investment, wealth creation,
poverty alleviation and economic development is amply attested to by the current downturn in
real estate markets across the developed world (Abdulai and Hammond, 2008). Economic
historians like North and Thomas (1973), Rosenberg and Birdzell (1986), Torstensson (1994)
and Goldsmith (1995) have documented the role played by, particularly, private real estate in
the economic development of advanced nations. Indeed, shelter and for that matter real estate,
has been and continues to be one of the major global economic drivers. Housing, for instance,
has played a major role in shaping the business cycles of countries like USA, Britain, New
Zealand and Canada (Hale, 2008). According Hale (2008), from 2000-2005, for instance,
house prices rose by 78% in Australia, 65% in New Zealand, 50% in Canada, 102% in Britain
and 50% in the USA and this housing boom within the period produced negative savings rates
and higher consumer spending in such countries. Hale (2008) further observes that the current
weakness in house prices caused by the credit crunch has produced a downturn in consumer
spending especially in Britain and the USA. In Britain alone, currently, there are 11.8 million
residential mortgages, with loans worth over £1.2 trillion (CML, 2008) and the commercial
real estate markets alone contribute some 6% to its annual Gross Domestic Product (GDP)
(BPF, 2006). The figure is bound to be considerably more if the contributions of residential
and other segments of the real estate market are taken into account in Britain.
The above shows that there is a connection between real estate and economic
development. It, therefore, implies that the performance of any economy is, to a large extent,
contingent upon the performance of its underlying real estate markets. This point is selfevident across the rich countries of the world but as aptly noted by Hammond (2006), it is
less so in developing nations, particularly in sub-Saharan Africa. It follows that, if correctly
organised, real estate markets in developing countries, particularly, in sub-Saharan Africa,
could spur greater economic growth and possibly facilitate the lifting of the four-fifth of the
regions population that currently survive on less than US$2.50 a day (Chen and Ravallion,
2008).
In order to attain these ends, real estate markets must facilitate large volumes of year-onyear first time and repeat voluntary real estate trades. It must also interact constructively with
the financial system to facilitate large volumes of year-on-year real estate based credit
transactions and indirect investments in real estate. By fostering large volumes of first time
and repeat transactions in real estate, especially, between well informed voluntary buyers,
sellers, borrowers and creditors, real estate markets are able to continually push real estate
resources from lower productive uses to higher productive uses. By so doing, they ensure that
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Restructuring Real Estate Market Information Management to Facilitate …
77
real estate in a given society continually improves upon its contributions to the economic
development processes.
The trouble is real estate markets are typically beset with fundamental restraints, which
prevent or slow down the conduct of large volume of first time and repeat real estate trades.
These restraints generally consist of ill-defined property rights (Alchian, 1965; Demsetz,
1967; Pejovich, 1990; Feder and Feeney, 1991), high transaction costs (Coase, 1960;
Williamson, 1981; Rao, 2003), monopolisation of segments of the real estate markets,
uncontrolled negative side effects of libertarian competition and the possible non-provision of
public goods (Smith, 1776; Hicks, 1939; Harberger, 1959; Friedman, 2002). Whilst all of
these restraints are important and need urgent attention, costly access to full and perfect
information is lately being recognised as the most enduring restraint (Stigler, 1961;
Williamson, 1991; Wyatt & Fisher, 1998; Rose, 2002), especially, in less developed
economies. To appreciate the reasons why costly and imperfect information is critical, it is
important to commence with the nature and purpose of the real estate markets.
Most real estate trades involve the reciprocal interchange of property rights on one hand
and money on the other. In effect, through trade, the parties interchange positions – the
original owner of the real estate takes the place of the original owner of the price money and
vice versa. Thereafter, the new owner of the real estate gains the backing of the law to make
whatever uses and profits from the real estate, subject of course to the provisions of land use
planning and other relevant laws of the society. The new owner may use the real estate as
collateral to raise capital for investments to earn better returns. At the same time, the law
disables all others from making any use or decisions regarding the real estate unless where the
law so permits. The economic view is that buyers pay a price to interchange positions with
original owners because, in their estimation, the original owners have undervalued or
underpriced the economic potentials of the real estate in so far as its future rents/benefits are
concerned (Smith, 1776). Likewise, the owners of real estate may also feel that purchasers
have undervalued the real worth or benefits of the money they intend to use in purchasing it
and that they (the owners of the real estate) can generate more returns from the money than
anticipated by the purchasers.
Consequently, by interchanging positions, buyers are able to make such uses of the real
estate involved or rearrange or modify it in a manner that will enable it to generate the
anticipated full benefits. Buyers who are proven right by the coming market events reap better
benefits/returns from the real estate than what the original owners would have reaped. By
buying, improving and putting real estate to those uses that generate higher benefits/returns,
purchasers, through the interchange, push the real estate from lower productive uses to higher
productive uses. If real estate that was the subject matter of a previous transaction is
subsequently sold again, all things being equal, it would be pushed further into a higher
productive use and so on. By so doing, real estate continually increases its contributions to the
economic development processes.
Real estate markets function well when buyers, creditors, investors, sellers, borrowers
and issuers make informed decisions and choices relating to sales, purchases and pricing. The
ability of actors to make informed decisions depends on easy and cheap access to high quality
and clear information on which they make their decisions. To aid this, there is the need for
real estate market participants to possess upfront, clear and quality information on all
available real estate stock on the market at any given time. They must also know the true
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
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78
Raymond T. Abdulai and Felix N. Hammond
owners of each available stock. Finally, they must have information relating to recent
transactions involving comparable real estate.
Without such upfront information, transacting parties will suffer from information deficit
with severe consequences. Firstly, purchasers or investors suffering from information deficit
may end up paying more for real estate than the total income that it is capable of generating.
Likewise, creditors may end up advancing more credit than the economic worth of the real
estate in question. Secondly, since the essence of the transaction is for the parties to
interchange positions, purchasers or creditors may end up paying the price money or
advancing credit to phony owners. This is very crucial, because, without buying the property
rights from the actual owner, the law will disable the buyer, creditor or investor from taking
the place of the true owner. Thus, if the purported seller is not actually the owner, the buyer,
by paying the seller, misallocates his or her financial resources. As always, the market will
punish such misallocations with economic loses. Such buyers, creditors and investors will, as
a result, lose their money and gain nothing in return. It is a rational expectation that buyers,
creditors and investors would seek keenly to avoid or minimise such punishments. They will,
thus, strive to feel sure that the purported sellers they intend to deal with are, indeed, the
undisputed owners, before embarking on the transactions.
However, without a workable system to provide the key information at the right quality,
with the right contents, in the right form and at reasonable cost, there is a real risk that real
estate market participants may make their most serious decisions based on suspicious data
and many would end up as recipients of the punishments of the market. The cost of
information deficit can be huge, though less readily obvious. These costs include loss of
money through payments to phony parties, ownership litigation expenses, loss of income
earning opportunities and so on. The prospects of such cost would lower the confidence of
market participants, thereby slowing down the rate of transactions, which will in turn slow
down the performance of the market and hence deny the development process of the
invaluable contributions that the real estate market could have made to it.
Fortunately, real estate is an example of search goods (Hammond, 2006; Abdulai, 2007)
and hence distinct from experience goods such as a meal. Unlike experience goods whose
potential benefits can only be ascertained after consumption, it is possible to search and
discover sufficient information about real estate far before they are purchased or admitted as
collateral in credit transactions (Weiner and Vining, 1999; Hammond, 2006). It is possible,
for instance, to discover through inspections the relevant information regarding its essential
physical (for example size, location and neighbourhood quality), legal (property rights and
restrictions) and economic (price and rental values) attributes that determine its utilities
before arriving at a final purchase or price decisions. The implications are three-fold.
Firstly, in an economic system in which the requisite information is readily available at
zero search cost, owners (borrowers) or sellers (creditors) would base their purchase, sale or
price decisions on concrete and reliable information or market data. This will expectedly lead
to optimal decisions on price and quantities that “will satisfy preference to the greatest extent
possible” (Nell, 1984). Secondly, where, on the other hand, because of its technical nature,
the data is not intelligible to the market participants above, they may seek advice from those
equipped with the skills and expertise in analysing such data. These specialists may include
valuers, lawyers and estate agents. Thirdly, where the required information is non-existent,
severely disorganised, out of date or considerably costly to obtain, market participants as well
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as market intermediaries such as valuers, estate agents and lawyers may end up basing
purchase, sale or pricing decisions on suspicious or severely inadequate information.
By its very nature, however, most of the information that buyers could base their
decisions on are held by the owners of the real estate. Meanwhile because real estate
transactions are not contracts of good faith (Uberrimae Fidei), owners are not under legal
obligations to disclose any information known to them, which could help buyers form their
views on the terms of trade. Besides, the prospects of opportunistic behaviours suggest that
buyers cannot fully trust the information volunteered by owners as they could be inaccurate or
intended to mislead them. The alternative will be for buyers to conduct their own private real
estate research and interviews with neighbours to procure the relevant information needed for
their decisions. This, however, is time consuming and a costly process. As Stigler (1961),
Vickrey (1961) and Mirrlees (1971) argue, because of the cost involved in information
gathering, actors are inevitably less than fully informed when they make their most optimal
market decisions.
Traditionally, buyers rely on publicly accessible information systems such as land
registration and cadastral systems to develop their views as to the validity of the claims of
ownership made by the parties purporting to sell or use the real estate as security in credit
transactions. In the case of Ghana, however, the World Bank (2007) estimates that these
publicly available systems hold just about 8% of information on real estate transactions. Thus,
there are about nine in ten chances that information on real estate that a buyer is interested in
would not be held in these systems. This makes buyers to incur high costs to investigate the
true ownership position privately through market inquiries and interviews with neighbours.
This heightens the cost of real estate transactions in Ghana, which may be slowing down the
development of its real estate market. It is the job of government to alleviate these costs to get
the market operating at efficient levels. To do this, there is the need, first, to understand the
causes of the considerable backlog of real estate ownership information capture and through
the causation proffer workable solutions. It is this aim the chapter seeks to pursue.
The rest of the chapter is organised as follows. The section that follows this introduction
describes the land registration systems that exist globally after which section three gives an
overview of the Ghanaian land registration systems. The research methodology adopted for
the study is described in section four whilst section five presents and discusses the date
collected from a field survey. The penultimate section deals with implications and the last
section concludes the chapter.
TYPES OF LAND REGISTRATION SYSTEMS
Globally there are two types of land registration systems, which are deed registration and
title registration. The fundamental principles that underpin the two systems are explicated as
follows. In a deed registration system, legally recognized and protected real estate ownership
arises upon conclusion of an agreement or contract between the real estate grantor and grantee
(Deininger, 2003). According to Deininger (2003), the entry of the agreement and its key
contents into the public registry is to provide notice to the world of the existence of the real
estate ownership and challenges to it will be handled through civil litigation. However, in a
title registration system, it is the entry into the registry that gives the ownership legal validity,
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Raymond T. Abdulai and Felix N. Hammond
guaranteed by the State; all entries in the register are prima facie evidence of the legal status
of the real estate (Deininger, 2003).
Even though in the title registration system, legal validity of real estate ownership arises
from the fact of registration, it is important to note that it does not emanate from only
registration. In the deed registration system, for example, legal validity arises from the
contract or agreement between the grantor and grantee and not from registration.
Furthermore, legal validity and recognition of real estate ownership can emanate from other
legislation apart from the title registration system depending on the legal system that operates
in a particular country. In Ghana, for example, the customary or traditional system of real
estate ownership is not based on registration but it is recognized by the legal system. Under
Ghana’s customary law, proof of real estate ownership is not by any form of documentation;
it is rather by physical occupation and possession and the recognition of this fact by members
of the society, particularly, adjoining owners of real estate (Antwi, 2000; Abdulai, 2006 and
2007). However, under the 1992 Constitution of Ghana (the supreme law of the country) and
the Conveyancing Decree of 1973 (NRCD 175), the customary system of real estate
ownership is recognized by the common law courts. Indeed, legal recognition of Ghana’s
customary system of real estate ownership dates back to 1925 when the British colonial
administrators introduced the Native Courts (Crook, 2002).
In Nigeria, despite the nationalization of land under the Land Use Decree of 1978,
customary real estate transactions (which are not based on any form of documentation) are
recognized by the state sponsored court system in so far as they are consistent with existing
State law and are also not opposed to public policy (Ikejiofor, 2006). Other countries in the
developing world where the customary system of real estate ownership is legally recognised
(whether it is registered or not) include Mozambique, Lesotho, Malawi, Mali, Namibia,
Niger, South Africa, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe (Alden-Wiley,
2002; cited in Deininger, 2003). Unregistered real estate ownership is legally recognised in
the United Kingdom; however, in unregistered real estate sales, the law requires the root of
title to be investigated 15 years back (which used to be 30 years per-dating 1970) before the
trading takes place (Abdulai, 2006; MacKenzie and Phillips, 2008).
In comparative terms, the gap between the percentage of real estate ownership that is
registered in the developing and developed world is very huge. Available data from de Soto
(2000), Deininger (2003), World Bank (2007), Abdulai (2007) and HM Land Registry (2009)
bespeak that in the developing world, the percentage of registered real estate ownership is 15
whilst in the advanced world, it ranges from 65% to 100%. In Africa, it is 2% - 10% while in
East Asia it is less than 30%.
Having distinguished between the types of land registration systems that exist globally, it
is expedient at this stage to explain how real estate and its registration affect the economic
development of a nation. It is real estate that is normally accepted by financial institutions as
collateral for advancing loans for investment. Real estate is regarded as a suitable collateral
asset because it is a commodity that is consumed in situ; that is, it is an immovable asset. The
importance of mortgaging one’s real estate asset to financial institutions cannot be overemphasised from the perspective of both the mortgagor (borrower) and the mortgagee
(lender). This is because collateral arrangements partly or fully shift the risks of loan loss
from the mortgagee to the mortgagor. In the event of default on the part of the mortgagor, it
will trigger the loss of his or her mortgaged real estate. Thus, for the mortgagee, the collateral
provides a form of protection or insurance against the loss of the loan since there are normally
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enough legal arrangements to foreclose the mortgage transaction where the mortgagor
defaults. At the same time, the prospect of losing one’s real estate is an incentive for the
mortgagor to be committed regarding the repayment of the loan granted.
The pledging of real estate, accompanied by the registration of ownership and mortgage
transactions tremendously overcomes the problems of asymmetrical information and moral
hazard. Land registration is a record keeping system and, therefore, creates a real estate
ownership database, which is very important in every economy. When information is
recorded in a central system, which is accessible to the public, it makes it easy for such data
to be accessed for various purposes. The availability of a real estate ownership database
facilitates real estate transactions and real estate taxation, which reduces transactions costs.
De Soto (2000) alludes to this purpose when he emphasises the role of land registration in
facilitating communication, information sharing, networking and transactions. In the absence
of land registration, legal experts are often commissioned to conduct searches to trace the root
of title in real estate transactions and verify that the real estate is not subject to any
undisclosed obligations, which is normally a long process, time consuming and expensive.
This point is amply demonstrated by what obtains in the UK and France. As earlier, indicated,
in the UK where real estate is not registered and it is to be traded, the law requires that the
root of title be investigated 15 years back. In the case of France, it is 30 years. Thus, land
registration significantly enhances the smooth operation of real estate markets.
Regarding the registration of mortgages, the common law position is that if a mortgage
transaction is not registered and the mortgagor clandestinely transfers the mortgaged real state
via sale to a bona fide purchaser for value who is unaware of the existence of the mortgage
transaction, the mortgage as an encumbrance will not be binding on such a purchaser
(MacKenzie and Phillips, 2008). However, if the mortgage transaction is registered, the
purchaser cannot defend himself or herself on the basis that he or she is a bona fide purchaser
for value without notice since the existence of the mortgage will be a public record and based
on the principle of caveat emptor, it will be his or her responsibility to check the public
records. Thus, the registration of mortgage transactions protects lenders/financial institutions
from the activities of unscrupulous mortgagors as it makes it impossible for any purchaser of
mortgaged real estate to plead bona fide purchaser for value without notice. From the
preceding discussion, it is obvious that real estate and land and mortgage registration play a
critical role in the economic development of a nation; they greatly help to improve access to
formal capital for investment and wealth creation.
REAL ESTATE OWNERSHIP
INFORMATION SYSTEMS IN GHANA
Albeit traditionally, deeds and land title registration systems are the two main systems
that have been employed by many nations to overcome real estate ownership information
asymmetry, in Ghana, various parallel models of real estate ownership registration systems
manned by six separate and independent public real estate sector institutions have emerged
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Raymond T. Abdulai and Felix N. Hammond
for the task. These are Lands Commission, Deed Registry, Land Title Registry, Survey
Department, Land Valuation, Board, and Office of the Administrator of Stool Land1 (OASL).
The earliest official real estate ownership registration system in the country is the deed
registration system (manned by the Deed Registry which, is practically under Lands
Commission), a legacy of the colonial order established largely to supposedly protect the
interests of expatriate merchants of the colonial era (Meek, 1949). This commenced with the
enactment of the Land Registry Ordinance (No. 8) in 1883, which was re-enacted in 1895
(cap 133) and revised in 1951. The first postcolonial administration, in 1962, replaced these
laws with the Land Registry Act (Act 122), which made registration of instruments
accompanied by a survey plan of the land, compulsory (Section 24 Act 122).
The second official though not juridical real estate ownership registration system is the
Plotting System, which is manned by Lands Commission. This came into being when in 1945
the Stamp Ordinance (Cap 168) was amended (No. 29 of 1945) to introduce an internal
administrative requirement to attach a “Particulars Delivered Form” (PDF) to all real estate
contract documents submitted to the erstwhile Lands Department (now Lands Commission)
for the assessment and payment of stamp duty on the purchase or leasing of real estate. The
PDF made provision for a short description of land and details of the transaction together with
an annexed plan of the land and any building thereon. The extracts from these data together
with the site plan was then plotted onto survey maps and referenced. This became an
administrative parcel-based database in which data is organised around the proprietary land
unit for valuation and stamp duty assessment purposes. Considering its purpose as an
administrative database, which was not meant for public consumption, the scientific accuracy
of the survey plans attached was not insisted on. Also as Brobbey (1991), a former chairman
of Land Valuation Board (the original custodian of the system), states, consideration and
prices stipulated in the transaction documents, which formed the basis and main inputs for
this database were severely understated to evade higher stamp duty or tax liabilities.
Additionally, according to Antwi (2000), Hammond (2006) and Abdulai (2007), the
plotting system admits irreconcilable data in respect of the same proprietary land unit, as that
did not adversely affect the objective of the database. Yet, owing to the difficulties in
obtaining search reports from the deed registration system as against the relative ease with
which information could be obtained from the plotting system (because it was parcel-based),
many real estate market participants relied more on unofficial searches from the plotting
system as the basis for their real estate market decisions; at present about 90% of all real
estate information searches are conducted from the plotting system instead of the juridical
deed and land title registration systems even though the plotting registers still lack legal
recognition (Hammond, 2006). Information obtained from this plotting system by the public
confers no rights or obligations against the government regarding its accuracy. Records held
in the plotting system are most of the time so inaccurate that invariably an inspection has to
be conducted to confirm doubts in a majority of the cases submitted for registration; in some
1
In Ghana, there are primarily two types of landownership, which are State and private landownership. Private land
composes of traditional land (land vested in communities represented by chiefs/kings and families/clans) and
individual land whilst State land refers to land that has been acquired by the State from the private land sector. In
the southern part of Ghana, chiefs/kings as traditional rulers sit in state on specially designed stools or chairs. In
the olden days, they sat in state on stools, which were regarded as the symbols of authority. However, as time
progressed and with modernization, they started using specially designed chairs. Thus today, most of them use
chairs but the stool remains the symbol of authority. In southern Ghana where land is vested in communities
represented by chiefs/kings, it is call stool land whilst in northern Ghana, it is referred to as skin land.
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cases site visits have established that the real estate intended to be registered does not even
exist on the geographical maps at all (Hammond, 2006).
The third model is the concurrence system, which is also handled by the Lands
Commission. Practically the distinction between this system and the plotting system is only
one of convenience. In 1962, the enactment of the Administration of Lands Act (Act 123)
produced the curious outcome that non-commercial stool land transactions were not to be
formalised and hence need not be captured within the prevailing official information
machinery. Transacting parties could, thus, dispense with the formalities of seeking the public
recording of those transactions that are made “without payment of valuable consideration or
in exchange for nominal consideration” (Act 123). Such transactions need not be evidenced in
writing and need not be registered under the deed registry model as well as the plotting
system. Under the same law, however, commercial stool land transactions that “involves the
payment of any valuable consideration or which would, by reason of it being to a person not
entitled by customary law to the free use of land involved the payment of any such
consideration” (Section 8 of Act, 123) were to be compulsorily formalised. Since the deeds,
plotting and concurrence systems are practically unified, the chapter will group them together
in the ensuing analysis and discussion.
The fourth real estate ownership information system is the land title registration system,
which was introduced in 1986 under the Land Title Registration Law of 1986 (PNDCL, 152).
The law is currently operational in Greater Accra region and Kumasi in the Ashanti region
only2. The agency responsible for title registration is the Land Title Registry. The system was
introduced to replace the deeds registration model and by implication the plotting model as
well. The implementation of the law had false starts with serious implications for the
asymmetry problems. Firstly, an entirely new government department (the Land Title
Registry) was established to carry out the implementation of the law. This engendered fierce
clash of departmental interests as managers of the existing system felt their powers and
influence were threatened by the new agency. The import of section 13 of the law was to
ensure that within 90 days of declaring an area a registration district, all previously recorded
transactions under the deeds system considered genuine and accurate were to be transferred
directly onto the new land title register under PNDCL 152. Thereafter, deeds and plotting will
cease to operate in the declared areas. However, to date (after almost 23 years) not a single
document has been transferred this way. The practical consequences are that the land title
system operates in parallel with the deeds and plotting systems. As aptly noted by Antwi
(2000) and Hammond (2006), the harsh reality is that the information in these respective
systems is not harmonised in any comprehensive way; indeed, often contradictory.
From the preceding discourse, it is obvious that government interventions in the area of
real estate market information management in Ghana have had 126 years of unbroken history.
Put roughly, for the registration systems to have been able to capture all existing real estate
ownership or transactions information by now, assuming all information captured is accurate,
the systems should have been capturing information at a rate of at least 0.8 percent (less then
1 %) annually; this figure is calculated by simply dividing 100% coverage by the 126 years of
land registration practice in Ghana. Yet according to AMCAD (1998), only about 30% of
2
Ghana is divided into ten regions for political and administrative purposes and each region has a capital, which is
the administrative centre. Greater Accra and Ashanti regions are some of the regions in Ghana. The capital of
Greater Accra region is Accra, which is also the capital of Ghana and that of Ashanti region is Kumasi.
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Raymond T. Abdulai and Felix N. Hammond
existing information on urban real estate has been captured as at 1998. This represents an
information capture rate of about 0.26% per annum in urban areas of Ghana, which is about a
third as fast as it should be. The World Bank finding referred to earlier that only 8% of
information on real estate transactions across the country are recorded in the registration
systems also indicates that a much slower national rate of capture than the 0.26% per annum.
This implies that, even if all the information captured by these parallel registration systems is
accurate and consistent (and they are not) only about 8% of the asymmetric information
problems together with their economic repercussions would have been dealt with by these
systems. The challenge to this chapter is to provide a convincing and empirically validated
explanation to the large lag in real estate market information capture in Ghana and to offer
practical solutions.
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RESEARCH METHODOLOGY
The quantitative, qualitative and mixed methodologies research approaches were
examined as to their appropriateness for the study and the quantitative research methodology
was finally adopted. Empirical data was collected in Ghana between June 2007 and January
2008 using field survey data collection procedures. The survey participants fell into two broad
groups. The first group consisted of the government agencies in charge of the administration
of the State real estate ownership information systems. The second group consisted of
purchasers of real estate who had sought or were seeking to register their real estate
ownership with the relevant systems.
With regard to the first group of survey participants, Lands Commission, Deeds and Land
Title Registries represent the end points of all the activities directed at registration of real
estate ownership in the country and were, therefore, the agencies surveyed. As earlier noted,
the deeds, plotting and concurrence systems are practically unified and are, thus, grouped
together under Lands Commission. Due to time and cost considerations, the study
concentrated on Accra, which is the capital of Ghana; Accra also has the most active real
estate market in the country. Data were obtained from these agencies through systematic
records investigation and participant observation. Regarding participant observation, firstly,
the chain link of activities involved in registering real estate ownership were meticulously
observed individually, timed and costed. Each activity was observed and timed on three
separate occasions without the prior notice of the observee. The average time for the three
observations was then adopted as the typical time span for each activity.
Admittedly, the accuracy could have be improved if the number of observations had been
increased, but prevailing time and cost constraints restricted the number of observations that
could possibly be taken. Even so, this study is aimed at providing indications of the order of
things and not to actually estimate pinpoint accurate figures. The average time per activity
was then valued. The best way of valuing the time spent is to apply the time preference rate;
that is, the going risk free interest rate (government bond rates), applicable at the time.
However, government bond markets in Ghana are well known to be highly underdeveloped
and overly laden with political influences to the extent that on occasions, the risk-free rate
surpasses the risk adjusted rates obtainable from the country’s stock market. It is, therefore,
quite unreasonable to rely on the prevailing bond rates as accurate reflection of the time value
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of money in that country. Even if an accurate risk-free rate were obtainable, it has to be
adjusted to reflect the risk of the activity by adding a risk premium to it. Being a public
service, determining the applicable risk premium to apply is problematic and can be fraught
with errors. As a result, the simplified way around this, was to adopt the average wage rate for
the official performing the activity as the minimum time value of money rate. This is valid
because, it at least, reflects the government’s own (of course not the market) valuation of the
time of the official. The time spent on an activity was, thus, multiplied by the wage rate of the
official to arrive at the minimum estimate of the value of the activity.
Additionally, 303 real estate purchasers were randomly selected and surveyed using
purposely-designed questionnaire administered face-to-face. No questionnaire was posted or
left with respondents for later collection or administration. Generally, the questionnaire
sought for data on how much it cost them in terms of time spent and out of pocket expenses to
have their real estate ownership recorded in the public registers. Also, similar data on how
much it cost to conduct a search from the registration systems was obtained from the
respondents.
PRESENTATION OF DATA AND DISCUSSION
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Delays
To ascertain whether the plotting/concurrence system is competent in capturing the
relevant information early, fully and accurately, data on 235 randomly sampled documents
supplied to the system and processed for concurrence under the concurrence and plotting
systems were obtained. Table 1 shows the descriptive statistics on the times (in days) it took
for the relevant property information to be captured into the concurrence information system.
The study identified a total of 22 bureaucratic steps in the information capture process
and take, according to Table 1, 600 days on the average to complete the recording of
information on an average plot size of 2.27 acres. Most of the documents were processed in
258 days (Mode). The fastest processing time was 34 days while the slowest processing time
was 3,530 days. The crucial point at issue, however, is given that real estate transactions are
occurring regularly in Ghana, the average processing time of 600 days means that the system
could be some 600 days out of date at any particular time. The sluggishness of the system is
clearly contributing significantly to the lag in information capture and even leading to the data
available being possibly outdated.
The data used to analyze the speed of data capture under the land title registration system
is the full data on applications made by real estate owners in the study area to the land title
registry from the date of its inception in practice in 1988 to 2002 (15 years). The data, thus,
represent the total return series for the 15-year period. The 2002 end point has been
deliberately chosen because, in actual fact, the year 2002 marks a new era in land
administration in Ghana. It is in reality the year that the ongoing Ghana Land Administration
Project under the auspices of the World Bank took off in practice. The data and following
analysis, therefore, provides an important baseline data against which the performance of the
Ghana Land Administration project can be ascertained ex-post.
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Raymond T. Abdulai and Felix N. Hammond
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Table 2 provides statistical description of the data. According to the Table, on the average
7,452 applications are made to the Land Title Registry annually. Out of this, about 1,849
constituting 25% come from areas where pre-prepared sectional survey maps exist. Averagely
15.8 % of this (that is, the 25%) is completed per year, representing a backlog of about 84%
per year of all applications coming from areas with pre-prepared sectional plans.
The cumulative effect of this 16% completion rate is extraordinary. Table 3 reports the
data on the cumulative effects of annual backlogs imposed on succeeding years and then on
the overall outstanding applications. The Table shows that over the period, the number of
titles completed declined progressively to about 3% by 2002 of outstanding cases. The total
backlog of cases as at 2002 stood at 94,106, representing about 97% of total requests for
documentation, which could not be serviced.
To be able to make rational predictions about the processing time required to reduce the
backlog considerably, an Ordinary Least Square (OLS) model is derived based on the data
obtained. An additional independent dataset, SECTPLAN is included in the data contained in
Table 3 to help estimate the impacts of the prior availability of sectional map for particular
applications on the processing time. The key variables relied on in deriving the regression
model are, thus, YEAR representing the year of applications, APPLICATION, representing the
cumulative applications for particular years, COMPLETED, representing the number of
applications completed in a particular year and SECTPLAN representing the number of
applications for which sectional plans existed. For applications coming from areas where preprepared sectional maps exist, it is expected that the physical details of the proprietary land
units were already known and the processing will not entail a re-survey of the land concerned.
The model summary from the regression analysis shows that the variables SECTPLAN,
APPLICATION and YEAR as defined earlier explains about 65% (R Square = 0.646 in Table
4) of the number of titles completed in any particular year.
Table 1. Descriptive Statistics on Processing Delays
Plot Size
Days
Valid N
(listwise)
N
Minimum
Maximum
Statistic
235.00
235.00
Statistic
0.01
34.00
Statistic
61.42
3530.00
Mean
Statistic
2.27
599.90
Mode
Std. Error
0.59
41.50
Statistic
0.32
258.00
Std.
Deviation
Statistic
9.00
636.19
235.00
Source: Field Survey (2008)
Table 2. Descriptive Statistics
Applications
Sectplan
Completed
Valid N
(Listwise)
N
15
15
15
15
Minimum
2,274.00
172.00
150.00
Maximum
23,409.00
24,59.00
2,663.00
Sum
11,1778.00
27,731.00
17,672.00
Source: Field Survey (2008)
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Mean
7,451.8667
1,848.7333
1,178.1333
Std. Deviation
5773.3847
664.1310
715.0598
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87
Table 3. Cumulative Delays in Land Title Registration
Year
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Cumulative Pending Application
2,274.00
5,809.00
11,318.00
17,472.00
29,120.00
45,584.00
67,633.00
69,363.00
74,563.00
78,682.00
83,494.00
86,328.00
89,677.00
93,607.00
96,769.00
Completed
843.00
150.00
400.00
200.00
1,010.00
1,360.00
2,000.00
650.00
1,906.00
1,018.00
1,977.00
1,098.00
1,147.00
1,250.00
2,663.00
Backlog
1,431.00
5,659.00
10,918.00
17,272.00
28,110.00
44,224.00
65,633.00
68,713.00
72,657.00
77,664.00
81,517.00
85,230.00
88,530.00
92,357.00
94,106.00
% Completed
37.07
2.58
3.53
1.14
3.47
2.98
2.96
0.94
2.56
1.29
2.37
1.27
1.28
1.34
2.75
Source: Field Survey, 2008
Table 5. Reports the regression coefficients of the model.
The results show that the functional relationship between the number of completed
applications and the explanatory variables is:
Table 4. Correlation Coefficient
Model
1
R
.804
R Square
.646
Adjusted R Square
.549
Std. Error of the Estimate
480.1509
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a Predictors: (Constant), SECTPLAN, APPLICATIONS, YEAR
Table 5. Regression Coefficients
Model
1
a
Unstandardized
Coefficients
B
(Constant)
-300404.10
Year
151.31
Applications
0.08
Sectplan
-0.46
Dependent Variable: Completed
Std. Error
75629.66
38.03
0.03
0.29
Standardized
Coefficients
Beta
0.95
0.63
-0.43
t
-3.97
3.98
2.68
-1.60
Sig.
0.00
0.00
0.02
0.14
COMPLETED= −300404.10 + 151.31YEAR+ 0.08APPLICATIO
N − 0.46SECTIONPLA
N
This indicates that the backlog situation increases or deteriorates by some 151 documents
every year and the pre-existence of sectional maps helps improve the backlog situation only
marginally by less than a day. The number of applications per particular year also contributes
marginal increases to the existing backlog situation. What is perhaps more interesting is that,
the number of completed applications in any given year is affected by the total number of
applications submitted in the same year plus the hangover of incomplete applications from
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Raymond T. Abdulai and Felix N. Hammond
previous years. This is explained by the fact that, the backlog cases are not sitting unattended.
Resources are allocated to work on them at various stages. But the limitation of resources
means that the existing resources are so thinly spread out to cover all documents in the
processing line that in the end only few are completed. The model predicts that, if things
remain substantially unchanged, it would not be until the year 2070 that the backlogs could be
cleared (61 years from 2009).
The Policy Induced Asymmetry
The land title registration and concurrence/plotting systems, as already noted, operate in
parallel. Table 6 below reports the data on the documentation requests made to the respective
systems between 1997 and 2002.
Table 6. Comparison of Documents Presented for Land
Title Registration and Concurrence/Plotting Between 1997 and 2002
Applications for
plotting
Applications for land
title registration
Difference
2002
6,861
2001
14,371
2000
9,229
1999
6,610
1998
4,858
1997
4,267
4,412.00
5,077.00
4,447.00
4,811.00
5,830.00
6,025.00
2,449.00
9,294.00
4,782.00
1,799.00
-972.00
-1,758.00
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Source: Data on the applications for plotting was obtained from the records of the Lands Commission,
Accra while the data on applications for land title registration was obtained from the records of the
Land Title Registry, Accra.
According to the data, with the exception of 1997 and 1998, documentation requests
made to the plotting system consistently outstripped those to the land title registration system.
It is known in practice that some real estate owners who can afford prefer to direct their
documentation requests to both systems. But not many do that, as it is costly. This has led,
expectedly, to inconsistent dataset and even the prevalence of irreconcilable information on
the same real estate held in the two systems. This is ironic given that the plotting system
lacked legal recognition while the title registration system is legally compulsory, at least
according to the enabling law. Clearly, there is a market signal in favour of the plotting
system even though the signal is not strong enough to eliminate land title registration
altogether. The question that arises is why will more people prefer a system that has no legal
backing and fraught with inaccurate dataset to one that is legally compulsory based on
scientifically accurate surveys and also claims to offer indefeasible title? The answer to this
could dictate policy direction and reforms. Since costs are widely accepted as important
disincentive to patronage, the ensuing section examines the respective cost implications of the
two systems.
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Restructuring Real Estate Market Information Management to Facilitate …
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The Costs of Real Estate Documentation Requests
The official charges for one documentation under the plotting/concurrence system stood
at about a third (¢300,000.00) of the cost of the land title system, which is ¢950,000.00. The
study found that almost as a convention, in addition to the official charges, actors who request
for documentation make extra official payments to staff of the responsible agencies to get
their requests serviced as early as possible. Table 7 shows the detail extra-official costs
incurred under both systems. Column 1 of the Table indicates the respective milestone
activities available under both the land title and plotting/concurrence systems. To compute the
costs, the costs per milestone activity were first estimated from the data obtained from the
field survey. The costs per documentation procedure were then estimated by accumulating the
costs of all the respective activities required to service particular requests for documentation
under the respective systems. Thus, columns 3-10 are the costs of the different strands of
processes used to service requests for the different forms of documentation under the
registration systems.
Column 10 reports the unit costs incurred to have the documentation request serviced
under the land title registration system.
The data show that the extra official costs incurred in having documentation requests
serviced under the respective strands of procedures under the plotting/concurrence system
range from a minimum of about £3,837.00 to a maximum of about £3,900.00 with an average
costs of about £3,870.00. This is practically similar to the £3,860.00 under the land title
system (see Column 10, row 19). Thus, the extra-official payments do not appear to have
provided any significant incentive for the market to prefer one system to the other. Perhaps
the differences in the official charges rather could explain the differences in the patronage
between the two systems. This by itself does not tell much about the costliness of the system.
Indeed it could even be cheap if the benefits from the documentation to the individual actors
exceed these costs. Economic theory suggests, however, that if that is the case, utility
maximising actors would be willing, subject to affordability, to bear the costs in order to
receive the benefits. This means that overall requests for documentation will be very high if
the benefits exceed these costs. However, given the reality that only 8% of potential requests
for documentation have been made, it leaves no one in doubt that these costs hardly
commensurate with the benefits that actors are gaining from the documentation. But how
much is it costing the system to capture this information?
The Costs of Servicing Documentation Requests
The cost estimates for the respective processes are based on the labour hours spent per
processing tasks as obtained from the field survey. Available direct labour hours are used as
the allocation base to apportion the overhead costs (see Table 8 below).
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Table 7. Non-Official Compliance Costs Grouped by Policy Processes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Compliance
Activity
Transaction
Documents
Site plan
Concurrence
Stool Consent
Plotting
State Consent
Land Title
Oath of Proof
Tax Clearance
Certificate
Stamp Duty
Travel Time
Transportation
Waiting Time
Delays
Total (¢)
Total ($)
Total (£)
Source: Field Survey (2008)
State Lease
Renewal
Distributive
Concurrence
Stool Consent
Plotting
Allocation
State Consent
Regularization
Land Title
Regulative
Regulative
Regulative
Distributive
Distributive
Redistributive
Regulative
-
123,000.00
123,000.00
123,000.00
-
-
123,000.00
123,000.00
2,800,000.00
158,529.41
128,000.00
920,000.00
2,800,000.00
107,425.13
158,529.41
128,000.00
541,000.00
2,800,000.00
107,425.13
158,529.41
128,000.00
558,783.78
2,800,000.00
107,425.13
158,529.41
2,800,000.00
158,529.41
687,704.92
2,800,000.00
107,425.13
158,529.41
128,000.00
2,800,000.00
107,425.13
158,529.41
128,000.00
2,800,000.00
107,425.13
158,529.41
142,196.65
142,196.65
142,196.65
142,196.65
142,196.65
142,196.65
9,509,120.00 9,509,120.00 9,509,120.00 9,509,120.00 9,509,120.00 9,509,120.00
18,899,120.00 18,899,120.00 18,899,120.00 18,899,120.00 18,899,120.00 18,899,120.00
1,790,780.00 1,790,780.00 1,790,780.00 1,790,780.00 1,790,780.00 1,790,780.00
28,600,824.67 28,600,824.67 28,600,824.67 28,600,824.67 28,600,824.67 28,600,824.67
61,900,570.73 63,178,995.86 62,799,995.86 62,817,779.64 61,900,570.73 62,695,700.78
6,796.68
6,937.05
6,895.44
6,897.39
6,796.68
6,883.99
3,837.22
3,916.47
3,892.97
3,894.07
3,837.22
3,886.51
142,196.65
142,196.65
9,509,120.00 9,509,120.00
18,899,120.00 18,899,120.00
1,790,780.00 1,790,780.00
28,600,824.67 28,600,824.67
62,258,995.86 62,258,995.86
6,836.04
6,836.04
3,859.44
3,859.44
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Table 8. Overhead Labour Rate
Overheads
Administrative
Expenses
Service Expenses
Investment Expense
Total Overheads
Labour Force
Available Labour
Hours/Day
Number Of Days Per
Year
Total Direct Labour
Hours
Overhead Rate Per
Direct Labour Hour
Overhead Rate Per
Direct Labour Hour ($)
Overhead Rate Per
Direct Labour Hour (£)
Source: Field Survey (2008)
Lands Commission
Land Title
Registry
Land Valuation
Board
Survey
Department
¢6,133,170,000.00
¢2,000,000,000.00
¢1,800,000,000.00
¢5,431,000,000.00
¢2,531,200,000.00
¢1,442,170,000.00
¢12,676,170,000.00
¢20,251,510,000.00
264
¢2,400,000,000.00
¢3,200,000,000.00
¢7,600,000,000.00
82
¢2,538,400,000.00
¢1,100,000,000.00
¢5,438,400,000.00
818
¢89,488,000,000.00
¢60,826,000,000.00
¢155,745,000,000.00
251
¢1,949,400,000.00
¢4,689,400,000.00
¢9,170,000,000.00
539
8
8
8
8
8
260
260
260
260
260
549,120.00
170,560.00
1,701,440.00
522,080.00
1,121,120.00
¢36,879.94
¢44,559.10
¢3,196.35
¢298,316.35
¢8,179.32
$4.01
$4.84
$0.35
$32.43
$0.89
GBP 2.11
GBP 2.55
GBP 0.18
GBP 17.07
GBP 0.47
OASL
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Table 9. Summary Costing of Information Capture
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Information Capture
Process
Concurrence
Stool Land Consent
State Land Consent
Plotting
State Land Allocation
Stamp Duty
Tax Clearance
Certificate
Land Title Lodgement
Certificate Preparation
Title Certification
Regularization
Parcel Plan
Cadastral
Renewal Lease
Source: Field Survey (2008)
Direct
Labour Costs
226,064.86
183,207.80
458,004.23
139,559.77
1,246,417.74
51,896.08
Overheads
522,440.00
461,390.00
723,386.53
251,970.00
1,222,850.00
11,168.00
Total
748,504.86
644,597.80
1,181,390.76
391,529.77
2,469,267.74
63,064.08
15 % MarkUp Margin
112,275.73
96,689.67
177,208.61
58,729.47
370,390.16
9,459.61
Grand Total
860,780.59
741,287.47
1,358,599.38
450,259.23
2,839,657.90
72,523.70
Grand
Total ($)
93.56
80.57
147.67
48.94
308.66
7.88
Grand
Total (£)
49.24
42.41
77.72
25.76
162.45
4.15
44,308.57
15,503.58
38,298.23
52,033.87
2,057,974.10
377,991.64
1,895,021.73
1,199,528.89
102,900.00
142,720.00
347,434.00
164,574.00
1,993,190.00
140,958.00
517,092.00
935,730.00
147,208.57
158,223.58
385,732.23
216,607.87
4,051,164.10
518,949.64
2,412,113.73
2,135,258.89
22,081.29
23,733.54
57,859.83
32,491.18
607,674.62
77,842.45
361,817.06
320,288.83
169,289.85
181,957.12
443,592.06
249,099.05
4,658,838.72
596,792.08
2,773,930.79
2,455,547.72
18.40
19.78
48.22
27.08
506.40
64.87
301.51
266.91
9.68
10.41
25.38
14.25
266.52
34.14
158.69
140.48
Restructuring Real Estate Market Information Management …
93
In this analysis the total overheads are assumed to be the total budgets of the respective
agencies less their personnel expenses. This approach assumes that the greater the direct
labour hours for a task, the greater will be the overhead expenditure incurred. This may not
necessarily be the case but offers a reasonable basis for apportioning the overheads and
sufficient for the purpose of this study. To accomplish this, a single overhead rate for the
respective agencies based on their staff numbers and approved budgets for 2008 is estimated.
The estimates are reported in Table 10. The overhead rates are assigned to all processes
handled by the corresponding agencies. Based on these, the costs for the respective task are
established. Column 1 of Table 9 above is a break down of the policy processes into the major
identifiable activities. The estimated labour and overhead costs for each of the activities
together with their total costs have also been estimated in columns 3, 4, and 5 respectively. It
could be observed that a conservative mark-up of 15 percent (see column 6) has been applied
as provision for the incidents of costs that could not be established. The last two columns
provide the equivalent costs in US Dollars and Pounds for ease of international comparison.
Table 10 shows basically that the overall mean activity costs is ¢1,275,153 (Min = 72,523 and
Max = 4,658,838, St. Deviation = ¢1,379,787).
To appreciate the full scale of the costs incurred by both the government and market
dealers to have real estate market information captured in Ghana, the costs computed from
both ends have been put together. This is presented in Table 11. The Table reports at the top
row, the major individual policy activities and the corresponding policy category under which
they fall. Details of their corresponding compliance and administrative costs are then
computed and reported accordingly.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Table 10. Descriptive Statistics on the Costs of Processing Activities
Mean
Min
Max
St.
Deviation
Direct
Labour Costs
570,415.08
15,503.58
2,057,974.10
719,477.83
Overheads
Total
538,414.47
11,168.00
1,993,190.00
542,361.56
1,108,829.54
63,064.08
4,051,164.10
1,199,815.38
15% Margin
166,324.43
9,459.61
607,674.62
179,972.31
Grand Total
1,275,153.98
72,523.70
4,658,838.72
1,379,787.69
Source: Field Survey (2008)
The Budget Share
To examine the adequacy of the resources devoted to these systems, the budgets of the
responsible organisations are examined. But in the absence of any universally agreed standard
for ascertaining the optimality of organisation’s budgets, a comparative approach is adopted
by which the budgets of these systems are compared with those of similar organisations.
Rows 2 to 5 of Table 8 above show the average annual budgets of these agencies.
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Table 11. The Aggregate Marginal Costs of Real Estate Policy in Ghana
Policy Process
Policy Category
Compliance Costs
Administration Costs
Total (¢)
Total ($)
Total (£)
Source: Field Survey (2008).
State Lease
Concurrence
Renewal
Distributive
Regulative
61,900,570.73 75,178,995.86
5,235,478.15 3,960,999.84
67,136,048.88 79,139,995.70
7,297.40
8,602.17
1,533.09
2,219.82
Stool Consent
Plotting
Allocation
State Consent
Regularization
Land Title
Regulative
Regulative
Distributive
Distributive
Redistributive Regulative
74,799,995.86 62,817,779.64 61,900,570.73
62,695,700.78 62,258,995.86 62,258,995.86
3,841,506.73 4,000,737.73 5,939,877.16
4,458,818.64
7,759,057.98 3,100,219.26
78,641,502.59 66,818,517.37 67,840,447.89
67,154,519.42 70,018,053.84 65,359,215.12
8,547.99
7,262.88
7,373.96
7,299.40
7,610.66
7,104.26
2,191.30
1,514.93
1,573.39
1,534.15
1,697.97
1,431.44
Restructuring Real Estate Market Information Management …
95
Table 12 below presents an analysis of the share of these budgets on national internally
generated revenue, tax and non-tax revenues. The computed proportion of the budgets on the
total government revenue (column 6) is an index of the respective year’s budgets’ share of
national revenue. As reported in Table 12, the land sector’s budget share of the preceding
year’s government revenue oscillates between a minimum of 0.25 percent in 2001 to a
maximum of 0.65 percent attained in 2000. This works out to an overall average of less than
0.5 percent (Mean = 0.49%) share of government internally generated revenue in the
corresponding preceding years. This diverges starkly from for, example, the forestry sector’s
average share of 1.5 percent (minimum = 0.04% and maximum = 2.42%). This is so even
though the two agencies perform similar functions, the land sector focusing on the land side,
which constitutes about 62.9 percent of the total land area and the forestry sector focusing on
forestlands, savannah land transactions, shrub and thickets, which constitutes just about 26.6
percent of the total land area of Ghana (FAO, 2004). It does not, therefore, appear that the
land sector is at least heavily resourced.
Table 12. Comparison with Government Revenue (Million of Cedis)
Year
National
Non-Tax
Revenue
National Tax
Revenue
Total
Internally
Generated
Revenue
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1999
310,400.00 3,089,100.00 3,399,500.00
2000
961,600.00 3,731,700.00 4,693,300.00
2001 3,731,700.00 6,556,900.00 10,288,600.00
2002
252,400.00 8,547,500.00 8,799,900.00
2003
298,600.00 12,556,300.00 12,854,900.00
2004 1,136,300.00 17,403,000.00 18,539,300.00
Mean 1,115,166.67 8,647,416.67 9,762,583.33
Subsequent Year's
Budget Share of
Budget for the sampled
Government
land sector agencies
Revenue (%)
Land
Forestry
Land Forestry
Sector
Sector
Sector Sector
20,762.90
1,343.00
0.61
0.04
30,596.10 74,883.00
0.65
1.60
25,998.50 141,861.00
0.25
1.38
42,029.10 212,585.00
0.48
2.42
66,240.40 286,510.00
0.52
2.23
76,652.60 251,122.00
0.41
1.35
43,713.27 161,384.00
0.49
1.50
Source: Data on non-tax and tax revenue were extracted from the budget statements of Ghana
(1999 – 2005).
The Size of the Personnel Roster
In general, wages and salaries in the public services in Ghana are very low, indeed lowest
in West Africa (United Nations, 2002, p.9). According to the World Bank (2005, p.2) public
service wage bill as a proportion of Ghana’s GDP is lower than the average of low and
middle-income countries. That said, a look at the empirical evidence from a comparative
perspective shows that the land sector in practice deploys very small workforce in real estate
market information capture in Ghana. The evidence as analyzed in Table 13 shows actually
that the workforce sizes of the responsible bureaucracies are shrinking in real terms.
Overall, the agencies involved in real estate market information capture together employ
a total of 1,954 staff across the 10 regions of the country, which is less than 0.3 percent (0.24
%) of the total public service workforce of 800,000.00 (United Nations, 2005). The
distribution of this total staff level among the individual agencies is even more revealing.
Currently the Lands Commission, the agency responsible for the plotting/concurrence and
deed registration systems has a total workforce of 264, an average of 26.4 staff per region and
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Raymond T. Abdulai and Felix N. Hammond
0.03% of the public service. The Survey Department, which also services the land title system
with all its survey map needs, has a workforce of 539, an average of 53.9 staff per regional
office and 0.07% of the public service. The Land Title Registry, the agency responsible for
the land title system has 82 staff (representing 0.01% of the public service).
If it is taken that each staff works for 269 days in a year and 7 hours in a day then the
land title registry deploys a total of 154,406 man-hours per annum. Given that the Land Title
registry receives on the average 7,452 applications per year (see above), it follows that 21
man-hours is required to complete an application (approx 7 man hours for 0.33
documentation service). Thus, with the existing strength, the registry should be servicing
documentation requests at a rate of 28 documents per day, which appears reasonable. Yet the
current average rate of 1178 per year (Table 2) implies that the registry is on the contrary
delivering at about 4 documents per day. This explains a bulk of the delays. This does not
appear to be the result of inadequate staff but inefficient or under employment of available
staff. The absolute manual processing system employed in servicing requests worsens this.
The situation is practically the same with the plotting/concurrence and deed registration
systems under the Lands Commission.
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IMPLICATIONS
Figure 1 illustrates the implications of the preceding analyses. In Fig 1, take the vertical
axis to represent the price of particular category of real estate; say real estate class X, while
the horizontal axis indicates the likely quantities associated with particular prices. Take D0 to
denote the social optimal demand function for class X real estate under conditions of costless,
perfect and equally distributed information. At this position, the equilibrium or optimal
quantities of class X property expected to be purchased will be Q0 at unit price P0.
This price and quantity arrangements represent informed purchasers decision points and
it will be the point at which the real estate market will be said to be performing most
efficiently. Typically, few if any, real estate markets, like other specialists markets operate, as
mentioned earlier, at this level owing to general paucity of information in pristine real estate
markets. As Senior (1854; cited in Rose, 2002) argues “the detail of commerce are so
numerous, the difficulty of obtaining early and accurate information is so great, and the facts
themselves are so constantly changing that the most cautious merchants are often forced to act
upon very doubtful premises”. This is even more so in the developing world where as Geertz
(1978, p.29) asserts: “information is poor, scarce, mal-distributed, inefficiently communicated
and intensely valued”. This is not different from what actually pertains in Ghana, with only
8%real estate market information capture, huge backlogs of documentation requests to clear
and inordinate inaccuracies in the records system.
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Table 13. Real Estate Sector Workforce.
Agency
2001
Others
Total
Professionals
Subprofessional
52
24
211
33
29
23
7
Lands
Commission
Survey
Department
OASL
Land Title
Registry
Land
Valuation
Board
Total
Percentage
Source: Field Survey (2008).
2005
Others
Total
Professionals
Subprofessional
287
39
16
209
264
Inter-census
Percentage
Change
-8.01
459
521
33
35
471
539
3.45
21
3
205
56
249
66
23
23
21
36
301
23
251
82
0.80
24.24
36
28
773
837
35
655
128
818
-2.27
151
7.70
105
5.36
1,704
86.94
1960
100.00
153
7.83
763
39.05
1,132
57.93
1,954
100.00
-0.31
98
Raymond T. Abdulai and Felix N. Hammond
Price
P1
P0
S
c
b
a
D1
e
P2
d
D0
D2
Q2
Q0
Q1
Quantity
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Figure 1. The Dynamics of Asymmetric Property Information
The implications for Ghana can be daunting. As Akerlof (1970), Spence (1973),
Rothschild & Stiglitz (1976) and Stiglitz (2001) have found, in such a system because real
estate owners tend to know more about their properties including the quality of their titles
than potential buyers or lenders, if purchasers proceed on the basis of their less than full and
accurate information, multiple equilibriums could result. These may comprise one in which
purchasers/lenders overvalue the intrinsic qualities of the real estate and the other in which
they under value the intrinsic qualities. Much of the traditional literature on information
asymmetry cited above focused almost exclusively on the scenario in which purchasers
under-value the intrinsic qualities of commodities. The deeper implications of the scenario in
which purchasers overvalue the commodity have has little attention in the literature and this
is, thus, explored in this chapter.
If the intrinsic qualities of properties are overvalued, purchasers could bid higher or
lenders could grant higher credit more than their actual optimal price. This means that instead
of operating along D0, such purchasers or lenders could, in fact, operate on a sub-optimal
level D1. At this level, Q1 instead of Q0 quantities of say class X real estate will be demanded
at unit price of P1. The practical consequences of the D1 scenario are that real estate or land
resources in this category will more likely be allocated to uses above their optimal capacity.
This will lead to an efficiency loss as real estate is presumably taken out of its current use to a
new use that will produce returns that are less than the price (P1) offered for the real estate.
For example, if residential real estate is overpriced to the levels of commercial real estate, the
optimal returns produced by the particular real estate will match the optimal (P0) for
residential real estate (not for commercial real estate) and will most likely fall below those of
commercial real estate say P1. This premature allocation to commercial use may not be
sustainable and could result in under utilization or inefficient use of the real estate at the
expense of its actual optimal use, which is residential. From the credit market standpoint, real
estate owners whose real estate is overvalued by credit institutions will have less incentive to
pay up their loans as the amount of loan granted is likely to be much higher than the true price
of the real estate. They, therefore, stand to gain more if they fail to pay up their loans.
These situations will most likely impose on society a social cost or deadweight loss
represented by the triangle bce in Figure 1 on each unit of class X real estate acquired at price
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99
P1. Motivated by the higher windfall returns or rent associated with overpricing, real estate
owners are likely to firstly ensure that the rent associated with overpricing persists and
secondly that where possible they grow by hyping the utilities of their real estate and
downplaying the defects in their real estate to compel purchasers to overprice their bid and
operate along D1. Essentially, this is accomplished by withholding information, for instance,
regarding defects in their real estate. This requires the expenditure of real resources to provide
such information blockade through, for instance, the preparation and dissemination of false or
misleading boundary maps and documents, the employment of personnel to peddle
misinformation and even to maintain some form of physical presence on the land or real
estate in the form of land guards among others.
These expenditures are incurred by diverting resources away from other productive
sectors or uses just to prevent accurate information from getting to purchasers with the sole
aim of maintaining a sub-optimal condition D1. Indeed, if real estate owners do not protect
this rent and, hence, overpricing, they can be sure that other suppliers (erstwhile purchasers
who have now gained the true position of the information and, thus, realise they can gain by
operating as suppliers) will attempt to enter the market and increase supply thereby forcing
prices downward towards P0. This shows that under conditions that lead to D1 scenario the
market does not provide appropriate incentives for information disclosure by real estate
owners as their profits are maximised by the asymmetry and hence without any exogenous
interventions, the asymmetry will prevail.
Conversely, if purchasers or lenders undervalue the intrinsic qualities, they do so because
they perceive real estate in general as potentially problematic either as a result of title defects
or otherwise. The result is that even high quality real estate in terms of title perfections, for
instance, will also be priced the same as low quality ones by purchasers and creditors. This
will lead to D2 scenario at lower quantity Q2 and price P2. As Akerlof (1970) demonstrates,
when this happens, high quality real estate owners will have no incentive to put their real
estate on the market and hence most high quality real estate will be withdrawn leaving only
problematic real estate on the market.
Nonetheless, on occasions, circumstances such as urgent need for money and other
emergencies could force owners of high quality real estate to put their real estate on the
market for sale. But then, real estate owners in bringing their real estate onto the market will
prefer to supply their real estate between Q2 and Q0 in their existing uses because the returns
from that use (discounted over time) as given by the area under the supply curve (abQ2Q0)
exceed the price that is available in the market. This leads automatically to efficiency losses
as these uses fall short of the optimal uses and hence the real estate is denied its highest and
best uses.
If, however, purchasers fail to pay this price, then hard-pressed real estate owners will be
compelled to sell their real estate at the price of a low quality real estate. In that case, it is this
high quality real estate owner who eventually bears most of the inefficiency costs of the
asymmetric information since all they may get for their real estate is the price of a low quality
or defective registered real estate. The end result is that the real estate is more likely to be
allocated to uses below their current capacity and would generate less returns than they
should at the expense of the realisation of the social optimal for the society. The only actors
who stand to gain in this scenario are the purchasers as they make savings on price but this
again imposes social costs or deadweight losses represented on Figure 1 as triangle abd for
each unit of real estate purchase on society. As a result, in this scenario, good quality real
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Raymond T. Abdulai and Felix N. Hammond
estate owners have the incentive to disclose their information to purchasers or lenders so that
they could get better price for their real estate.
What emerges from this analysis is that: (a) real estate information asymmetry
fundamentally leads to market distortions; and (b) all forms of market distortions result in
social costs or efficiency losses. These social costs or efficiency losses arise out of a loss of
producer surplus (the area between the supply curve and P0 because some real estate owners
would be willing to sell some quantity of real estate at a price between P2 and P0, and of
consumer surplus (the area between P0 and the demand curve D1) as some buyers would be
willing to pay more than P0 for some of the real estate. In the long run, unless prevented by
artificial barriers such as government interventions and legal impediments, economic theory
suggests that the market will devise its own solutions to overcome the asymmetries. Spence
(1973) drawing on insights from Akerlof (1971) shows that as a market solution, purchasers
and lenders will most likely employ signalling to overcome the asymmetries by devising
proxies or signals that will enable them approximate the key qualities of real estate they
intend to deal in.
In Ghana, the pragmatic market signals used by purchasers in particular to ascertain the
general title quality of real estate includes: (1) the ability of the owner to physically be in
possession of the real estate as evidenced by the construction of permanent fencing,
substantial development of the land and the owners’ ability to deposit building materials on
the site without fear of confrontation; (2) the regular presence of an onsite caretaker; and (3)
positive testimonial from neighbours among others. Some purchasers and lenders rely on
these signals to sort out or screen real estate owners. Particular purchasers or lenders would
have their own predetermined threshold signal levels they require to be sufficiently confident
to progress with the transaction and would sort out real estate sellers or borrowers as either
being above or below the threshold level.
However, these have limited appeal to official purchasers or lenders, as they tend not to
provide all the details that such transactions require including title defects. Yet equipped with
the knowledge that some purchasers and informal lenders would rely on these signals, real
estate owners have the incentive to incur signalling and other costs by again diverting
resources from other productive sectors to acquire or put the relevant signals in place to
attract purchasers. Rothschild & Stiglitz (1976) also show that in addition to signalling or as
an alternative to it, purchasers and lenders may employ screening by offering a menu of
questions or terms and relying on the choices made by real estate owners or borrowers to
screen and categorize them into high quality or low quality real estate. In Ghana, screening is
used more in addition to than as an alternative to signalling in real estate transactions.
Beyond the signalling and screening, private economic agents are also expected to
emerge to take advantage of the accompanying arbitrage opportunity created by the
asymmetry. Indeed, the work of real estate brokers, valuers, agents and solicitors among
others is precisely to specialise in the acquisition and supply of this withheld accurate
information to get the market going not at D1 or D2 but at D0. Where in particular society’s
private actors are sluggish in stepping in to supply the requisite information, which is what
tends to be the case in the developing world, then government intervention may have to be
invoked to bridge the information gaps and get the market going at D0.
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CONCLUSION
The huge backlog of outstanding real estate ownership documentation in Ghana is
perhaps the most crucial bane on the development of that country's real estate market. Any
attempts at relieving that market of its inefficiency must necessary involve measures that will
speed up the rate of information capture and remedy the inaccuracies in the system. The
current arrangement as well as the proposed arrangements under the Land Administration
Project (costing $53 million and funded by international donor agencies like the World Bank),
does not hold the capacity to achieve this. The proposed arrangements are not different in
essence from the system that has led to this situation. The most likely way forward would be a
systematic real estate ownership census throughout the country to be financed by the state.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
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In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 105-125
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 4
INVESTMENT CHARACTERISTICS OF HOUSING
MARKET: FOCUSING ON THE STICKINESS OF
HOUSING RENT
Chihiro Shimizu
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
ABSTRACT
The turmoil in the international financial market since the subprime loan crisis has
had a significant effect on the real-estate investment market in Japan, particularly the
Japan real-estate investment trust (J-REIT) market. This suggests that the real-estate
investment market is becoming part of the financial market. It is necessary to precisely
understand the mechanism of risk generation and cash flow in the real-estate market to
understand the characteristics of the real-estate investment market.
The purpose of this study is to statistically clarify the characteristics of the five
problems that have been recently pointed out as risk factors in the real-estate investment
market for housing. Specifically, we have attempted to clarify the following five intrinsic
problems, which are considered to be characteristics of the housing market: 1) the return
problem, 2) the small-scale investment problem, 3) the risk associated with the
adjustment of rent, 4) the key tenant problem, and 5) the inflation problem, all of which
have been pointed out to be problems in the housing and commercial property markets.
Regarding the risk associated with the adjustment of rent, we investigated the actual
situation in the housing market by considering the decrease in housing rent with the age
of the building and the adjustment of housing rent when a new contract is concluded
between a landlord and a new tenant. The results indicated that the yearly rate of decrease
in housing rent for nontimbered houses is as high as approximately 6% over the first five
years after construction, but decreases to 2.6% over the 5th to 10th years and 2.5% over
the 10th to 20th years, indicating that the long-term rate of decrease in housing rent is
small. The probability of no change in rent was converted to a yearly value of 0.6585,
which means that the revenue from the housing rent of 65% of leasehold properties does
not change. This result revealed that housing rent in the Japanese market is extremely
sticky compared with that in the US. Regarding the risk associated with the adjustment of
rent, the probability of downward adjustment of the housing rent should be considered;
however, in most cases, the housing rent is left unchanged. Even when the housing rent is
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106
Chihiro Shimizu
adjusted downward, decreases of more than 10% comprised only 11.2% of all the
adjustments. Also note that the occurrence of rent adjustment is random with respect to
time; the housing rent market is not strongly affected by the economic environment, in
contrast to the market for office buildings; a turnover of residents occurs because of
events such as marriage, childbirth, and relocation, regardless of the economic cycle,
causing the housing rent to change.
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1. BACKGROUND TO REAL-ESTATE
INVESTMENT MARKET
Owing to the turmoil in the financial market since the subprime loan crisis, which started
in summer 2007, the structure of the real-estate investment market has drastically changed in
Japan and overseas. Not only has the cost of capital procurement increased owing to the
turmoil in the international financial market, but also the ideal risk control method of the
entire financial system is changing; under such circumstances, the mobilization of capital into
the real-estate market has slowed, and only limited capital has flowed into specific areas.
There is clear evidence of macroscopic slackness in the market and differentiation in the
allocation of funds (?).
In particular, Japan experienced a substantial decline of stock prices, particularly in the JREIT market. In October 2008, a house-related REIT was driven to bankruptcy.
In the J-REIT market, the decrease in stock prices of REITs of houses and commercial
properties has been significant since the middle of 2007, even before the start of the turmoil
in the international financial market. The reasons behind this are as follows. In the case of
house REITs, 1) an upward return cannot be expected when the real-estate investment market
is active (return problem), and 2) many properties should be involved in REITs to ensure a
certain scale of investment because the amount of investment per property is small; as a
result, the cost required to examine all of the properties (due diligence cost) is high (smallscale investment problem). In the case of commercial-property REITs, 3) large downward
adjustment of housing rents was implemented in Nagoya City (Narumi) in Aichi Prefecture
and Narashino City in Chiba Prefecture, indicating the uncertainty of profitability in a market
that has been considered to be stable (risk associated with the adjustment of rent), 4) related
to 3), because the share of revenue from key tenants in each commercial property is large,
adjustment of the rent of key tenants will significantly affect the total revenue from the
investment (key tenant problem), and 5) the trend of long-term cost-push inflation has
become apparent, increasing the probability of a decrease in profitability (inflation problem).
These problems will remain even when the condition of the financial market recovers,
because they are structural problems existing in each real-estate market. Therefore, unless
these problems are resolved, it will be difficult for these markets to properly attract
investment funds.
However, there are some misunderstandings associated with the above five problems, as
explained below. Also, these problems can be seen to be attractive when considered from a
different viewpoint or when they are interpreted in terms of an investment fund having
different characteristics.
The purpose of this study is to clarify the nature and structure of these five problems and
the characteristics of the housing market through a positive analysis.
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Investment Characteristics of Housing Market
107
2. CHARACTERISTICS OF HOUSING MARKET
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2.1. Investment Characteristics of Housing Market
For the return problem, two background issues exist: one is that the adjustment of
housing rent seldom occurs, that is, the housing rent is sticky, and the other is that the value
of houses decreases with age, causing the house price and housing rent to decrease. However,
the first issue can be interpreted as providing the investors with stable revenue. The housing
rent does not increase even during an economic upturn, which in turn means that revenue
does not significantly decrease when the market stagnates. This is very attractive for investors
who seek stable revenue.
Regarding the second issue, i.e., the decrease of both the revenue and house price with
increasing age of the building, it is necessary to consider this issue as a risk factor. It is also
necessary to precisely understand the rate of decrease in the housing rent, particularly with
increasing age of the building, and to develop a portfolio that can compensate the decrease in
the housing rent.
The second problem, the small-scale investment problem, is closely related to the third
and fourth problems. The scale of housing investment per apartment block (?) is extremely
small compared with that of other types of real estate. When this fact is considered
paradoxically, the investment can be regarded as diversified. Furthermore, because the scale
of each property is small and the investment is also diversified for one apartment block, the
key tenant problem observed in commercial property funds can be avoided in the case of the
housing market. However, regarding the risk associated with the adjustment of rent, which is
closely related to the return problem, it has been pointed out that the housing rent is
considered sticky only on the basis of the prediction of specialists; no experimental studies on
the mechanism of the adjustment of housing rent have been carried out. Considering the fact
that in some cases rent in the commercial property market, which had been considered to be
stable, was adjusted markedly downward, this problem should be discussed on the basis of
objective analytical results. If the stickiness is demonstrated and the mechanism underlying
the adjustment of housing rent is revealed, we can manage the risks in accordance with the
mechanism.
The final problem is the inflation problem. An increase in the prices of commodities
decreases consumers’ willingness to spend, which has an adverse effect on sales at
commercial properties. Demand for commodities with high price elasticity is strongly
affected by an increase in prices. In contrast, food and residential services have low price
elasticity, in the sense that demand for them does not change significantly when their prices
change, i.e., an increase in prices does not have a significant effect on demand.
For long-term funds such as pensions, the major goal of which is to act as an inflation
hedge, the management of assets, the performance of which is above the rate of increase of
the consumer price index (CPI), is required. Focusing on the constituents of the CPI basket,
housing rent made up 26.3% or approximately one-quarter of the CPI as of 2005. Therefore, it
is possible to consider investment in housing rent as being synonymous with investment in
the CPI. Assuming that the target of long-term funds is a stable CPI rather than a high upward
return, this target can be realized by investing funds in the long-term housing rent market.
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108
Chihiro Shimizu
In addition, the link between the CPI and economic and financial policies has been
strengthening. The central banks of various countries have increased interest rates with
increases in CPI, using the CPI as a policy target; this is known as inflation targeting. In this
sense, for long-term funds investing in the CPI or in housing rent, which is a major
constituent of the CPI, is considered to be an important strategy.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
2.2. Statistical Characteristics of Housing Market
When the characteristics of the housing market are considered from the viewpoint of the
investment market on the basis of the above discussion, the problems in the housing market
can be summarized into the following two problems: 1) housing rent depreciation with the
age of buildings and 2) the adjustment of housing rent.
The first problem is demonstrated by analyses such as regression analysis1. In this study,
the relationship between the age of buildings and housing rent depreciation in the housing
rent market is clarified using a similar method.
Next, we focus on the stickiness of housing rent. This topic is very important in the field
of macroeconomics and has been reported by many researchers.
For example, housing rent is adjusted at the time of contract renewal in the US. Including
new contracts between a new tenant and landlord, and rollover contracts, which are
completed when the tenant decides to remain in the same property after the initial contract has
ended, an average of 29% cases of housing rent (in terms of the number of transactions)
remains unchanged annually in the US2. In particular, it is reported that the percentage of
transactions in which the housing rent remains unchanged at a rollover contract is 36%; the
housing rent in a rollover contract is more sticky than that in a new contract.
It is expected that a similar tendency can also be observed in Japan. In particular, the
housing rent for a rollover contract is not altered during the term of the contract and is rarely
altered when the contract is renewed as long as the same resident continues to stay in the
property, because the adjustment of housing rent is restricted by the Land Lease and House
Lease Law and other factors.
We start by observing macroscale changes in the housing market (3.1), then estimate
housing rent depreciation on the basis of the hedonic function formulated in this study (3.2).
We then clarify the mechanism behind the adjustment of housing rent (3.3). Finally, the
characteristics of the housing market are reevaluated on the basis of the results obtained in
this analysis.
1
For example, refer to the studies by Housing Research and Advancement Foundation of Japan (2008), and
Shimizu, Nishimura, and Karato (2007).
2
In previous reports, US researchers analyzed the stickiness of housing rent by classifying housing rent into two
types of contract, i.e., new and rollover contracts, on the basis of individual data from an American Housing
Survey and a questionnaire-based follow-up survey
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Investment Characteristics of Housing Market
109
3. MACROSCALE CHANGES IN HOUSING RENT AND HOUSE PRICES
AND THE STICKINESS OF HOUSING RENT
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
3.1. Macroscale Changes in House Prices, Housing Rent, and CPI Housing
Rent
Macroscale changes in housing rent are observed in terms of the changes in house prices
and housing rent in the 23 wards of Tokyo.
First, it is necessary to estimate price indices to analyze macroscale changes in housing
rent and house prices.
Each house is different in terms of specifications and facilities, and it is not possible to
find two identical properties. Even if the specifications and facilities are the same, the extent
of deterioration will differ if the age of the building differs. In other words, the housing
market has a unique feature that no identical properties exist. In addition to this uniqueness,
the advance of technologies related to houses (particularly condominiums) is relatively fast,
and the quality of new properties increases with the progress of time. Such characteristics
have already been revealed in many previous studies.
To deal with the uniqueness of properties and rapid changes in their quality, the hedonic
price method and repeat sales method can be used to estimate house price indices. For
example, both the Halifax house price index, which is a typical house price index in the UK,
and the Recruit house price index in Japan are estimated by the hedonic price method; while
the Case-Shiller house price index, which is a typical house price index in the US, is
estimated by the repeat sales method.
In this study, we used the same estimation method as that adopted to estimate the Recruit
house price index and the data of the weekly housing advertisement magazines (for housing
rent) published by Recruit Co., Ltd. A housing rent index (hereafter, hedonic rent index) and
a house price index are estimated from the housing rent at the time of new contracts and
house price information, respectively, both of which are provided by Recruit Co., Ltd. The
estimated indices and the CPI rent index (Japanese CPI for rent) are compared.
In the series of analyses in this study, the following eight indices are analyzed and
compared: 1) nontimbered house price index, 2) timbered house price index (both of which
are estimated using the house price data prepared by Recruit Co., Ltd.), 3) hedonic rent index
(nontimbered + timbered), 4) nontimbered house hedonic rent index, 5) timbered house
hedonic rent index, 6) CPI rent index, 7) timbered house CPI rent index, and 8) nontimbered
house CPI rent index (the last two indices are obtained from the breakdown of the CPI rent
index). The reason for comparing estimated indices with the CPI rent indices is that the CPI
rent index is estimated on the basis of continuously paid housing rent. The revenue from
housing rent, which predominantly determines the performance of real-estate investments, is
calculated using the housing rent actually paid by a tenant, rather than the
housing rent in
the new contract. It is considered that the CPI rent index appropriately reflects these
considerations.
The hedonic price method is a method of estimating the price index by formulating the
structure of house prices and housing rent using a generalized regression analysis method.
Table 1 summarizes the results of regression analysis to estimate the housing rent for
both nontimbered and timbered houses, the nontimbered house price, and the timbered house
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110
Chihiro Shimizu
price. The adjusted values of R2 for the housing rent function, the nontimbered house price
function, and the timbered house price function are 0.657, 0.833, and 0.691, respectively. All
three models can estimate the price with a relatively high power of explanation.
The nontimbered house hedonic rent index, the timbered house hedonic rent index, and
the nontimbered house hedonic rent index for the central business district (CBD), which
includes Chiyoda, Chuo, and Minato wards, were estimated using a similar method to that
used for the house price index (Table 2).
Table 1. Estimation results of hedonic rent/price: 1986-2006
Property
Characteristics
(in log)
Constant
Non-timbered HP
(house price)
Coefficient
t-value
4.335
555.10
Timbered HP
(house price)
Coefficient
t-value
5.118
562.46
－
－
－
－
-0.185
-194.36
FS: Floor space
-0.230
-601.32
0.007
5.97
－
－
RW:Road Width
－
－
－
－
0.179
144.57
-0.037
-281.15
-0.184
-333.49
-0.070
-165.22
-0.039
-134.03
-0.061
-93.28
-0.137
-146.88
-0.036
-85.81
-0.034
-36.40
-0.058
-50.27
BD:Bus Dummy
-0.020
-1.61
－
－
-0.201
-8.59
BD×WT
-0.050
-10.15
-0.056
-38.85
0.009
1.07
－
－
0.020
37.85
－
－
0.009
38.66
0.016
34.15
0.010
21.72
-0.043
-93.52
－
－
－
－
-0.049
-102.25
－
－
－
－
－
－
－
－
0.010
12.21
－
－
－
－
0.035
7.82
LA:Lot Area
Age: Age of
building
WT: Walk Time
to the nearest
station
TT: Travel Time
to CBD
TU: Total Units
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
HR
(house rent)
Coefficient
t-value
9.009
2193.65
RT: Market
reservation time
FF: First Floor
Dummy
THD:Timbered
house dummy
SD:South
Dummy
LD:Land
Dummy
Ward (city)
Dummy
RDi (i=0,…,I)
Railway/Subway
Line Dummy
LDj (j=0,…,J)
Time Dummy
TDi (i=0,…,I)
Adjusted R
square=
Number of
Observations=
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.657
0.833
0.691
718,811
218,768
338,222
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Table 2. Estimation results of hedonic rent: 1990-2006
Property
Characteristics
(in log)
Non-timbered HR(house
rent)
Coefficient
t-value
9.223
3371.72
-0.220
-529.75
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Constant
FS: Floor space
Age: Age of
-0.041
building
WT: Walk Time
to the nearest
-0.036
station
TT: Travel Time
-0.029
to CBD
BD:Bus Dummy
-0.036
BD×WT
-0.048
RT: Market
0.008
reservation time
FF: First Floor
-0.041
Dummy
Ward (city)
Dummy
Yes
RDi (i=0,…,I)
Railway/Subway
Line Dummy
Yes
LDj (j=0,…,J)
Time Dummy
Yes
TDi (i=0,…,I)
Adjusted R
square=
0.680
Number of
Observations=
532,149
Non-timbered
HR(house rent:CBD)
Coefficient
t-value Coefficient t-value
9.596 1918.49
8.859 1000.02
-0.377 -405.27
-0.053
-37.86
Timbered HR(house rent)
-269.91
-0.050
-154.18
-0.039
-101.65
-113.60
-0.041
-68.20
-0.048
-39.80
-63.59
-0.056
-63.69
0.022
14.00
-2.29
-7.90
0.031
-0.049
1.47
-6.06
-0.046
-0.021
-0.39
-0.43
28.33
0.013
28.69
0.007
7.05
-70.28
-0.034
-52.51
-0.035
-10.17
Yes
Yes
Yes
Yes
Yes
Yes
0.695
0.695
153,625
153,625
The changes in the hedonic rent index, nontimbered house price index, and timbered
house price index over time are shown in Figure 1.
Both the nontimbered and timbered house price indices rapidly increased from the first
quarter of 1986 to the fourth quarter of 1987; assuming that the index in the first quarter of
1986 is 1, its value in the fourth quarter of 1987 increased to 2.3 for the nontimbered house
price index and 2.5 for the timbered house price index. Subsequently, the indices decreased
slightly then increased again, and in the fourth quarter of 1990, the nontimbered house price
index increased to 3.2 and the timbered house price index increased to 2.6. In similar studies
conducted over the same period, the indices were estimated using the actual transaction data
of houses in a residential district, and similar results in terms of the rate of increase and the
timing of the peak were obtained3.
On the other hand, the hedonic rent index gradually increased from 1986 to 1992; in the
second quarter of 1992 it reached its maximum value of 1.39, after which it decreased.
3
According to the studies by Shimizu and Nishimura (2006)(2007), the long-term land price index is calculated
using data from actual transactions of land. The long-term land price index increased by a factor of 2.8 from the
first quarter of 1986 to the fourth quarter of 1987. After that, the index decreased and then increased again until
the fourth quarter of 1990. Similar tendencies in terms of the degree of increase and the timing of the peak are
observed in analyses using different data sources, indicating the robustness of the result.
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Chihiro Shimizu
To elucidate the relationship between the hedonic rent and the prices of owned houses,
average houses are considered and the rate of return (ratio of estimated housing rent/house
price (%), hereafter rent/price ratio) was calculated (Figure 2). The rent/price ratio exceeded
6% in 1986; after that, because of the increase in house prices, it decreased to less than 3% in
1990. However, with the subsequent decrease in house prices, the ratio increased again and
surpassed 6.5% in 2001. With the recent increase in house prices, the ratio again decreased to
approximately 5.5% by the end of 2006.
3.5
Non-timbered house price
Index:1986/1st quarter=1
3
2.5
Timbered house price
2
Hedonic house rent
1.5
1
QT2006/4
QT2005/4
QT2004/4
QT2003/4
QT2002/4
QT2001/4
QT2000/4
QT1999/4
QT1998/4
QT1997/4
QT1996/4
QT1995/4
QT1994/4
QT1993/4
QT1992/4
QT1991/4
QT1990/4
QT1989/4
QT1988/4
QT1987/4
QT1986/4
0.5
Figure 1. Trend of house price/rent : 1986/1st quarter～2006/4th quarter
8
6
5
4
3
3.5
2
Index:1986/1st quarter=1
3
Non-timbered house price
2.5
2
Non-timbered house rent
1.5
QT2006/4
QT2005/4
QT2004/4
QT2003/4
QT2002/4
QT2001/4
QT2000/4
QT1999/4
QT1998/4
QT1997/4
QT1996/4
QT1995/4
QT1994/4
QT1993/4
QT1992/4
QT1991/4
QT1990/4
QT1989/4
QT1988/4
QT1987/4
1
QT1986/4
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Rent/Price Ratio
Rent / Price Ratio(%)
7
Figure 2. Trend of hedonic house rent index, price index and rent / price ratio (%) : 1986/1st
quarter～2006/4th quarter
Next, the hedonic rent index, estimated using the housing rents for new contracts, and the
CPI rent index are compared (Figure 3). The hedonic rent index increased by 40% from 1986
to the second quarter of 1992; however, the CPI rent index increased by only 15%. After that,
the hedonic rent index decreased but the CPI rent index continued to increase, although the
trend in the hedonic rent index has been roughly in agreement with that of the CPI rent index
since the fourth quarter of 1994.
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Investment Characteristics of Housing Market
113
1.4
1.35
Hedonic rent
Index:1986/1st quarter=1
1.3
1.25
1.2
1.15
1.1
CPI rent
1.05
1
2006/4
2005/4
2004/4
2003/4
2002/4
2001/4
2000/4
1999/4
1998/4
1997/4
1996/4
1995/4
1994/4
1993/4
1992/4
1991/4
1990/4
1989/4
1988/4
1987/4
1986/4
0.95
To observe the recent trends of these indices, separate estimations are carried out using
the timbered and nontimbered house hedonic rent indices. The trend in the nontimbered house
hedonic rent index, focusing on central Tokyo, was also analyzed to take into consideration
the variations between areas (the results of the regression analysis are summarized in Table
2). Figure 4 shows the nontimbered house hedonic rent index, timbered house hedonic rent
index, nontimbered house CPI rent index, and timbered house CPI rent index, using their
values in the first quarter of 2000 as a baseline. The nontimbered house hedonic rent index in
central Tokyo (for CBD), the nontimbered house hedonic rent index for the 23 wards of
Tokyo, and the timbered house hedonic rent index for the 23 wards of Tokyo decreased by
40, 20, and 10%, respectively, from their peaks to their values in 2000. However, both the
nontimbered and timbered house CPI rent indices continuously increased during the period
when the hedonic rent index was decreasing. The trends of the nontimbered and timbered
house CPI rent indices are similar to those of the hedonic rent indices between 1994 and
2000. In particular, after 2000, the timbered house CPI rent index decreased significantly.
Table 3 summarizes the average annual changes (%) in various indices for different
periods as a summary of the above findings. In 1987-1990, the hedonic rent index increased
by 5.2%, but the increases in the nontimbered and timbered house CPI rent indices were far
smaller, 2.93% and 1.7%, respectively.
1.4
1.3
Non-timbered hedonic rent:CBD
Index:1986/1st quarter=1
Non-timbered hedonic rent
1.2
1.1
Timbered hedonic rent
1
0.9
Non-timbered house CPI rent
Timbered house CPI rent
QT2006/4
QT2005/4
QT2004/4
QT2003/4
QT2002/4
QT2001/4
QT2000/4
QT1999/4
QT1998/4
QT1997/4
QT1996/4
QT1995/4
QT1994/4
QT1993/4
QT1992/4
QT1991/4
0.8
QT1990/4
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Figure 3. Trend of house hedonic rent and CPI : 1986/1st quarter～2006/4th quarter
Figure 4. Compare of Hedonic rent index and CPI : 1990/1st quarter～2006/4th quarter
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Table 3. Annual Change of House Price/Rent Index
Nontimbered HP
(house price)
Timbered
HP
(house price)
1987-1990
27.45%
1991-1993
-12.34%
1994-1996
-12.82%
1997-1999
-4.69%
2000-2002
-1.89%
2003-2005
1.55%
*Average Rate of Annual Change (%)
19.51%
-14.62%
-9.55%
-5.34%
-2.13%
2.23%
HR
(house rent)
Nontimbered HR
(house rent)
5.20%
0.46%
-3.37%
0.02%
0.39%
-0.49%
-0.11%
-3.48%
-0.10%
0.42%
-0.34%
Nontimbered
HR (house
rent): CBD
-4.59%
-4.80%
-0.46%
0.64%
-0.23%
Timbered
HR
(house rent)
CPI: HR
CPI: Nontimbered HR
CPI:
Timbered
HR
2.55%
-2.81%
0.37%
0.38%
-1.03%
2.31%
2.93%
0.33%
0.15%
-0.77%
-0.37%
2.93%
3.79%
1.05%
1.08%
-1.84%
-0.21%
1.70%
2.68%
0.03%
0.00%
-0.52%
-0.46%
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Investment Characteristics of Housing Market
115
Furthermore, between 1991 and 1993, the hedonic rent index decreased
(表３によりますと、マイナスは非木造のみです。ご確認ください。); however, the
nontimbered and timbered house CPI rent indices increased; these increases continued up to
1996.
As discussed above, the housing rent for new contracts and the hedonic rent index
experienced significant increases and decreases during the bubble period and the subsequent
collapse of the bubble economy, respectively. However, the CPI rent index gradually
continued increasing during the bubble period and did not decrease significantly after the
collapse of the bubble economy.
This gradual increase is related to the return problem; however, viewing it from a
different perspective, it can be considered that a stable return can be realized by investing in
the housing market.
When looking at reports on the housing investment market, indices using the housing
rents for new contracts are frequently referred to1. It is considered that the CPI rent index,
which is based on actual payments, is more appropriate as an index because the performance
of housing investments is not calculated on the basis of the housing rents for new contracts,
rather it is calculated as the sum of housing rents of properties in which tenants actually
reside.
Why does the CPI rent index markedly deviate from the hedonic rent index, which is
based on the housing rent for new contracts? Specialists in the US have commented that this
phenomenon is strange for investors in the US.
To understand the background behind this, it is important to observe the mechanism of
the adjustment of housing rents. Discussing this problem is also synonymous with examining
the problem of the adjustment of rent, which has been mentioned for the commercial property
market. In the next section, the problem of the adjustment of housing rent, along with the
change in housing rent due to depreciation with the age of the building is examined by
positive analysis.
3.2. Mechanism of Changes in Housing Rent
In observing the mechanism of changes in housing rent, change in housing rent caused by
depreciation with the age of the building, in addition to the macroscale changes discussed in
section 3.1, should be considered. On the basis of the results of regression analysis, the
change in housing rent caused by the deterioration of the building with age is observed
(Figure 5).
The yearly rate of decrease in the housing rent for nontimbered houses is lower than that
for timbered houses. Furthermore, the yearly rate of decrease in housing rent in the CBD is
small.
The yearly decrease in nontimbered house rent was found to be as large as approximately
6% over the first five years after construction, but to decrease to 2.6% over the 5th to 10th
years and 2.5% over the 10th to 20th years, indicating that the rate of decrease in housing rent
1
In the housing rent indices prepared by Recruit Co., Ltd., and the condominium rent index prepared by STB
Research Institute Co., Ltd. (http://www.athome.co.jp/news/m_index/imagessample02.pdf), the
indices are calculated using housing rents for new contracts.
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116
Chihiro Shimizu
is low. This finding indicates that only the initial decrease in the housing rent, which is
observed over the first several years after the construction of the building, should be
controlled. In other words, the decrease in the housing rent does not have a significant effect
on the investment in properties in which tenants actually reside.
1
Index:First Age=1
0.95
CBD_Non
0.9
Non-timbered
Timbered
0.85
0.8
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435
Age of Building(Year)
Figure 5. Property Age and Rent Level.
h
30
7
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35
Figure 6. Changes in Housing Rent with Time.
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117
The above finding indicates the average trend for housing rents in the 23 wards of Tokyo.
Figure 6 shows the changes in housing rent with time for seven selected properties as
samples.
As shown in Figure 6, adjustment of the housing rent occurs only at the time of contract
renewal; in other words, the housing rent does not change continuously.
The important points here are the timing and monetary range of the adjustment of
housing rent, for example, the occurrence and the range of risks associated with the
adjustment of rent, such as those observed in the commercial property market
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3.3. Frequency of Adjustments and Stickiness of Housing Rent
It was found that housing rent, which is determined in the goods and services market,
changes very slowly compared with price changes in the asset market. Furthermore, the
mechanism of changes in the hedonic rent index, which is determined on the basis of housing
rent in new contracts alone, is different from that of the CPI rent index, which is determined
on the basis of housing rent for both new and rollover contracts.
The period of lease contracts in the Tokyo metropolitan area is generally two years,
during which the probability of rent adjustment is low. In addition, adjustments of housing
rents are markedly affected by the institutional constraints imposed by laws such as the Land
Lease and House Lease Law; the adjustment of housing rent, particularly an increase in
housing rent, rarely occurs, even at the renewal of a contract, as long as the same tenant lives
in the same property (Yamazaki (2000)).
Next, the degree of stickiness of housing rent clarified in the previous study is explained.
The weekly change in the housing rent for a certain housing unit i in period t (Rit) with
respect to the housing rent for the same property in the previous week (Rit-1) was observed
for data obtained from the database prepared by Recruit Co., Ltd. We can obtain the dates at
which the former tenant leaves and a new tenant arrives and the range of adjustment of the
housing rent from the database. However, it is not possible to observe the adjustments of
housing rent at the time of agreement of rollover contracts. According to the analysis of data
provided by major property management companies, the adjustment of housing rent can occur
at the time of agreement of a rollover contract, although this only happened in approximately
3% of cases. In this sense, in the analysis of data obtained from the database prepared by
Recruit Co., Ltd., we should be aware that a constant level of error may be involved in the
estimation of Rit/Rit-1, although the error level is thought to be minute.
Figure 7 shows the distribution of weekly rent changes calculated under the assumptions
described above (n=18,582,863).
The probability of a housing unit having no change in rent was 0.992, indicating the high
potential stickiness of the housing rent. This was converted to a yearly value of 65%
(0.99252). It has been reported that the yearly value for the US is 29%. The stickiness in the
housing rent market in Japan is extremely high.
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118
Chihiro Shimizu
0.0006
0.0005
0.0004
0.0003
0.0002
0.0001
99,2]
9,1.9]
9,1.8]
9,1.7]
9,1.6]
9,1.5]
9,1.4]
9,1.3]
9,1.2]
1.00
9,1.1]
0.91]
0.81]
0.71]
0.61]
0.51]
0.41]
0.31]
0.21]
0.11]
0
Figure 7. Weekly rent change distribution
0.0012
1989-1991
1986-1988
0.001
1989-1991
1992-1994
0.0008
1995-1997
1998-2000
0.0006
1986-1988
2001-2003
2001-2003
0.0004
1986-1988
0.0002
(1.95,1.96]
(1.84,1.85]
(1.73,1.74]
(1.62,1.63]
(1.51,1.52]
(1.4,1.41]
(1.29,1.3]
(1.18,1.19]
(1.07,1.08]
(0.97,0.98]
(0.86,0.87]
(0.75,0.76]
(0.64,0.65]
(0.53,0.54]
(0.42,0.43]
(0.31,0.32]
(0.2,0.21]
0
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Figure 8. Weekly rent change distribution by Year 1
Regarding the risk associated with the adjustment of rent, the probability of downward
rent adjustment is considered. Considering the fact that the adjustment of housing rent is
observed in only 35% of properties, this risk is extremely small. In most cases, housing rents
remained unchanged. Also, when the housing rent was adjusted downward, a decrease in rent
exceeding 10% accounted for only 11.2% of cases.
The distribution of weekly changes in rent for different time periods is shown in Figure 8.
As shown in the figure, the percentage of adjustment and its distribution have varied
markedly over time. In particular, between 1989 and 1991, i.e., a period of rising housing
rents, a large peak exists to the right of 1, and the distribution is skewed to the right. For other
periods, the distributions are similar, and the frequency of adjustment toward a decrease in
housing rent is large.
Figure 9 shows weekly price stickiness in terms of Rt/Rt-1 over time. Except during the
bubble period, the weekly price stickiness of housing rent has remained almost constant with
an average of approximately 0.992 from 1992 to 2006. This finding suggests that the
stickiness of the housing rent estimated in Figure 7 remains similar, except during exceptional
periods such as the bubble period.
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Investment Characteristics of Housing Market
119
1
0.998
0.996
0.994
0.992
0.99
Rt / Rt-1
0.988
0.986
0.984
0.982
0.98
0.978
0.976
0.974
0.972
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
0.97
Year
Figure 9. Weekly rent change distribution by Year2
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Although the above tendency is observed, in the actual management of real estate, factors
such as how long tenants continuously reside in a certain property or the reasons for tenants to
move in and out are important. The lease contract is generally renewed every two years in
Japan. Therefore, it is expected that the adjustment of housing rent is dependent on time
(time-dependent pricing).
No. of observations
Average
Median
Mode
SD
Skewness
Kurtosis
Min
Max
Figure 10. Histogram of completed price spells: duration time
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112
177
108
155
1.507
2.402
53
1,144
120
Chihiro Shimizu
Figure 11. CDF of Price Duration
Nelson-Aalen cumulative hazard estimate
.5
.0 0 2
0
.0 0 1
0
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1
.0 0 3
1 .5
.0 0 4
2
.0 0 5
Smoothed hazard estimate
0
0
200
95% CI
400
analysis time
600
800
Smoothed hazard function
200
400
analysis time
95% CI
Cumulative hazard
600
800
95% CI
y
Figure 12. Estimate Result of Hazard Function
According to the results of previous studies, the weekly probability of tenant turnover
(the former tenant of a property expressing the intention to vacate the property so that it
becomes available for lease) is almost constant at 0.0025 for residential periods of 100 weeks
to approximately 400 weeks. Viewing this from a different perspective, this figure is
converted into a value of stickiness; the weekly probability that a tenant continues to reside in
the same property is 0.9975. This figure corresponds reasonably closely to the stickiness of a
housing unit having no change in rent (0.992) in Figure 7. The stickiness of no rent change
can be converted to a monthly value of 0.9900 (0.9924), indicating that the probability of
tenant turnover in a given month is approximately 1%.
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Investment Characteristics of Housing Market
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These results indicate that the occurrence of tenant turnover, which is when the rent is
most likely to be adjusted, is independent of time; adjustments usually occur because of
events such as marriage, childbirth, and relocation. This means that the housing market is not
strongly affected by the economic environment, in contrast to the market for office buildings;
rather, tenant turnover is triggered by the above events, which are independent of the business
cycle, and the housing rent is adjusted at these times.
4. IMPLICATIONS FOR HOUSING INVESTMENT MARKET
In this study, the characteristics of the housing market are discussed in terms of the
stickiness of housing rent; the degree of stickiness and the mechanism of the adjustment of
housing rent are clarified by statistical analysis.
It is considered that the results of the study have the following implications regarding the
housing investment market, and that the close link between housing investments and the CPI
has several important implications for the management of investments.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Conclusion 1. Inflation Hedge Function
For long-term funds, such as pensions, the major goal of which is to act as an inflation
hedge, the management of assets, the performance of which is above the rate of increase of
the CPI, is required. Focusing on the constituents of the CPI basket, housing rent made up
26.3% or approximately one-quarter of the CPI as of 2005. Therefore, it is possible to
consider investment in housing rent as being synonymous with investment in the CPI. (The
correlation coefficient between the general CPI rent index in the 23 wards of Tokyo and the
CPI rent index is 0.998 and that between the former and the CPI rent for leased houses
under private management is 0.990.) Assuming that the target of long-term funds is a stable
CPI rather than a high upward return, this target can be realized by investing in long-term
housing rent.
However, the actual investment return is considered as net income, which is calculated by
subtracting several costs such as long-term repair expenses from the revenue obtained from
housing rent; we should pay attention to changes in these costs. The analysis of several costs
associated with investments in property will be discussed in future studies.
Conclusion 2. Relationship with Interest Rate Risk
Recently, the link between the CPI and economic and financial policies has been
strengthening. The central banks of different countries have increased interest rates whenever
the CPI has increased, using the CPI as a policy target; this is known as inflation targeting. In
this sense, for long-term funds, investing in the CPI or in housing rent, which is a major
constituent of the CPI, is considered to be an important strategy.
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Chihiro Shimizu
Conclusion 3. Stability of Housing Rent
The hedonic rent index, which is determined on the basis of housing rent in new contracts
alone, is compared with the CPI rent index for the period of 1986 to 2006, including the
bubble period. First, the hedonic rent index increased from 1986 to 1992; assuming that the
index in the first quarter of 1986 was 1, in the second quarter of 1992 it reached its maximum
value of 1.39, after which it decreased.
Next, when compared with the CPI rent index, the hedonic rent index increased by 40%
from 1986 to the second quarter of 1992. However, the CPI rent index increased by only
15%. After that, the hedonic rent index decreased but the CPI rent index continued to
increase, although the trend in the hedonic rent index has been roughly in agreement with that
of the CPI rent index since the fourth quarter of 1994.
Namely, the CPI rent index gradually increased even during the bubble period and
experienced no significant decrease after the collapse of the bubble economy. It is considered
that the CPI rent index, which is determined on the basis of actual payments, is more
appropriate as an index because the performance of housing investments is not calculated
from the housing rents for new contracts, rather it is calculated as the sum of housing rents of
properties in which tenants actually reside. In this sense, the gradual increase in the CPI rent
index is considered as a return problem. Viewing this from a different perspective, it can be
concluded that a stable return can be realized owing to this gradual increase and that the risk
of a macroscopic decrease in the housing rent is extremely limited.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Conclusion 4. Problem Associated with Depreciation with age of Building
Although the risk of a macroscopic decrease in the housing rent is limited, the risk of the
housing rent decreasing with the age of the building is considered for each property. We
examined this by a hedonic approach, and the results indicated that the yearly rate of
depreciation of the housing rent of nontimbered houses is as large as approximately 6% over
the first five years after construction, but decreases to 2.6% over the 5th to 10th years and
2.5% over the 10th to 20th years, indicating that the long-term rate of decrease in housing rate
is small.
This finding indicates that only the initial decrease in the housing rent, which is observed
in the first several years after the construction of the building, should be controlled as a risk
factor. In other words, the decrease in the housing rent owing to the increasing age of the
building has a negligible effect on the investment in properties in which tenants actually
reside.
However, it is known that the lifetime of residential houses is shorter than that of
buildings used for offices. Therefore, it is expected that the long-term costs of repair,
maintenance, and operation, which are generated during the period of investment, may
significantly vary. The risk associated with this factor should be discussed in future studies.
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Investment Characteristics of Housing Market
123
Conclusion 5. Problem Associated with Adjustment of Rent
In the commercial facility market, the significant downward adjustment of the rent of key
tenants has a significant impact on the total revenue from the investment; therefore, the
problem associated with the adjustment of rent has attracted attention. However, in the case of
housing, this problem is less likely to occur because the percentage of the revenue from each
property is small.
The weekly probability of a housing unit having no change in rent was 0.992. This was
converted to a yearly value of 0.6585 (0.99252). This figure indicates that the yearly
adjustment of housing rent is observed in only 35% of properties; conversely, revenue from
the housing rent of 65% of leasehold properties does not change each year. A previous study
reported that the corresponding figure for the US is 29%, demonstrating that the stickiness in
the housing rent market in Japan is extremely high.
Regarding the risk associated with the adjustment of rent, the probability of downward
rent adjustment should be considered. Considering the fact that the adjustment of housing rent
is observed in only 35% of properties each year, this risk is extremely small. In most cases,
housing rents remained unchanged. It was also found that when a downward adjustment of
housing rent occurred, the percentage of cases in which the decrease in rent exceeded 10% of
the original rent was only 33.2% (p.6の日本語には11.2%とありました。)
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Conclusion 6. Independence of Economic Environment
Whether the adjustment of housing rent is correlated with or independent of the economic
environment is very important in composing a portfolio in combination with other assets such
as stocks. The probability of tenant turnover with respect to the residence period was
calculated by formulating a hazard function for the residence period.
When the probability of tenant turnover was converted to a monthly value for stickiness,
it was 0.9900 (0.9924), indicating that the monthly probability of tenant turnover is
approximately 1%. This value is converted to a yearly value of slightly below 12%.
The above results indicate that the occurrence of tenant turnover, which is when the rent
is most likely to be adjusted, is independent of time; adjustments of housing rent occur
because of events such as marriage, childbirth, and relocation. In other words, the housing
market is not strongly affected by the economic environment, in contrast to the market for
office buildings.
The characteristics of the housing investment market were statistically clarified through
the series of analyses explained above.
As a result of the recent turmoil in the international financial market, it is necessary to
reconstruct the real-estate investment market from a comprehensive perspective as a target of
investment and management in the future. In concrete terms, we should put importance on
converting real estate into financial investment products by regarding it as a core asset among
a diverse portfolio by fully extracting the investment characteristics of real estate, focusing on
the housing market.
For example, in deciding the constituents of a portfolio and the share of each constituent,
various factors must be considered: whether to hedge the risk by not choosing investment
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Chihiro Shimizu
products that are linked with the financial market should be pursued; whether products that
hedge the risk of inflation should be pursued; whether higher return than other operating
assets is pursued; or whether high stability is pursued. There are no investment products that
satisfy all of the above factors, and investors should clarify their goals when they invest in
real estate.
Once the characteristics of a source that induces a flow of revenue are fully understood,
the design and development of financial products that extract the advantages of the source
using financial techniques are required. We should note that the characteristics that induce a
flow of revenue depend on the type of real estate.
We hope that a real-estate financial market in which the attractive properties of the
investment market are further enhanced will evolve via the reconstruction of the conventional
financial system.
REFERENCES
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[1]
Abe, Naohito & Akiyuki Tonogi (2008). “Micro and Macro Price Dynamics over
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Goodman, A. C. & Thibodeau, T. G. (2003). “Housing market segmentation and
hedonic prediction accuracy”, Journal of Housing Economics, Vol.12, 181-201.
Gordon, Robert, J. & Todd van Goethem, (2005). “A Century of Housing Shelter
Prices: Is there a downward bias in the CPI”, NBER Working Paper, No. 11776.
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Ito, Takatoshi & Keiko Nosse Hirono, (1993). “Efficiency of the Tokyo Housing
Market”, NBER Working Paper, No. 4832.
Saito, Yukiko & Tsutomu Watanabe, (2008). “Menu Costs and Price Change
Distributions: Evidence from Japanese Scanner Data”.(Mimeo)
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Shimizu, C. & Nishimura, K. G. (2007). “Pricing structure in Tokyo metropolitan land
markets and its structural changes: pre-bubble, bubble, and post-bubble periods”,
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Shimizu, C., Nishimura, K. G. & Asami, Y. (2004). “Search and Vacancy Costs in the
Tokyo Housing Market: An Attempt to Measure Social Costs of Imperfect
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Yamazaki, F. (2000). “Economic Analysis of Land and Housing Market” (in Japanese,
“Tochi to Jutakusijou no keizaibunseki”), University of Tokyo Press.
House Price Index Research Group, (2008). “Research on Improvement of Housing
Market in Japan –the Possibility of Introducing Nonrecourse Loans and the Structure of
House Prices-,” Housing Research and Advancement Foundation of Japan.
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Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
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In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 125-137
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 5
FANNIE MAE AND FREDDIE MAC:
CHANGES TO THE REGULATION OF
THEIR MORTGAGE PORTFOLIOS
N. Eric Weiss
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
SUMMARY
This chapter analyzes the costs and benefits of the Fannie Mae’s and Freddie Mac’s
retained portfolios while they remain under conservatorship.
Increasing numbers of homeowners are threatened with foreclosure because of interest
rate resets on subprime mortgages, combined with stagnant or falling home prices. Congress
responded to this situation by passing the Housing and Economic Recovery Act of 2008
(H.R. 3221, P.L. 110-289), which uses the congressionally chartered, stockholder-owned
government-sponsored enterprises (GSEs), Fannie Mae and Freddie Mac, to lead the market
in providing more affordable mortgages.
The GSEs have retained mortgage portfolios with a combined value of more than $1.4
trillion. The size of the portfolios, past management problems, risks to the financial system,
and potential cost to the taxpayer led, in part, to provisions of the Housing and Economic
Recovery Act that changed the rules governing the activities and regulation of Fannie Mae
and Freddie Mac. The bill created the Federal Housing Finance Agency (FHFA) and
authorized it to regulate the size of the GSEs’ retained mortgage portfolios; it also raised the
conforming loan limit in certain high-cost areas, thereby allowing the GSEs to purchase larger
mortgages in these areas.
Previous regulatory actions have affected the GSEs’ portfolios. In 2006, following
discovery of accounting and management problems, the GSEs agreed to restrictions on their
retained portfolios. In 2007, the Office of Federal Housing Enterprise Oversight (OFHEO),
now the Federal Housing Finance Agency (FHFA), denied requests from both Fannie and
Freddie to raise or eliminate the caps, but these restrictions were relaxed shortly afterwards.
On September 6, 2008, the GSEs were placed in conservatorship (government management).
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128
N. Eric Weiss
One condition of the conservatorship set the portfolio limit to $850 billion as of December
2009, with a 10% yearly decline until the portfolios reach $250 billion.
The GSEs’ portfolios include mortgages and mortgage-backed securities (MBS) that are
subject to financial risks. When these risks are not managed properly, or if market movements
turn dramatically against the GSEs, the government faces two unsatisfactoryalternatives:
eitherlet the GSEs go into default and work to control the financial repercussions, or step in
and assume payments on the GSEs’ debt at a significant cost to taxpayers. The GSEs and
their supporters argue that the profits generated by the investment portfolios enhanced the
GSEs’ ability to support affordable housing programs and reduce mortgage interest rates.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
BACKGROUND
Increasing numbers of homeowners are threatened with foreclosure because of interest
rate resets on mortgages in the subprime and Alt-A mortgage markets, and falling home
prices in formerly rapidly appreciating markets. The Economic Stimulus Act of 2008 (P.L.
110-185) temporarily increased the conforming loan limit, which established the maximum
size of a mortgage that Fannie Mae and Freddie Mac — two congressionally chartered,
stockholder-owned businesses — can purchase.1 The GSEs, which are prohibited by law from
directly making mortgage loans to homeowners, purchase mortgages from the original
lenders, who can then make more loans. Fannie Mae and Freddie Mac add their guarantee of
timely payment of the mortgages and bundle them into mortgage-backed securities (MBS),
which they either keep in their portfolios or sell to investors. The Housing and Economic
Recovery Act of 2008 (P.L. 110-289) created a new regulator (the Federal Housing Finance
Agency or FHFA), and gave it broad authority to regulate the GSEs’ assets including their
retained mortgage portfolios. The legislation could help homeowners by making affordable
refinancing more available and by increasing the conforming loan limit.
On September 7, 2007, regulators placed Fannie Mae and Freddie Mac under
conservatorship, which gives FHFA control over their operations. FHFA increased the limit
for GSEs’ portfolios to $850 billion each until December 31, 2009, and then requires the GSE
to reduce their portfolios by at least 10% annually until they reach $250 billion each.
Absent conservatorship, the Housing and Economic Recovery Act could encourage the
GSEs to purchase mortgages that refinance homeowners out of subprime and other troubled
mortgages by adding new funds to support mortgages for distressed homeowners. The high
cost exception to the conforming loan limit could allow certain homeowners in these high
1
The nationwide conforming loan limit, the maximum size mortgage that Fannie Mae and Freddie Mac can
purchase, was modified by the Economic Stimulus Act of 2008, P.L. 110-185, from $417,000 to add a $729,720
limit in high cost areas; this increase expires December 31, 2008. The Housing and Economic Recovery Act of
2008, P.L. 110-289, makes the high cost exception permanent, but revises downward the maximum mortgage
size to $625,500. These limits are revised annually based on house prices. Reform of the regulator of Fannie
Mae, Freddie Mac, and the Federal Home Loan Banks is contained in Title I of the Housingand Economic
Recovery Act of 2008, signed by the president July 30, 2008. Unless stated otherwise, all bills in this chapter
were introduced in the 110th Congress. Fannie Mae and Freddie Mac are known as government-sponsored
enterprises (GSEs). This chapter will refer to them as GSEs. There is a third housing GSE, the Federal Home
Loan Banks (FHLBanks) that have not created large portfolios and are owned by members, not the public. This
chapter does not discuss the FHLBs; for additional information on them, see CRS Report RL32815, Federal
Home Loan Bank System: Policy Issues, by Edward V. Murphy.
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Fannie Mae and Freddie Mac
129
cost areas to benefit from the lower interest rate that conforming mortgages have compared to
jumbo mortgages.2
The FHFA, with financial support from Treasury, established a conservatorship and, as
part of the conservatorship agreement, temporarily raised portfolio limits to $850 billion.
Portfolio limits are then gradually reduced by at least 10% annually until each portfolio is less
than $250 billion. The temporary increase could allow the GSEs to provide more liquidity to
mortgage markets during the current financial turmoil, but the gradual reduction could
address concerns about systemic risk.3 Treasury’s financial support allows the GSEs to buy
more mortgages than they would otherwise be able to in turbulent financial markets. If the
GSEs respond by acquiring more mortgages, then the GSEs would assume the risk of default
by the homeowner.
At the time that the GSE conservatorship was announced, Treasury announced that it had
signed contracts to provide financial support for Fannie Mae and Freddie Mac. Treasury
agreed to
•
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•
•
make short-term, collateralized loans to the GSEs with interest rates set at the
London Inter Bank Offer Rate (LIBOR) plus 50 basis points (0.5%),
purchase new GSE MBS on the open market, and
purchase senior preferred stock from the GSEs if their liabilities exceed their assets.
In return, Treasury received from each GSE $1 billion in new senior preferred stock and
warrants to purchase 80% of the common stock at a nominal price.
Prior to conservatorship, accounting and management problems at the GSEs led FHFA’s
predecessor, the Office of Federal Housing Enterprise Oversight (OFHEO), to restrict the
GSEs’ activities by limiting the size of their mortgage portfolios. These problems at both of
the GSEs came to light after they agreed to register one class of stock with the Securities and
Exchange Commission (SEC). By law, the GSEs were exempt from filing financial
statements with the SEC. Nevertheless, both agreed to register one class of common stock.4
This irrevocable decision made them subject to requirements to file reports with the SEC on
their finances and on changes in insider stock holdings.
While preparing to register its stock, Freddie Mac announced in January 2003 that it had
understated its earnings, and it began to revise its financial statements and to install
management controls to ensure accurate financial reporting in the future.5 In a restatement
issued November 2003, Freddie Mac increased its net income for 2002 and earlier years by a
total of $5.0 billion. Freddie Mac paid $125 million in civil fines, and $50 million to settle
SEC charges that it fraudulently misstated earnings. In addition, Freddie Mac has paid more
2
Jumbo mortgages traditionally have been defined as mortgages that are larger than the conforming loan limit. With
the combination of a national conforming loan limit (currently $417,000) and a high-cost area exception
($729,750 until December 31, 2008), different people who use the term“jumbo” either refer to loans above
$417,000 or above $729,750. In any case, mortgages not eligible for GSE purchase are typically more expensive
than those that the GSEs can purchase.
3
Systemic risk is the risk that problems in one area (or one company) could spread throughout the system in
potentially catastrophic ways.
4
12 U.S.C. 1717(c)(1) exempted Fannie Mae from registering with the SEC, and 12 U.S.C. 155(g) exempted
Freddie Mac. Section 1112 of P.L. 110-289 ended that exemption.
5
CRS Report RS21567, Accounting and Management Problems at Freddie Mac, by Mark Jickling contains more
details.
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130
N. Eric Weiss
than $410 million to settle investor lawsuits. Unable to file required financial statements with
the SEC until its accounting problems were resolved, Freddie Mac filed its first
timelyquarterly report (10-Q) with the SEC on July 18, 2008.
Fannie Mae registered its common stock with the SEC on March 31, 2003, and thus
became subject to SEC reporting requirements. In September 2004, OFHEO charged that
Fannie Mae had failed to follow Generally Accepted Accounting Principles (GAAP).6 Fannie
Mae responded that its disagreement with OFHEO involved differences in interpretation of
very technical rules, rather than improprieties. After investigating, the SEC announced that
Fannie Mae’s financial reports and managementwere inadequateand directed the GSE to
restate itsearnings for the previous five years. Fannie Mae was unable to file required
financial statements with the SEC until its accounting problems were resolved. In December
2006, Fannie Mae released restated financials for 2001-2005 that reduced its earnings by $6.3
billion, and Fannie Mae subsequently paid $400 million in civil penalties. Fannie Mae
resumed timely SEC filings on November 9, 2007.
Because of concerns over the GSE’s management and controls, OFHEO proposed in
2006 that Fannie Mae should not increase its retained mortgage-related portfolio to more than
the amount held on December 31, 2005 ($727 billion). Fannie Mae agreed. Separately,
Freddie Mac agreed in a letter to OFHEO to limit its annual portfolio growth to 2%, or
approximately $28 billion. Without these agreements, the GSEs would have been able to
increase their retained portfolios as desired.
On August 11, 2007, OFHEO denied requests from both GSEs to relax the limitations on
their portfolios. OFHEO stated that sufficient progress had not been made to resume timely
financial reporting (including annual 10-K and quarterly 10-Q filings with the SEC) and that
management controls were not adequate for more growth.
Approximately one month later (on September 19, 2007), OFHEO announced that it was
making several changes that would have the effect of allowing the GSEs to increase their
retained mortgage holdings to $735 billion each and to grow beyond this.7 First, it gave each
GSE the same portfolio cap as of July 1, 2007.8 Second, it agreed that Fannie Mae could
increase its portfolio at the same rate as Freddie Mac — not more than 2% per year and not
more than 0.5% per quarter. This would allow each GSE to increase its portfolio by$14.7
billion annually, or $3.7 billion quarterly. Third, for the fourth quarter of 2007 (OctoberDecember 2007), each GSE’s portfolio could grow by up to 1%, but the 2% annual cap would
still apply. This would allow each GSE to increase its portfolio size by $7.4 billion in the last
quarter of 2007. Fourth, OFHEO imposed additional reporting requirements on both GSEs.
The GSEs have lost money every quarter starting in the third quarter of 2007. FHFA
placed the GSEs in conservatorship on September 7, 2008. In reaching its decision, the FHFA
cited continuing troubles in the mortgage credit environment in general, and the inability of
the GSEs to raise significant capital in particular.9 As part of the conservatorship, the GSEs
6
CRS Report RS21949, Accounting Problems at Fannie Mae, by Mark Jickling.
Office of Federal Housing Enterprise Oversight, “OFHEO Provides Flexibility on Fannie Mae, Freddie Mac
Mortgage
Portfolios”
September
19,
2007,
available
at
[http://www.ofheo.gov/
newsroom.aspx?ID=388&q1=0&q2=0].
8
Historically, Fannie Mae’s retained mortgageportfolio has been larger than Freddie Mac’s. The difference has
narrowed since the agreements on portfolio size with OFHEO.
9
U.S. FHFA, “Statement of James Lockart,” press release, September 7, 2008, p. 3-5, available at
[http://www.treas.gov/press/releases/reports/fhfa_statement_ 090708hp1128.pdf]. The press release discusses
7
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131
agreed to new rules for their portfolios. Initially, the GSEs would be allowed to expand their
retained portfolios without additional capital requirements to $850 billion each until
December 31, 2009. After that, the conservatorship agreements call for portfolios to decline
10% per year until they reach $250 billion each.10 The GSEs can create and sell an unlimited
amount of MBS without additional capital.
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GSE RISKS
Although lenders had been informed that the GSEs’ bonds were not backed by the U.S.
government, many thought that there was an implied guarantee that the federal government
would back the GSEs, if necessary. There was some basis for this belief, because tax laws
were revised in 1982 to help Fannie Mae avoid becoming insolvent.11 The conservatorships
with their continued bond payments and Treasury financial support add to this justification, as
does testimony by FHFA Director James B. Lockhart before the Senate Committee on
Banking, Housing, and Urban Affairs on October 23, 2008.12 This section discusses potential
financial risks that the reorganized GSEs are likely to confront during and after the
conservatorship. The conservatorship and the agreements with Treasury have placed an all
but explicit guarantee behind the GSEs’ bonds, although stockholders were not protected.
Under the agreements signed with Treasury, the GSEs’ risks are effectively transferred to
the federal government. Treasury has agreed to purchase $100 billion of new preferred stock
on an as needed basis from each GSE.13 In other words, if a GSE were to become insolvent,
the government would invest up to $100 billion in the GSE. The government will receive
warrants to purchase common stock for a nominal cost if it purchases the preferred stock.
Treasury can increase one or both ceilings with a new agreement with conservator(s).
If the GSEs are unable to sell new MBS, the Treasury has agreed to purchase them using
the Federal Reserve Bank of New York as its fiscal agent. The only limit on the amount of
MBS purchased is the debt ceiling. Treasury announced that it has begun to purchase MBS,
but it has not announced the volume of these purchases.14 Treasury has attempted to minimize
the risk by requiring collateral for loans and obtaining first claim on any funds available for
dividends.
financial markets troubles from February 2008 onward, especially a market indicator of lack of confidence in the
GSEs, the spread between GSE debt yields and yields on U.S. Treasuries.
10
U.S. Treasury, Fact Sheet: Treasury Senior Preferred Stock Purchase Agreement, press release, September 7,
2008, p. 2, available at [http://www.treas.gov/press/releases/reports/ pspa_factsheet_090708%20hp1128.pdf].
11
P.L. 97-372, 96 Stat.1726 et seq., “The Miscellaneous Revenue Act of 1982.” See Section 102, titled
“Adjustment to Net Operating Loss Carryback and Carryforward Rules for Federal National Mortgage
Association.”
12
Testimony of FHFA Director James B. Lockhart before U.S. Senate Committee on Banking, Housing and Urban
Affairs, “Turmoil in the U.S. Credit Markets: Examining Recent Regulatory Responses,” 110th Cong., 2nd sess.,
October 23, 2008, available at [http://banking.senate.gov/public/_files/LOCKHARTTestimony1023.pdf]. A
clarification is available at [http://www.ofheo.gov/newsroom.aspx?ID=478&q1=1&q2=None].
13
U.S. Treasury, Fact Sheet: Government Sponsored Enterprise Credit Facility, press release, September 7, 2008,
available at [http://www.treas.gov/press/releases/reports/ gsecf_factsheet_090708.pdf].
14
“US Treasury began buying Fannie, Freddie MBS in September,” Reuters, available at
[http://www.reuters.com/article/rbssFinancialServicesAndRealEstateNews/idUSN03340 78720081003].
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N. Eric Weiss
To conserve GSE funds, the conservators have suspended dividends on common and
preferred stock. After this announcement, the price of the GSEs’ common and preferred
stocks declined. If conservatorship ends or dividend payments resume, the prices of the
various types of stock are likely to increase.
Conservatorship may affect the GSEs’ portfolios because it gives them access to a new
source of funds, the Government-Sponsored Enterprise Credit Facility (GSECF), and allows
Treasury to purchase new GSE mortgage-backed securities. This assures Fannie Mae and
Freddie Mac access to relatively inexpensive funds to finance their portfolios and a ready
market for MBS if they decide to sell them.
Following standard financial risk analysis, GSE risks are broken down into credit risk,
prepayment risk, interest rate risk, and operational risk. These risks are discussed as they
apply to the GSE. How various legislative options would affect these risks is discussed in the
analysis section, which follows.
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Credit Risk. Credit risk is the risk that the borrowers (mortgagors) will not repay their
loans on time. When Fannie and Freddie buy mortgages and combine them into MBS, they
guarantee that the loans will be repaid on time. In 2005, according to media reports, Standard
& Poor’s and most other major observers concluded that because of the different maturity
dates, loan-to-value ratios, private mortgage insurance, and geographic diversification, credit
risk was not a serious problem.15 In hindsight, default rates on loans increased in many places
in the country at the same time, for many classes of mortgages, so geographic diversification
proved to be less of a protection for the GSEs than many assumed it would be.16
Prepayment Risk. Prepayment risk is the risk to an investor that a mortgage will be paid
before its full term is concluded, leaving the investor to find another investment — perhaps
when interest rates have decreased. Prior to the current housing cycle, prepayment risk was
considered more likely to be serious than credit risk. Homeowners prepay for two major
reasons: moving and to obtain more favorable terms. Many subprime borrowers took out their
mortgages anticipating prepaying. Prepayment risk falls on the ultimate holder of a mortgage
or MBS. Since 1986, the GSEs have offered multiclass MBS, which divide prepayment risk
among the different classes. They are customized for investors to match their tolerance and
preference for prepayment risk versus anticipated yield. When GSEs keep the MBS, they also
keep this risk.
Interest Rate Risk. Interest rate risk comes from financing the MBS portfolios by
borrowing money (issuing bonds), and is related to prepayment risk. The GSEs face much
higher interest rate risk for mortgages held in portfolio than for mortgages that they issue as
MBS. To finance the long-term loans held in their portfolios, the GSEs use short-term bonds
and financial derivatives. When interest rates increase, the GSEs must roll over their bonds
with higher-rate ones. When interest rates decrease, homeowners prepay their mortgages, and
15
James R. Haggerty, “Mortgage-Securities Drop Will Depend on Economy,” Wall Street Journal, September 17,
2005, p. B7. For a typical Standard and Poor’s analysis see Victoria Wagner, “Freddie Mac,” Standard & Poor’s
Raging Direct, November 30, 2005. Available at [http://www.freddiemac.com/investors/pdffiles/s-andp2005.pdf].
16
See U.S. FHFA, “Statement of James Lockart,” press release, September 7, 2008, p. 4, citingthe “alarming
levels” of mortgagedelinquency rates as a contributing factor to placing the GSEs in conservatorship.
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133
the GSEs buy new ones at lower rates. Between July 2007 and July 2008, Fannie Mae’s gross
mortgage portfolio rose from $730 billion to $758 billion. Fannie Mae’s mortgage guarantee
business through MBS was much larger, rising from $2.2 trillion to $2.6 trillion during the
same period.17
Interest rate risk can be very serious. Many savings and loan associations became
insolvent in the early 1980s because of it. During that time, Fannie Mae’s portfolio was
poorly hedged. While he was Treasury Secretary, John W. Snow testified that “Fannie Mae
became insolvent on a mark-to-market basis. Only a combination of legislative tax relief,
regulatory forbearance, and a decline in interest rates allowed Fannie Mae to grow out of its
problem.”18 Despite state-of-the-art hedging with financial derivatives, some believe that the
GSEs’ portfolios continue to have significant interest rate risk.
If the GSEs have to make large adjustments to their portfolios, only very large financial
institutions will beableto handlethe other side of the financial transactions. If these financial
institutions are unwilling or unable to take the other side of the financial transaction, the
GSEs could be unable to refinance or adjust their retained mortgage portfolios.19
Operational Risk. Operational risk is the risk of loss due to inadequate or failed internal
procedures and systems. Fannie Mae’s and Freddie Mac’s accounting and management
problems have raised questions about internal controls. Accounting systems provide the basis
for portfolio adjustment decisions. If the accounting system is providing inaccurate
information, the resulting portfolio adjustment decisions are likely to be incorrect.
THE ROLE OF PORTFOLIOS UNDER CONSERVATORSHIP
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
FHFA’s conservatorship announcement cited five reasons for the action:
•
•
•
•
•
Safety and soundness issues including capitalization,
Current market conditions,
Financial performance and condition of each company,
Funding difficulties, and
The critical importance each company has in supporting the residential mortgage
market in this country.20
17
Fannie
Mae,
Monthly
Summary
Highlights:
July
2008,
July
2008,
available
at
[http://www.fanniemae.com/ir/pdf/monthly/2008/073108.pdf].
18
U.S. Department of Treasury, Testimony of Secretary John W. Snow Before the U.S. Senate Committee on
Banking, Housing and Urban Affairs, “Proposals for Housing GSE Reform, ” press release, April 7, 2005, p. 4,
available at [http://www.treas.gov/press/releases/ js2362.htm].
19
In a letter from Alan Greenspan, then-Chairman of the Federal Reserve, to the Honorable Robert F. Bennett, U.S.
Senate,
September
2,
2005,
p.
1,
available
at
[http://online.wsj.com/public/resources/d
ocuments/Greenspan091505.pdf]. Greenspan wrote: “Moreover, the success of interest-rate-risk management,
especially the exceptionally rapid timing necessitated by dynamic risk adjustments, requires that the ultimate
counterparties to the GSEs’ transactions provide sufficient liquidity to finance an interest-rate-risk transfer that
counters the risk. Otherwise, large and destabilizing adjustments will result in sharp changes in the interest rates
required to rebalance and hedge a portfolio.”
20
U.S. FHFA, “Statement of FHFA Director James B. Lockhart,” press release, September 7, 2008, p. 5, available
at [http://www.treas.gov/press/releases/reports/fhfa_statement_ 090708hp1128.pdf].
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134
N. Eric Weiss
The issues of current capitalization, financial performance and condition, and the
inability of each GSE to fund itself directly arguably relate to problems created by financing
long-term mortgages with short-term borrowing. Arguably with smaller portfolios, their need
to raise capital would have been less and their capitalization Fannie Mae reports that as of the
end of August 2008, approximately one week before being placed under conservatorship, it
had a portfolio of $760 billion, and Freddie Mac reports that its portfolio at the end of August
2008 was $761 billion.21 Fannie Mae’s portfolio grew at a relatively slow 4.4% annualized
rate in the month of August, but Freddie Mac’s portfolio decreased at an annualized 56.2%
rate. Both GSEs appear to have been slowing their portfolio growth rates since February
2008, but this has not been a smooth month-to-month decline. Delinquency rates on
mortgages steadily increased between July 2007 and August 2008. Some might conclude
from this that, in response to financial market conditions, the GSEs were both trying to limit
or reduce their portfolio sizes. One advantage of reducing portfolio size is that it both raises
capital and reduces the need for capital as a cushion against delinquency and losses. The
government’s financial support and the elimination of capital requirements allow each of the
GSEs to increase its mortgage portfolio by approximately $90 billion very inexpensively. It
can also sell new MBS without any reserve against losses. This could increase profitability.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
GSE MORTGAGE PORTFOLIOS
This section analyzes the benefits and costs of proposals to alter the limits on the GSEs’
portfolios.
As discussed above, the conservatorshipagreements with GSE have temporarily increased
GSE portfolio limits to $850 billion each, with this amount declining gradually to $250
billion each. Furthermore, the terms of the conservatorship do not require the GSE to hold
capital against increases in their portfolios or new MBS sold. Absent the conservatorship, the
recently enacted GSE reform bill delegated authority to the FHFA to regulate the GSEs’
portfolios. This section discusses the issues involved in either increasing or decreasing those
limits.
Linking Limits to Subprime Refinances
During the legislative debate on GSE regulation, some proposals to increase the GSEs’
mortgage portfolios contained a requirement that a large percentage (which varied depending
on the proposal) would be devoted to providing subprime borrowers with a way to refinance
out of their high interest rate mortgages into more affordable ones.22 The homeowners would
benefit because they would keep their homes and refinance into a mortgage with lower
21
Fannie Mae, Monthly Summary, August 2008, available at [http://www.fanniemae.com/ ir/pdf/monthly/
2008/083108.pdf]. Freddie Mac, Monthly Volume Summary, August 2008, available at
[http://www.freddiemac.com/investors/volsum/pdf/0808mvs.pdf].
22
CRS reports on subprime mortgages include CRS Report RL33930, Subprime Mortgages: Primer on Current
Lending and Foreclosure Issues, by Edward Vincent Murphy; and CRSReport RL33775, Alternative Mortgages:
Causes and Policy Implications of TroubledMortgage Resets in the Subprime and Alt-A Markets, by Edward
Vincent Murphy.
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135
monthly payments. Some investors holding the subprime mortgages could benefit as they get
out of subprime mortgages that have a higher probability of defaulting and causing losses.
Other investors, such as those expecting interestpaymentsinlater years, would suffer losses
because of the prepayments. The GSEs could benefit because the new mortgages might be
profitable, and the increase in their mortgage portfolios could provide additional profit.
A subprime mortgage can have a fixed rate or an adjustable rate. A fixed rate subprime
can have an introductory reduced payment before becoming fully amortizing at the agreed
upon fixed rate. An adjustable-rate subprime mortgage also can have an introductory “teaser”
period (typically two or three years), before becoming fully amortizing and adjusting based
on some interest rate on a stated schedule. A news story highlighted the case of a subprime
borrower whose mortgage interest rate will increase in 2008 from 8.2% to 14%; the monthly
payment will increase from $3,700 to $8,000.23 The idea is that many subprime homeowners
who cannot afford the subprime mortgage after the reset could afford the monthly payments
of a traditional 30-year mortgage.
For calendar year 2007, even before changes to OFHEO’s policy, the GSEs could
purchase and retain in portfolio approximately $320 billion in mortgages to replace those
being paid off by borrowers. Fannie Mae could purchase and retain in portfolio $124 billion
in mortgages and MBS.24 Likewise, for calendar year 2007, Freddie Mac could purchase and
retain in portfolio $196 billion of mortgages and MBS; $168 billion would replace those
being paid off by borrowers, and $28 billion would be allowed by the 2% growth.25 In
addition, Fannie Mae and Freddie Mac can purchase without limit mortgages that they
assemble in mortgage-backed securities (MBS), add their guarantees of timely payment of
principal and interest, and sell to investors.
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Loans Related to a Public Policy Goal
The portfolio limits could be tied to purchases of loans that are related to the GSEs’
public mission. Examples of other policy goals might include mortgages for higher-risk, lowincome borrowers, jumbo mortgages, energy efficient mortgages, elderly reverse mortgages,
or mortgages targeted to other populations. Often, these policy goals involve a mortgage
instrument without a long track record or with which the GSEs, or the investors who buy the
GSEs’ MBS, have little experience. The GSEs historically have kept some types of
nontraditional loans in their portfolios because they apparently are hard to package and to sell
in MBS at a price that the GSEs find attractive. The GSEs, with their experience, have found
them more profitable to retain than to sell. Allowing the GSEs to retain loans related to
23
Rick Brooks and Constance Mitchell Ford, “The United States of Subprime,” Wall Street Journal, October 11,
2007, p. A1, A16.
24
In the first half of 2007, Fannie Mae’s retained mortgage portfolio experienced nearly $62 billion in liquidations.
Fannie Mae’s annualized liquidation rate was 17%. See Fannie Mae, Monthly Summary, July 28, 2007, available
at [http://www.fanniemae.com/ir/pdf/monthly/ 2007/063007.pdf].
25
Freddie Mac experienced almost $84 million inliquidations and its annualized liquidation rate was 24% in the
first half of 2007. See Freddie Mac, Monthly Volume Summary: June 2007, available at [http://ww
w.freddiemac.com/investors/volsum/pdf/0607mvs.pdf].
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N. Eric Weiss
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another policy goal in their portfolios would then result in lower interest rates to borrowers
who meet the policy’s criteria.26
Also, the GSEs might be willing to purchase nontraditional mortgages related to another
policy goal if there were other provisions that would make the overall change profitable after
adjusting for risk and increased goodwill. For example, a statistical analysis of combined
enterprise profitability reveals that between 1983 and 2001, each $1 million of MBS
outstanding added $2,200 to net income (profit), but each $1 million in retained mortgages or
MBS added $5,300 to net income.27 In other words, a dollar in their retained portfolios
generated more than twice as much profit as a dollar of MBS sold to other investors.
Arguably, this increased profit from retaining a mortgage in portfolio might be sufficient to
induce the GSEs to buy nontraditional mortgages, but only if the nontraditional mortgages
could be retained in portfolio.
Allowing the GSEs to retain these mortgages would benefit nontraditional borrowers.
The GSEs would either expand existing lending programs, such as nontraditional mortgages
targeted to fulfil their housing goals, or create new programs. The interest rates on these loans
would be higher than on prime mortgages — the higher rate would compensate for the higher
risk of default — but the rates would be less than on mortgages financed outside the GSEs’
structure. Even so, not every nontraditional borrower would qualifyunder the GSEs’
underwriting standards.
In light of FHFA’s statements detailing the reasons for placing Fannie Mae and Freddie
Mac under conservatorship, the future of the GSEs’ policy oriented mortgage purchases —
housing goals, and contributions to the housing trust fund and capital magnet fund — is
unclear. With the need to conserve capital to survive, one could argue that these programs
should be suspended. One could also argue, however, that with the federal government’s
backing the need for capital is reduced and that the amount of capital that would be expended
for these programs is relatively insignificant.
Risk Elements
Prior to conservatorship, the costs of increasing the GSE portfolio caps were mainly the
costs of increased risk to the financial system.28 It is difficult to compare potential costs
against concrete benefits of increasing portfolio caps. The GSEs manage many risks common
to many businesses in the financial sector. These risks can affect the companies, stockholders,
employees, bondholders, and business partners, and because of their size, the GSEs’ risks can
also affect the nation’s financial system and the economy. These risks can be analyzed using
the four categories discussed previously.
26
In the secondary market, investors bid on mortgages taking the contracted interest rates as given. If investors
want a higher yield, they offer a lower price for mortgages. Investors might demand a higher yield because the
interest rates on alternative investments have increased, or because risk has increased.
27
This relationship breaks down after 2001. The reason appears to be in part due to the restatement of earnings by
the GSEs, and in part to net interest income almost doubling between 2001 and 2002. Data source: Office of
Federal Housing Enterprise Oversight, Mortgage Markets and The Enterprises in 2006.
28
CRS Report RS22307, Limiting Fannie Mae’s and Freddie Mac’s Portfolio Size, by N. Eric Weiss covers the
risks from the GSEs’ portfolios in more detail.
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Fannie Mae and Freddie Mac
137
Under conservatorship, any losses in excess of the GSEs’ capital will be adirect cost to
the Treasury. While Treasury states that it anticipates that the short-term GSE credit facility
loans and MBS purchases will be profitable, there is no way to guarantee this. Suspension of
dividends has saved funds for the GSEs at the cost of the stockholders who would have
received them.
CONCLUSION
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The GSEs’ portfolios include mortgages and mortgage-backed securities that are subject
to credit risk, prepayment risk, interest rate risk, and operational risk. If these risks are
mismanaged, or if market movements turn unexpectedly against the GSEs, the government
faces two unsatisfactory alternatives: either let the GSEs go into default and try to control the
financial repercussions, or step in and assume payments on the GSEs’ debt at taxpayer
expense. On September 7, 2008, the government chose to assume GSE obligations at taxpayer
expense. The issue of portfolio size will likely continue to be debated as policymakers
consider what form the GSEs should take when they emerge from conservatorship.
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In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 139-149
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 6
OVERVIEW OF THE SECURITIES ACT OF 1933 AS
APPLIED TO PRIVATE LABEL MORTGAGE-BACKED
SECURITIES
Kathleen Ann Ruane*
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
SUMMARY
Mortgage-backed securities that are packaged and issued by private industry participants
are required to comply with the Securities Act of 1933. Issuers of so-called private label
mortgage-backed securities must either register these securities pursuant to the rules the
Securities and Exchange Commission has set forth, or obtain an exemption from registration.
Failure to register or fall under an exemption could result in liability for the issuer and other
parties involved in the offering. Furthermore, material misstatements or omissions in the
offering materials may also result in liability under the Securities Act. This chapter will
provide an overview of the Securities Act of 1933 as it may be applied to mortgage-backed
securities issued by private industry participants.
INTRODUCTION
Generally speaking, there are two types of mortgage-backed securities (MBSs). The first
are those securities that are packaged and issued by government sponsored entities (GSEs) —
the Federal National Mortgage Association (“Fannie Mae”) and the Federal Home Loan
Mortgage Corporation (“Freddie Mac”) — and a wholly owned government corporation, the
Government National Mortgage Association (“Ginnie Mae”). The second are those MBSs that
are packaged and issued by private market participants (i.e., mortgage companies, savings and
loans, and commercial banks), known as private label MBSs.
*
Email: kruane@crs.loc.gov, 7-9135
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140
Kathleen Ann Ruane
The laws governing the issuance of these two types of MBSs are different. MBSs offered
by the GSEs and Ginnie Mae are exempt from the registration requirements and ongoing
disclosure obligations contained in the federal securities laws.1 Private label MBSs do not
enjoy a blanket exemption from the federal securities laws and are classified by the Securities
and Exchange Commission as a type of “asset-backed security” (ABS) that must register
under the Securities Act of 1933 (‘33 Act or Securities Act) or obtain an exemption and
provide continuing disclosures required by the Securities Exchange Act of 1934 (‘34 Act or
Exchange Act).2
This chapter will provide an overview of the registration requirements for private label
MBSs under the Securities Act. It also highlights the most frequently used exemptions for
private label MBSs. It outlines potential liability for fraud and/or material misstatements in
the required disclosures and the consequences for failure to register when required by federal
securities laws. This chapter will not discuss reporting requirements or liability for MBSs
under the Exchange Act of 1934.3
SECURITIES ACT REGISTRATION FOR PRIVATE LABEL MORTGAGEBACKED SECURITIES
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
The Securities Act requires issuers of all types of securities to register the offering with
the Securities and Exchange Commission (SEC) or to qualify for an exemption from the
registration requirements.4 A registration statement consists of two parts: a prospectus, which
must be delivered with every offer to sell the securities and contain the information outlined
in Section 10 of the Securities Act,5 and other information which need not be provided to
potential purchasers but must be on file with the SEC and available for public inspection.6
1
Ginnie Mae is a wholly owned corporation of the U.S. government and the securities it guarantees are exempt
securities under Section 3(a)(2) of the Securities Act of 1933 (15 U.S.C. § 77c(a)(2)) and Section 3(a)(12) of
the Securities Exchange Act of 1934 (15 U.S.C. § 78c(a)(12)). The Federal National Mortgage Association
Charter Act provides that securities guaranteed by Fannie Mae will be exempt securities in the same manner as
those guaranteed by Ginnie Mae. 12 U.S.C. § 1723c. A similar exemption for Freddie Mac-guaranteed
securities is contained in the Federal Home Loan Mortgage Corporation Act. 12 U.S.C. § 1455g. It is worth
noting that these exemptions do not exempt these securities from all of the antifraud provisions. For instance,
Section 10(b) of the Exchange Act and Rule 10b-5 apply to all issuers of securities whether or not the security
was registered. 15 U.S.C. §78j; 17 C.F.R. § 10b-5.
2
Securities Act Release No. 33-8518; 34-5095, 70 Fed. Reg. 1506 (Jan. 7, 2005) (“Final Rule in Regulation AB”).
Codified at 17 C.F.R. Parts 210, 228, 229 et al.
3
Many MBS issuers are able to suspend their reporting obligations under the Exchange Act, because the offerings
typically have such a small number of record holders. See Final Rule in Regulation AB, supra note 2, §III D.
Section 15(d) of the Exchange Act suspends reporting requirements each year so long as there are fewer than
300 record holders at the beginning of the year. 15 U.S.C. §78o(d). This assumes that the MBS is not trading
on a national securities exchange or automated quotation system (which MBSs, again typically, do not),
because such trading would trigger registration requirements under Section 12 of the Exchange Act. 15 U.S.C.
§78l. Suspension of reporting requirements does not mean exemption from liability under other portions of the
Exchange Act. See e.g., 15 U.S.C. §78j.
4
See Sections 3(b), 4, 5, 7 of Securities Act, 15 U.S.C. §77b-g.
5
15 U.S.C. § 77j.
6
Section 7 of Securities Act, 15 U.S.C. §77g.
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141
Failure to file a registration statement when one is required results in a violation of Section 5
of the ‘33 Act and strict liability under Section 12(a)(1).7
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
The Registration Statement for Private Label MBSs
Sections 4 and 5 of the Securities Act require issuers of securities to register the offerings
and provide prospectuses for sales that are not exempt.8 Sections 7 and 10 of the Securities
Act prescribe the information required in the registration statements and prospectuses that are
issued pursuant to offerings under Sections 4 and 5.9 Section 7 requires the registration
statement to contain the information and documents outlined in Schedule A (15 U.S.C.
§77aa), which is the Schedule under which all issuers that are not foreign governments must
file.10 Section 7 grants the Commission the power to prescribe rules and regulations
describing the information and documents to be contained in registration statements if the
Commission deems them to be “necessary or appropriate in the public interest or for the
protection of investors.”11
Pursuant to this authority, the Commission has designed registration statements, which
correspond to the various types of securities and types of issuers of securities. For private
label MBSs, issuers must use either registration statement Form S-1 or Form S-3.12 Form S-3
is the preferable registration statement type for most issuers because it is considered to be less
burdensome than other types of registration statements. In order to be eligible for Form S-3,
in most cases, the registrant must already have a class of securities registered pursuant to
Sections 12(b) or 12(g) of the Exchange Act (15 U.S.C. §78l), or be required to file reports
pursuant to Section 15(d) of the Exchange Act for at least the preceding 12 months (15 U.S.C.
§78o).13 The Commission included this requirement under the theory that information
contained in the disclosures required by these sections could be incorporated by reference into
the new MBS registration, thereby reducing the work required to prepare a new MBS
registration statement.14 The registrant must also have filed all reports required in a timely
manner within the previous 12 months.15
If the MBS offering qualifies as an offering of investment grade securities, however, the
requirements for use of Form S-3 are slightly different. A non-convertible security (such as an
MBS) may qualify as an investment grade security if, at the time of sale, “at least one
nationally recognized statistical rating organization ... has rated the security in one of its
7
Section 12(a)(1) of the Securities Act states that any person who sells a security in violation of Section 5 (15
U.S.C.§77e) is liable to the person purchasing the securities from him for the purchase price with interest, less
any incomereceived from the security or for damages if the purchaser no longer owns the security.
8
15 U.S.C. §§ 77d – 77e.
9
15 U.S.C. §§ 77g, 77j.
10
Section 7 of Securities Act, 15 U.S.C. §77g. A Section 10 prospectus is required to contain much of the
information contained in the registration statement, unless the prospectus is of a type permitted by the
Commission that summarizesor omits information contained in the base prospectus. Furthermore, the
Commission may require more information tobe provided in prospectuses by rule or regulation. 15 U.S.C.
§77j.
11
Id.
12
Final Rule in Regulation AB, supra note 2, §III B.
13
17 C.F.R. § 239.13.
14
See Item 12, Form S-3, 17 C.F.R. § 239.13.
15
17 C.F.R. § 239.13.
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Kathleen Ann Ruane
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generic rating categories which signifies investment grade; typically, the four highest rating
categories (within which there may be sub-categories or gradations indicating relative
standing) signify investment grade.”16 An offering of investment grade MBSs occurs when
MBSs that qualify as investment grade are offered for cash and delinquent assets within the
asset pool do not constitute 20% or more of the pool (measured in dollar volume).17
If the offering is an offering of investment grade MBSs, the registrant is not required to
have securities registered pursuant to Sections 12(b) or 12(g) of the Exchange Act (15 U.S.C.
§78l) or be subject to the reporting requirements of Section 15(d) of the Exchange Act (15
U.S.C. §78o) in order to register using Form S-3.18 The issuer of an offering of investment
grade MBSs still must have filed all reports required in the previous 12 calendar months in a
timely fashion to qualify to use Form S-3.
If the MBS offering does not qualify to use Form S-3, then the offering must be
registered on Form S-1, which is the form all registrants must use if they do not qualify to
register on another form.19
Shelf-Registration
Shelf-registration allows an issuer to file a registration statement and, instead of selling
the securities immediately following the effective date, place the securities on a “shelf” to be
sold when the issuer believes the time to be right.20 This is a popular method of registration
for private label MBSs. Mortgage related securities, a subset of MBSs, automatically qualify
for “shelf-registration.”21 Even if the private label MBS offering in question is not a mortgage
related security, the private label MBS offering may qualify for shelf-registration
nonetheless.22
For private label MBS offerings, the securities may remain on the “shelf” for up to three
years from the initial effective date.23 Once the company “takes down” the securities for sale,
if there has been a change involving the structural features of the MBSs, credit enhancement
or other aspects of the MBSs that were not described in the base prospectus, a new
registration statement, or post-effective amendment may be required.24 Some changes do not
warrant such labor intensive disclosure, however, and the changes may be described in the
final prospectus filed with the SEC.25 If the securities have not been sold by the end of the
original three-year period, another registration statement may be filed.26
16
17 C.F.R. § 239.13 (b)(2). A non-convertible security is a security that cannot be converted into some other
security.
17 17 C.F.R. § 239.13 (b)(5).
18
17 C.F.R. § 239.13 (a)(4).
19
17 C.F.R. § 239.11.
20
See Wilmarth, Jr., Arthur E., The Transformation of the U.S. Financial Services Industry, 1975-2000: Competition
Consolidation and Increased Risk, 2002 U. Ill. L. Rev. 215, 410 (“A shelf registration permits a qualified
issuer to sell securities at any time during an extended offering [period].”).
21
Rule 415 under the Securities Act, 17 C.F.R. §230.415 (a)(vii). In order to be considered a “mortgage related
security” the security must be rated in one of the top two ratings by at least one nationally recognized
statistical rating organization. 15 U.S.C. § 78c(a)(41).
22
17 C.F.R. §230.415 (a). The rule allows for shelf-registration of securities that are registered on form S-3.
23
Rule 415(b), 17 C.F.R. §230.415.
24
See Final Rule in Regulation AB, supra note 2, §III.
25
Securities Act Rule 424, 17 C.F.R. §230.424.
26
Rule 415(b), 17 C.F.R. §230.415.
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Regulation AB
Private label MBSs are required to file registration statements that comply with
Regulation AB.27 Regulation AB is tailored specifically to various types of asset-backed
securities (like MBSs).28 The Commission realized that disclosures required by other SEC
regulations were not properly tailored to elicit useful information for MBS investors.29 Other
regulations required too much information irrelevant to MBSs and little or no information
about other aspects of MBSs that investors needed in order to make informed investment
decisions. Therefore, Regulation AB requires more information about the assets in a particular
securitized pool, delinquent assets in the pool, the structure of the transaction, the experience
of the servicer of the asset pool as well as other parties involved in administering the
particular asset pool at issue, and other information unique to offerings of asset-backed
securities (like credit enhancements on the asset pool).30 Information with respect to the
registrant (management of the registrant company, performance of the registrant company’s
stock) may be omitted for MBS offerings because this information does not necessarily
inform the investor about the potential performance of the asset pool.31
Exemptions
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Certain offerings of private label MBSs may be exempt from registration under the
Securities Act. The most common exemptions for MBS offerings are described below.
Private Placement Offerings (Regulation D)
The most common exemption from registration for MBSs is the exemption for so-called
“private placement offerings.” Section 4(2) of the Securities Act exempts “transactions by an
issuer not involving any public offering.”32 Section 3(b) allows the Commission to exempt
certain offerings, not in excess of a specified dollar amount, from registration by rule or
regulation. Pursuant to its authority in these two sections, the Commission adopted
Regulation D.33 Regulation D, found in Rules 501 through 508 under the Securities Act,
provides guidance to issuers regarding which offerings would not be considered “public
offerings.”34 The issuer must file notice with the SEC of any sales pursuant to Regulation D.35
27
See 17 CFR §§ 229.110 – 229.1123.
“Asset-backed security means a security that is primarily serviced by the cash flows of a discrete pool of
receivables or other financial assets, either fixed or revolving, that by their terms convert into cash within a
finite time period, plus any rights or other assets designed to assure the servicing or timely distributions of
proceeds to the security holders ... ” 17 C.F.R. § 229.1101(c). The definition is intentionally broad, because the
Commission intended to create a principals based approach to disclosure relating to these types of assets rather
than a set of rigid rules for each different type ofABS. See Final Rule in Regulation AB, supra note 2.
29
See Final Rule in Regulation AB, supra note 2.
30
See 17 CFR §§ 229.110 – 229.1123.
31
See Final Rule in Regulation AB, supra note 2.
32
15 U.S.C. §77d.
33
15 U.S.C. §77c (b).
34
17 C.F.R. §§ 230.501-508.
35
17 C.F.R. § 230.503.
28
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Kathleen Ann Ruane
Rule 504
Under Rule 504, an issuer (except an issuer that is an investment company) may sell up
to $1 million worth of private label MBSs in any 12-month period to any number of
purchasers, regardless of their accreditation.36 No information is required to be provided to
investors purchasing securities pursuant to this exemption.37
Rule 505
An issuer may sell up $5 million worth of private label MBSs in a 12 month period to any
number of accredited investors and up to 35 other purchasers.38 Accredited investors are
defined to include large, frequent market participants that are presumed to have the ability to
independently obtain the information that they need.39 If the securities are offered to
unaccredited investors, some disclosure is required under Rule 502, but a full registration
statement is not required.40
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Rule 506
Rule 506 is likely the most common exemption from registration for MBSs. Under this
rule, an issuer may sell any amount of securities to any number of accredited investors41 and
up to 35 so called “sophisticated investors.”42 In order for the unaccredited investors to be
considered “sophisticated,” the issuer must reasonably believe that those investors (or their
representatives) are capable of evaluating the merits and risks of the securities offered.43 If the
securities are offered to unaccredited investors, some disclosure is required under Rule 502,
but a full registration statement is not required.44
Section 4(6) of the Securities Act
This section exempts sales of up to $5 million from registration if the sales are made to
accredited investors.45 To qualify for this exemption, the issuer may not publicly advertise the
sale of the securities, nor may the issuer publicly solicit buyers. The issuer must also file
notice with the Commission of the sale, a requirement similar to that of Regulation D.
36
17 C.F.R. § 230.504.
Id.
38
17 C.F.R. § 230.505. However, in order to calculate the number of purchasers of securities, one must refer to Rule
501 (e), which exempts accredited investors from the total number of purchasers when calculating that number
for purpose of exemptions under Rules 505(b) and 506(b). 17 C.F.R. § 230.501(e).
39
They are, for example, banks, insurance companies, investment companies, employee benefit plans, business
development companies, large charitable and educational institutions, directors, executive officers, and general
partners of the issuer, persons with a net worth about $1 million, persons with an annual income of more than
$200,000, and any trust valued over $5 million that is run by a sophisticated person. 17 C.F.R. § 230.501(a).
40
17 C.F.R. § 230.502(b).
41
See 17 C.F.R. § 230.501(a).
42
17 C.F.R. § 230.506. Rule 506 actually says that there may be no more than 35 purchasers for an offering to
qualify under this section. See 17 C.F.R. § 230.506(2)(i). However, in order to calculate the number of
purchasers of securities, one must refer to Rule 501 (e), which exempts accredited investors from the total
number of purchasers when calculating that number for purpose of exemptions under Rules 505(b) and 506(b).
17 C.F.R. § 230.501(e).
43
17 C.F.R. § 230.506 (b)(2)(ii).
44
17 C.F.R. § 230.502(b).
45
15 U.S.C. § 77d(6).
37
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Rule 144A
Rule 144A allows the unlimited resale of securities that were never registered pursuant to
the Securities Act so long as the purchaser is a “qualified institutional buyer” (QIB).46 QIBs
are defined as enumerated types of institutional investors (i.e., insurance companies or
employee benefit plans) that own over $100 million in securities unaffiliated with the entity
making the offering.47 Because the market for private label MBSs consists primarily of QIBs,
Rule 144A is commonly used.48
Section 28
Section 28 of the Securities Act gives the Commission the authority to, conditionally or
unconditionally, “exempt any person, security, or transaction, or any class of persons,
securities, or transactions, from any provision or provisions of this title or of any rule or
regulation issued under this title, to the extent that such exemption is necessary or appropriate
in the public interest, and is consistent with the protection of investors.”49 The Commission,
therefore, has wide discretion to create exemptions from the registration requirements of the
Securities Act.
PRIVATE RIGHTS OF ACTION UNDER THE SECURITIES ACT
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Sections 11 and 12 of the Securities Act provide private causes of actions for material
misstatements or omissions contained in the registration of private label MBS securities.50
Section 15 of the act creates liability for controlling persons. These causes of action are
described in this section.
Section 11 Civil Liability for a False Registration Statement
Section 11 creates a private right of action for purchasers of securities issued pursuant to
a false or materially misleading registration statement.51 To establish liability, a plaintiff must
show that the registration statement, at the time it became effective, contained a material
misstatement or omission.52 A statement is material if “an average prudent investor ought
46
17 C.F.R. § 230.144A.
17 C.F.R. § 230.144A(a).
48
See Final Rule in Regulation AB, supra note 2.
49
15 U.S.C § 77z-3.
50
It is important to note that these causes of action apply only to public offerings (Section 12) and offerings made
pursuant to an effective registration statement (Section 11). They do not apply to many offerings that are
exempted from registration requirements. As a result, in order to recover for fraud and other deceptive
practices related to the sale of exempted securities, investors likely would need to allege violations of Section
10b of the Exchange Act and Rule 10b-5 (15 U.S.C. §78j; 17 C.F.R. § 10b-5).
51
15 U.S.C. § 77k.
52
Id. Unlike claims for violations of Rule 10b-5 under the Exchange Act, plaintiffs do not need to allege or
provescienter (i.e., knowledge on the part of the defendant) for violations of Section 11 of the Securities Act.
See AlaskaElec. Pension Fund v. Pharmacia Corp., 554 F.3d 342, 348 n.4 (3d. Cir. 2009); J&R Mktg. v. GMC,
549 F.3d 384, 392(6th Cir. 2008). Because claims for violations of Section 11 “sound in fraud,” plaintiffs must
comply with Rule 9(b) ofthe Federal Rules of Civil Procedure. Rule 9(b) requires a plaintiff to “state with
47
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Kathleen Ann Ruane
reasonably to be informed [of the information] before purchasing the security registered.”53
For the purposes of Section 11, a statement is material if, had it been stated correctly or
disclosed, it “would have deterred or tended to deter the average prudent investor from
purchasing the securities in question.”54 Because Section 11 requires an effective registration
statement in order to apply, securities that are sold pursuant to an exemption from registration
are not subject to liability for violations of Section 11.55
Liability for violations may include
•
•
•
the difference between the amount paid for the security and the value at the timethe
suit is brought, or
the difference between the amount paid for the security and the price at which the
security was sold in the market before the suit, or
the difference between the amount paid for the security and the price at which it was
sold after suit, but before judgment is entered, if that amount is less than the damages
representing the difference between the amount paid for the security and the value at
the time the suit was brought.56
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
MBS Suits
A number of lawsuits have been filed by investors against issuers of MBSs alleging
violations of Section 11 of the Securities Act. Alleged violations include failure to comply
with the underwriting standards described in the offering documents, failure to disclose true
risks of default on loans, and misrepresentations that the assets backed by the securities were,
in fact, “investment grade.”57 As these cases move through the courts, issues facing the causes
of action will become more clear.
Defenses
A claim of liability under Section 11 may always be defeated by proof that the purchaser
knew of the untruth or omission at the time the security was acquired.58 Furthermore, if a
defendant can prove that “any portion or all [of the damages suffered by the plaintiff]
represents other than the depreciation in value of such security resulting from” the
misstatement in the registration statement, that portion of the damages is not recoverable.59 In
other words, if a defendant can show that it was not the misstatement or omission in the
registration statement that caused the value of the shares to fall, but some other market force,
the plaintiff cannot recover the loss of value represented by the extraneous influence.
particularity the circumstancesconstituting fraud or mistake. Malice, intent, knowledge, and other conditions
of a person's mind may be allegedgenerally.” Fed. R. Civ. Pro. 9(b).
53
Rule 405 under the Securities Act, 17 C.F.R. § 230.405.
54
Escott v. BarChris Constr Corp., 283 F. Supp. 643, 681 (S.D.N.Y. 1968).
55
15 U.S.C. § 77k.
56
15 U.S.C. § 77k(a).
57
See, Public Employees Retirement System of Mississippi v. Goldman Sachs Group, Inc., Case No. 09-CV-1110
(S.D.N.Y. February, 2009); Public Employees Retirement System of Mississippi v. Morgan Stanley, Case No.
________ (Cal. Sup. Ct. December, 2008); Boilermaker-Blacksmith National Pension Trust v. Wells Fargo
Mortgage-Backed Securities 2006-AR1 Trust, Case No. 09-CV-833 (S.D.N.Y. January, 2009); BoilermakerBlacksmith National Pension Trust v. WAMU Mortgage Pass Through Certificates, Series 2006-AR1, Case
No. C-09-0037 (W.D. WA January, 2009).
58
15 U.S.C. § 77k(a).
59
15 U.S.C. § 77k(e).
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The issuer has absolute liability under Section 11.60 Section 11 allows other individuals,
besides the issuer, to be sued for violations, including corporate executives and others who
signed the registration statement.61 These defendants may assert the “due diligence” defense.62
For the purposes of this defense, there are two portions of a registration statement: the
“expert” portions and the “unexpert” portions. For example, in MBS offerings, the portion
describing the pooling and servicing agreement for the underlying asset pool is prepared and
signed by experts in accounting and auditing.63
Defendants, other than the expert that prepared the “expert” section at issue, may assert a
due diligence defense to the preparation of the expert portions if the defendants can show that,
after a reasonable investigation, they “had no reasonable grounds to believe and did not
believe” there to be any material misstatements or omissions in the expert portion of the
registration statement.64 “Reasonable investigation” means that which is required of a
reasonable man in the care of his own property.65 In other words, those who sign the
registration statement are entitled to trust the experts paid to prepare the expert portions,
absent any red flags.66
With respect to the unexpert portions (and to the expert portions for the expert charged
with preparing and signing those portions), defendants may assert the due diligence defense if
they can show that, after a reasonable investigation the defendants had reasonable grounds to
believe and did believe that there was no material misstatement or omission.67 This is a higher
standard than the standard described in the preceding paragraph.68 Those signing the
registration statement are not entitled to assume all information in it is correct because they
trust those who prepared the statement. The defendants must, at the least, have read the
registration statement and taken into account all knowledge available to them to gauge the
statements accuracy.69
Section 12 Civil Liability Arising in Connection with Prospectuses and
Communications
Section 12 applies to two different scenarios, each of which may apply to the issuance of
private label MBSs. Both are briefly described below.
60
15 U.S.C. § 77k(b).
15 U.S.C. § 77k(a). These persons or entities are principal executive officers, principal financial officers,
controllers or principal accounting officers, directors, persons that are about to become directors, those who
prepared and signed the expert portions of the registration statement, and underwriters.
62
See 15 U.S.C. § 77k(b)(3).
63
See, e.g., Form S-3, Sun Real Estate Trust (August 29, 2007), available at http://www.sec.gov/
Archives/edgar/data/ 1407749/000137468007000006/forms3.txt.
64
15 U.S.C. § 77k(b)(3)(A).
65
15 U.S.C. § 77k(c).
66
BarChris Constr Corp., 283 F. Supp. at 687.
67
15 U.S.C. § 77k(b)(3)(B)-(C).
68
See BarChris Constr Corp., 283 F. Supp. at 688.
69
Id.
61
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Kathleen Ann Ruane
Section 12(a)(1)
Under Section 12(a)(1), a seller70 is strictly liable for selling securities in violation of
Section 5. To establish a claim under this subsection, a plaintiff need only show that he
bought securities and that the securities were not registered.71 The burden is on the defendant
to show that there was an exemption for the offering.
Section 12(a)(2)
Section 12(a)(2) creates liability for any person who sells securities pursuant to a
prospectus or oral communication that contains a material misstatement or omission.72
Liability under this section is not strict liability, however. A defendant who can prove that “he
did not know, and in the exercise of reasonable care could not have known of such untruth or
omission” will not be held liable.73 A defendant may reduce his liability under 12(a)(2) to the
extent that he can show the decrease in the securities’ value was caused by factors other than
the alleged misstatement or omission in the prospectus or oral communication.74 Furthermore,
this section only applies to public offerings; private placements, such as those accomplished
under Rules 506, are not covered.75
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MBS Suits
Many of the suits filed alleging violations of Section 11 in the registration and sale of
MBSs, also allege violations of Section 12(a)(2).76 As these cases move through the courts,
issues facing the causes of action will become more clear.
Section 15 Liability of Controlling Persons
Section 15 makes those persons or entities that, through stock ownership or other
arrangement, control the persons or entities that are liable under Sections 11 and 12 jointly
and severably liable for violations of those sections.77 This provision could become important
for the purposes of private label MBS liability. Issuers of MBSs are typically specially created
for the purposes of a specific offering.78 Therefore, in order to recover for violations of
70
More persons than the issuer may be included in the definition of “seller.” “Sellers” may include individuals such
as solicitors and others who may be financially motivated to sell a security. See Pinter v. Dahl, 486 U.S. 622
(1988).
71
15 U.S.C. § 77l(a)(1).
72
15 U.S.C. § 77l(a)(2).
73
Id.
74
15 U.S.C. § 77l(b).
75
Gustafson v. Alloyd Co., 513 U.S. 561 (1995).
76
See, Public Employees Retirement System of Mississippi, v. Goldman Sachs Group, Inc., Case No. 09-CV-1110
(S.D.N.Y. February, 2009); Public Employees Retirement System of Mississippi v. Morgan Stanley, Case No.
________ (Cal. Sup. Ct. December, 2008); Boilermaker-Blacksmith National Pension Trust v. Wells Fargo
Mortgage-Backed Securities 2006-AR1 Trust, Case No. 09-CV-833 (S.D.N.Y. January, 2009); BoilermakerBlacksmith National Pension Trust v. WAMU Mortgage Pass Through Certificates, Series 2006-AR1, Case
No. C-09-0037 (W.D.WA January, 2009).
77
15 U.S.C § 77o.
78
See Final Rule in Regulation AB, supra note 2, §III B. See, e.g., Public Employees Retirement System of
Mississippi, v. Goldman Sachs Group, Inc., Case No. 09-CV-1110 (S.D.N.Y. February, 2009); Public
Employees Retirement System of Mississippi v. Morgan Stanley, Case No. ________ (Cal. Sup. Ct. December,
2008); Boilermaker-Blacksmith National Pension Trust v. Wells Fargo Mortgage-Backed Securities 2006-AR1
Trust, Case No. 09-CV-833 (S.D.N.Y. January, 2009); Boilermaker-Blacksmith National Pension Trust v.
WAMU Mortgage Pass Through Certificates, Series 2006-AR1, Case No. C-09-0037 (WD.WA. January,
2009).
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Section 11 and 12 in MBS offerings, it may be necessary to sue the persons controlling the
entities making the offering.
SEC ENFORCEMENT OF THE SECURITIES ACT
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The Commission has the statutory authority to bring an action for violation of the
Securities Act, as well as any violation of the rules and regulations issued by the Commission
pursuant to the act.79 Whenever the Commission believes a person has violated or is about to
violate the provisions of the Securities Act, the Commission has the power to issue a cease
and desist order.80 Pursuant to any cease and desist order, the Commission has the authority to
order accounting and disgorgement.81 The Commission may also bring civil or criminal
actions for violations of the act.82
In conjunction with the enforcement described above, the Commission may bring an
action for violation of Section 17 of the Securities Act. Section 17 is a general antifraud
provision. It prohibits any individual, in the offer or sale of securities, from employing
various means or devices of fraud.83 Some courts have held that there is an implied private
right of action under Section 17 (similar to that of Rule 10b-5 of the Exchange Act), but the
Supreme Court has yet to rule on this question.84
79
Sections 19, 20(a) of the Securities Act, 15 U.S.C. §§77s, 77t.
15 U.S.C § 77h-1.
81
Id.
82
15 U.S.C. §77t.
83
15 U.S.C. § 77q.
84
Herman & Maclean v. Huddleston, 459 U.S. 375, 378 n. 2 (1983).
80
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In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 151-159
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 7
EXAMINING THE CONTINUING CRISIS IN
RESIDENTIAL FORECLOSURES AND THE EMERGING
COMMERCIAL REAL ESTATE CRISIS: PERSPECTIVES
FROM ATLANTA
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Jon D. Greenlee
Chairman Kucinich, Ranking Member Jordan, and members of the Subcommittee, I
appreciate the opportunity to appear before you today to examine several issues related to the
condition of the banking system. First, I will discuss credit conditions and bank underwriting
standards, with a particular focus on commercial real estate (CRE), and I will briefly address
conditions in the state of Georgia. I will then describe Federal Reserve activities to enhance
liquidity and improve conditions in financial markets. Finally, I will discuss the ongoing
efforts of the Federal Reserve to ensure the overall safety and soundness of the banking
system, as well as actions taken to promote credit availability.
BACKGROUND
The Federal Reserve has supervisory and regulatory authority for bank holding
companies, state-chartered banks that are members of the Federal Reserve System (state
member banks), and certain other financial institutions and activities. We work with other
federal and state supervisory authorities to ensure safety and soundness of the banking
industry, foster stability of the financial system, and provide for the fair and equitable
treatment of consumers in financial transactions. The Federal Reserve is not the primary
federal supervisor for the majority of commercial banks. Rather, it is the consolidated
supervisor of bank holding companies, including financial holding companies, and conducts
inspections of those institutions.
The primary purpose of inspections is to ensure that the holding company and its
nonbank subsidiaries do not pose a threat to the soundness of the company’s depository
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Jon D. Greenlee
institutions. In fulfilling this role, the Federal Reserve is required to rely to the fullest extent
possible on information and analysis provided by the appropriate supervisory authority of the
company’s bank, securities, or insurance subsidiaries. The Federal Reserve is also the primary
federal supervisor of state member banks, sharing supervisory responsibilities with state
agencies. In this role, Federal Reserve supervisory staff regularly conduct on-site
examinations and off-site monitoring to ensure the safety and soundness of supervised state
member banks. A key aspect of the supervisory process is evaluating risk-management
practices.
The Federal Reserve is involved in both regulation--establishing the rules within which
banking organizations must operate--and supervision--ensuring that banking organizations
abide by those rules and remain, overall, in safe and sound condition. Because rules and
regulations in many cases cannot reasonably prescribe the exact practices each individual
bank should use for risk management, supervisors design policies and guidance that expand
upon requirements set in rules and regulations and establish expectations for the range of
acceptable practices. Supervisors rely extensively on these policies and guidance as they
conduct examinations and assign supervisory ratings.
Beginning in the summer of 2007, the U.S. and global economies entered a period of
intense financial turmoil that has presented significant challenges for the financial services
industry. These challenges intensified in the latter part of 2008 as the global economic
environment weakened further. As a result, parts of the U.S. banking system have come under
severe strain, with some banking institutions suffering sizable losses. The number of bank
failures has also risen this year.
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CONDITIONS IN FINANCIAL MARKETS AND THE ECONOMY
Although conditions and sentiment in financial markets have improved in recent months,
significant stress and weaknesses persist. Corporate bond spreads remain high by historical
standards as both expected losses and risk premiums remain elevated. Encouragingly,
economic growth moved back into positive territory last quarter, in part reflecting a pickup in
consumer spending and an increase in residential investment. However, the unemployment
rate has continued to rise, reaching 9.8 percent in September.
In this environment, borrowing by businesses and households has remained weak. The
available data suggest that household and nonfinancial business debt likely decreased in the
third quarter after having contracted in the first half of the year. For households, residential
mortgage debt and consumer credit fell sharply in the first half of the year, and the decline in
consumer credit continued in July and August. Nonfinancial business debt also decreased
modestly in the first half of 2009 and appears to have contracted further in the third quarter as
net decreases in commercial paper outstanding and bank loans more than offset solid net
issuance of corporate bonds.
Loans outstanding at depository institutions fell in the second quarter of 2009. In
addition, the Federal Reserve’s weekly bank credit data suggest that bank loans to households
and to nonfinancial businesses contracted sharply in the third quarter as well. These declines
reflect the fact that weak economic growth can both dampen demand for credit and lead to
tighter credit supply conditions. Tighter credit conditions are especially challenging for small
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Examining the Continuing Crisis in Residential Foreclosures and the …
153
businesses, which tend to rely more heavily on despository institutions for credit. There are
more than 27 million small businesses nationally that employ about half of the nation’s
private-sector workforce and these businesses have approximately $1 trillion in debt. In a
recent National Federation of Independent Business survey, small businesses reported that
credit conditions were about as tight as in previous recessions; at the same time, their main
economic concern was lower sales.
Results from the Federal Reserve’s Senior Loan Officer Opinion Survey on Bank
Lending Practices in July indicate that both the availability and demand for bank loans are
well below pre-crisis levels. In July, more banks reported tightening their lending standards
on consumer and business loans than reported easing, although the degree of net tightening
was well below levels reported last year. Almost all of the banks that tightened standards
indicated concerns about a weaker or more uncertain economic outlook, and about one-third
of banks surveyed cited concerns about deterioration in their own current or future capital
positions. The survey also indicated that demand for consumer and business loans had
weakened further. Indeed, decreased loan demand from creditworthy borrowers was the most
common explanation given by respondents for the contraction of business loans this year.
Loan quality deteriorated significantly for both large and small institutions during the
second quarter of this year. At the largest 50 bank holding companies, nonperforming assets
climbed more than 20 percent, raising the ratio of nonperforming assets to 4.3 percent of
loans and other real estate owned. Most of the deterioration was concentrated in residential
mortgage and construction loans, but commercial, CRE, and credit card loans also
experienced rising delinquency rates. Results of the banking agencies’ Shared National Credit
review, released in September, also document significant deterioration in large syndicated
loans, signaling likely further deterioration in commercial loans.1 At community and small
regional banks, nonperforming assets increased to 4.4 percent of loans at the end of the
second quarter, more than six times the level for this ratio at year-end 2006, before the
financial crisis began. Home mortgages and CRE loans accounted for most of the increase,
but commercial loans have also shown marked deterioration during recent quarters.
As a result, credit losses at banking organizations continued to rise, and banks face risks
of sizable additional credit losses given the outlook for production and employment. In
addition, while the year-on-year decline in housing prices slowed in the second quarter,
continued adjustments in the housing market suggest that foreclosures and mortgage loss
severities are likely to remain elevated. Moreover, the value of both existing commercial
properties and land, which collateralize commercial and residential development loans, have
declined sharply in the first half of this year, suggesting that banks are vulnerable to
significant further deterioration in their CRE loans. In sum, banking organizations continue to
face significant challenges, and credit markets are far from fully healed.
1
See Board of Governors of the Federal Reserve System, Federal Deposit Insurance Corporation, Office of the
Comptroller of the Currency, and Office of Thrift Supervision (2009), “Credit Quality Declines in Annual
Shared National Credits Review ,” joint press release, September 24.
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Jon D. Greenlee
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PERFORMANCE OF THE BANKING SYSTEM
Despite these challenges, the stability of the banking system has improved since last year.
Many financial institutions have raised significant amounts of capital and have achieved
greater access to funding. Importantly, through the rigorous Supervisory Capital Assessment
Program (SCAP) stress test conducted by the banking agencies earlier this year, some
institutions demonstrated that they have the capacity to withstand more-adverse
macroeconomic conditions than are expected to develop and have repaid the government’s
Troubled Asset Relief Program (TARP) investments.2 Depositors’ concerns about the safety
of their funds during the immediate crisis last year have also largely abated. As a result,
financial institutions have seen their access to core deposit funding improve.
However, the condition of the banking system is far from robust. Two years into a
substantial economic downturn, loan quality is poor across many asset classes and, as noted
earlier, continues to deteriorate as weakness in housing markets affects the performance of
residential mortgages and construction loans. Higher loan losses are depleting loan loss
reserves at many banking organizations, necessitating large new provisions that are producing
net losses or low earnings. In addition, although capital ratios are considerably higher than
they were at the start of the crisis for many banking organizations, poor loan quality, subpar
earnings, and uncertainty about future conditions raise questions about capital adequacy for
some institutions. Diminished loan demand, more-conservative underwriting standards in the
wake of the crisis, recessionary economic conditions, and a focus on working out problem
loans have also limited the degree to which banks have added high-quality loans to their
portfolios, an essential step to expanding profitable assets and thus restoring earnings
performance.
In Georgia, the performance of banking organizations has deteriorated significantly over
the past several quarters as the region’s real estate expansion reversed course. Like their
counterparts nationally, Georgia banks have seen a steady rise in non-current loans and
provisions for loan losses, which have weighed on bank earnings and capital. Since the
turmoil in financial markets emerged more than two years ago, 25 banks in Georgia have
failed. Notably, almost all of the banks that have failed in Georgia thus far were located in the
metro- Atlanta market and had a high percentage of total loans in land acquisition,
development, and construction. Most of the lending activity at these failed banks was related
to the region’s housing boom in the first half of this decade. Also of note, many of the failed
banks relied heavily on brokered deposit funding to support what had been very strong asset
growth. At the end of 2007, the average ratio of brokered deposit funds was 13 percent at
banks in the state of Georgia, compared to just 7 percent at the national level.
It is clear that substantial financial challenges remain for banking institutions, both in
Georgia and across the United States. In particular, some large regional and community
banking firms that have built up unprecedented concentrations in CRE loans will be
particularly affected by emerging conditions in real estate markets.
2
For more information about the SCAP, see Ben S. Bernanke (2009), “The Supervisory Capital Assessment
Program ,” speech delivered at the Federal Reserve Bank of Atlanta 2009 Financial Markets Conference, held
in Jekyll Island, Ga., May 11, www.federalreserve.gov/newsevents/speech/bernanke20090511a.htm.
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CURRENT CONDITIONS IN COMMERCIAL
REAL ESTATE MARKETS
The Federal Reserve has been focused on CRE exposures at supervised institutions for
some time. As part of our supervision of banking organizations in the early part of this
decade, we observed rising CRE concentrations, especially in some large regional and
community banking firms. Given the central role that CRE lending played in the banking
problems of the late 1980s and early 1990s, we led an interagency effort to develop
supervisory guidance on CRE concentrations. The guidance was finalized in 2006 and
published in the Federal Register in early 2007. In that guidance, we emphasized our concern
that some institutions’ strategic-and capital-planning processes did not adequately recognize
the risks arising from their CRE concentrations. We stated that institutions actively involved
in CRE lending should perform ongoing assessments to identify and manage concentrations
through stress testing and similar exercises were needed to identify the potential impact of
adverse market conditions on earnings and capital.
As weaker housing markets and deteriorating economic conditions have impaired the
quality of CRE loans at supervised banking organizations, the Federal Reserve has devoted
significantly more resources to assessing the quality of regulated institutions’ CRE portfolios.
These efforts include monitoring the impact of declining cash flows and collateral values, as
well as assessing the extent to which banks have been complying with the CRE guidance.
Reserve Banks that are located in more adversely affected geographic areas have been
particularly focused on evaluating exposures arising from CRE lending. We have found,
through horizontal reviews and other examination activities, that many institutions would
benefit from portfolio-level stress testing, improved management information systems, and
more robust appraisal practices. Additionally, some institutions need to improve their
understanding of how single-name, sectoral and geographic concentrations can impact capital
levels during downturns.
Prices of existing commercial properties have already declined substantially from the
peak in 2007 and will likely decline further. As job losses have accelerated, demand for
commercial property has declined and vacancy rates have increased. The higher vacancy
levels and significant decline in the value of existing properties have placed particularly
heavy pressure on construction and development projects that do not generate income until
after completion. Developers typically depend on the sales of completed projects to repay
their outstanding loans, and with prices depressed amid sluggish sales, many developers are
finding their ability to service existing construction loans strained.
As a result, Federal Reserve examiners are reporting a sharp deterioration in the credit
performance of loans in banks’ portfolios and loans in commercial mortgage-backed
securities (CMBS). At the end of the second quarter of 2009, approximately $3.5 trillion of
outstanding debt was associated with CRE, including loans for multifamily housing
developments. Of this, $1.7 trillion was held on the books of banks and thrifts, and an
additional $900 billion represented collateral for CMBS, with other investors holding the
remaining balance of $900 billion. Also at the end of the second quarter, about 9 percent of
CRE loans in bank portfolios were considered delinquent, almost double the level of a year
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Jon D. Greenlee
earlier.3 Loan performance problems were the most striking for construction and development
loans, especially for those that financed residential development. More than 16 percent of all
construction and development loans were considered delinquent at the end of the second
quarter.
Of particular concern, almost $500 billion of CRE loans will mature during each of the
next few years. In addition to losses caused by declining property cash flows and
deteriorating conditions for construction loans, losses will also be boosted by the depreciating
collateral value underlying those maturing loans. The losses will place continued pressure on
banks’ earnings, especially those of smaller regional and community banks that have high
concentrations of CRE loans.
The current fundamental weakness in CRE markets is exacerbated by the fact that the
CMBS market, which previously had financed about 30 percent of originations and
completed construction projects, has remained closed since the start of the crisis.
Delinquencies of mortgages backing CMBS have increased markedly in recent months.
Market participants anticipate these rates will climb higher by the end of this year, driven not
only by negative fundamentals but also by borrowers’ difficulty in rolling over maturing debt.
In addition, the decline in CMBS prices has generated significant stresses on the balance
sheets of financial institutions that must mark these securities to market, further limiting their
appetite for taking on new CRE exposure.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
FEDERAL RESERVE ACTIVITIES TO HELP
REVITALIZE CREDIT MARKETS
The Federal Reserve, along with other government agencies, has taken a number of
actions to strengthen the financial sector and to promote the availability of credit to
businesses and households. In addition to aggressively easing monetary policy, the Federal
Reserve has established a number of facilities to improve liquidity in financial markets. One
such program is the Term Asset-Backed Securities Loan Facility (TALF), which was
announced in November 2008 to facilitate the extension of credit to households and small
businesses.
Before the crisis, securitization markets were an important conduit of credit to the
household and business sectors; some have referred to these markets as the “shadow banking
system.” Securitization markets (other than those for mortgages guaranteed by the
government) closed in mid-2008, with most of the issuance since that time importantly
dependent on government support. Under the TALF, eligible investors may borrow to finance
purchases of the AAA-rated tranches of various classes of asset-backed securities. The
program originally focused on credit for households and small businesses, including auto
loans, credit card loans, student loans, and loans guaranteed by the Small Business
Administration. More recently, investors have also been able to use the TALF to purchase
both existing and newly issued CMBS, which were included to help mitigate the refinancing
problem in that sector.
3
The CRE loans considered delinquent on banks’ books were non-owner-occupied CRE loans that were 30 days or
more past due.
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The TALF has had some success in restarting securitization markets. Rate spreads for
asset-backed securities have declined substantially, and there is some new issuance that does
not depend on the facility. By improving credit market functioning and adding liquidity to the
system, the TALF and other programs have provided critical support to the financial system
and the economy.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
AVAILABILITY OF CREDIT
The Federal Reserve has long-standing policies in place to support sound lending and
credit intermediation. Guidance issued during the CRE downturn in 1991 and in effect today
instructs examiners to ensure that regulatory policies and actions do not inadvertently curtail
the availability of credit to sound borrowers.4 This guidance also states that examiners should
ensure that loans are being reviewed in a consistent, prudent, and balanced fashion to prevent
inappropriate downgrades of credits. It is consistent with guidance published in early 2007
that addressed risk management of CRE concentrations. The 2007 guidance states that
institutions that have experienced losses, hold less capital, and are operating in a more risksensitive environment are expected to employ appropriate risk-management practices to
ensure their viability.5
We are currently in the final stages of developing interagency guidance on CRE loan
restructurings and workouts. Banks have raised concerns that Federal Reserve examiners are
not always taking a balanced approach to the assessment of CRE loan restructurings. At the
same time, our examiners have observed incidents where banks have been slow to
acknowledge declines in CRE project cash flows and collateral values in their assessment of
potential loan repayment. This new guidance supports balanced and prudent decisionmaking
with respect to loan restructuring, accurate and timely recognition of losses, and appropriate
loan classification. The guidance reiterates that classification of a loan should not be based
solely on a decline in collateral value, in the absence of other adverse factors, and that loan
restructurings are often in the best interest of both the financial institution and the borrower.
The expectation is that banks should restructure CRE loans in a prudent manner, recognizing
the associated credit risk, and not simply renew a loan in an effort to delay loss recognition.
Prudent real estate lending depends upon reliable and timely information on the market
value of the real estate collateral. This has been a cornerstone of the regulatory requirements
for real estate lending and is reflected in the agencies’ appraisal regulations. In that regard,
the Federal Reserve requires its regulated institution to have real estate appraisals that meet
minimum appraisal standards, including the Uniform Standards of Professional Appraisal
Practice, and contain sufficient information to support the institution’s credit decision. Over
4
See Board of Governors of the Federal Reserve System, Division of Banking Supervision and Regulation (1991),
“Interagency Examination Guidance on Commercial Real Estate Loans,” Supervision and Regulation Letter
SR 91-24 (November 7), www.federalreserve.gov/BoardDocs/SRLetters/1991/SR9124.htm; and Office of the
Comptroller of the Currency, Federal Deposit Insurance Corporation, Federal Reserve Board, and Office of
Thrift Supervision (1991), “Interagency Policy Statement on the Review and Classification of Commercial
Real
Estate
Loans,”
joint
policy
statement,
November
7,www.federalreserve.gov/
BoardDocs/SRLetters/1991/SR9124a1.pdf.
5
See Board of Governors of the Federal Reserve System, Division of Banking Supervision and Regulation (2007),
“Interagency Guidance on Concentrations in Commercial Real Estate,” Supervision and Regulation Letter SR
07-1 (January 4), www.federalreserve.gov/boarddocs/srletters/2007/SR0701.htm.
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Jon D. Greenlee
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the past several years, the Federal Reserve has issued several appraisal-related guidance
documents to emphasize the importance of a bank’s appraisal function and the need for
independent and reliable appraisals. More recently, the Federal Reserve and the other federal
agencies issued a proposal to revise the Interagency Appraisal and Evaluation Guidelines,
which is expected to be finalized in the coming months. These guidelines reinforce the
importance of sound appraisal practices.
Given the lack of market sales in many markets and the predominant number of
distressed sales in the current environment, regulated institutions face significant challenges
today in assessing the value of real estate. We expect institutions to have policies and
procedures for obtaining new or updated appraisals as part of their ongoing credit review. An
institution should have appraisals or other market information that provide appropriate
analysis of the market value of the real estate collateral and reflect relevant market conditions,
the property’s current “as is” condition, and reasonable assumptions and conclusions. Bank
examiners generally will not challenge an institution’s appraisal and other collateral valuation
information that are based on well-supported analysis.
Guidance issued in November 2008 by the Federal Reserve and the other federal banking
agencies also encouraged banks to meet the needs of creditworthy borrowers in a manner
consistent with the principles of safety and soundness while taking a balanced approach in
assessing borrowers’ ability to repay and making realistic assessments of collateral
valuations.6 In addition, the Federal Reserve has directed examiners to be mindful of the
effects of excessive credit tightening in the broader economy, and we have implemented
training for examiners and outreach to the banking industry to underscore these intentions.
We are aware that bankers may become overly conservative in an attempt to ameliorate past
weaknesses in lending practices, and we are working to emphasize that it is in all parties’ best
interests to continue making loans to creditworthy borrowers.
CONCLUSION
While financial market conditions in the United States have improved notably over the
past year, the overall environment continues to be somewhat strained, and some geographic
areas like the Southeast are experiencing more difficultly than others. The Federal Reserve,
working with the other banking agencies has acted--and will continue to act--to ensure that
the banking system remains safe and sound and is able to meet the credit needs of our
economy. We have aggressively pursued monetary policy actions and have provided liquidity
to help repair the financial system. In our supervisory efforts, we are mindful of the riskmanagement deficiencies at banking institutions revealed by the financial crisis and are
ensuring that institutions develop appropriate corrective actions.
It will take some time for the banking industry to work through this current set of
challenges and for the financial markets to fully recover. In this environment, the economy
will need a strong and stable financial system that can make credit available. We want banks
6
See Board of Governors of the Federal Reserve System, FDIC, Office of the Comptroller of the Currency, and
Office of Thrift Supervision (2008), “Interagency Statement on Meeting the Needs of Creditworthy
Borrowers,”
joint
press
release,
November
12,
www.federalreserve.gov/newsevents
/press/bcreg/20081112a.htm.
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to deploy capital and liquidity, but in a responsible way that avoids past mistakes and does
not create new ones. The Federal Reserve is committed to working with other banking
agencies and the Congress to promote the concurrent goals of fostering credit availability and
a safe and sound banking system.
Thank you again for your invitation to discuss these important issues at today’s hearing. I
would be happy to answer any questions that you may have.
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In: Real Estate Investment Market
Editor: Sofia M. Lombardi, pp. 161-168
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 8
SHORT COMMUNICATION: DIVERSIFICATION IN
LISTED REAL
ESTATE INVESTMENT FUND REPORTING
IN SOUTH AFRICA
Valmond Ghyoot*
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
ABSTRACT
The study set out to determine the extent to which diversification, as promoted in the
financial literature, is actually implemented by institutional investors in South Africa.
Diversification theory is encapsulated in a conceptual model of potential diversification
strategies. The universe of listed real estate investment trusts in South Africa (Property Unit
Trusts and Property Loan Stock Companies) was evaluated in 2004 and the study was
updated in 2009. Content analysis was used to compare the conceptual model of potential
diversification strategies with the annual reports of the listed real estate investment funds. The
study finds that in 2004 few of the available diversification strategies were reported on. By
2009, reporting was more comprehensive. The study also explores focused strategies as an
alternative to diversification.
INTRODUCTION
Real estate portfolio diversification is a popular research area, judging by the regularity
of scientific articles on the topic. It is also one of the most urgent issues identified in surveys
about real estate investment research priorities among pension funds in the U.S.A. and in
Australia (Worzala, Gilliand and Gordon, 2002; Newell, Acheompong and Worzala, 2002).
*
Corresponding author: Email: vghyoot@gmail.com.
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162
Valmond Ghyoot
Several within-real estate diversification categories have been identified and shown to be
effective, based on such criteria as economic base, international differences, industry type,
tenant type and ownership vehicle. Yet, examination of the annual reports of Property Unit
Trust (PUT) or Property Loan Stock (PLS) companies1 will reveal that diversification is
usually reported on mainly in two categories: property sector (property type) and geographic
region. This narrow focus has been questioned before (Ori, 1995:27). More than two decades
ago, Hartzell, Heckman and Miles (1986:252) warned that “... current industry practice
represents little more than naive diversification. Due to the low levels of systematic risk,
current distinctions by region and property type make little sense in a world of costly
diversification.” They recommend that more diversification categories should be used in real
estate portfolio management.
This study examines the apparent discrepancy between real estate diversification
strategies in research and in practice, using the annual reports of listed real estate trusts as a
basis. Alternatives to diversification are also explored.2
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DIVERSIFICATION LOGIC
Within a real estate portfolio, diversification is especially important. Real estate assets are
not homogeneous and do not move as a group. Portfolios therefore have proportionally lower
systematic risk and higher unsystematic risk than stocks. This makes real estate
diversification more effective (Miles and McCue, 1984:66; Hartzell, Heckman and Miles,
1986:246). If a real estate portfolio is not diversified efficiently, the manager is accepting
unnecessary unsystematic risk.
Simply spreading an investment over many properties will remove some unsystematic
risk. Average portfolio variance, a common risk measure for real estate investment, decreases
rapidly as the number of properties increases from one to ten (Grissom, Kuhle and Walther,
1996:201). Accordingly, simply adding more properties will diversify a portfolio. This is
naive diversification (Markowitz, 1952). Beyond the initial gains, however, no more benefit is
derived simply by adding additional properties (Francis, 1993:595). The portfolio manager
has to use a more efficient approach. Much like analysis of variance in statistics, markets
have to be broken down into homogeneous segments that have high internal correlation and
low correlation with other segments (Francis, 1993:598-599; Lieblich, 1995:1021).
The efficient frontier of a portfolio of properties is defined by yield and risk. Any
characteristic of real estate that affects these parameters could thus be a source of
diversification. The bundle of rights theory is useful here, because an almost unlimited
number of partial interests (limited real rights) may be created in a parcel of real estate—for
example, a lease, or a financial interest such as a mortgage bond or a derivative. Every partial
interest could be a basis of diversification.
1
2
Property Unit Trusts and Property Loan Stock Companies are similar, but not identical to REITS. Negotiations
are under way to implement a REIT structure in South Africa.
This is an update of a previous study (Ghyoot, 2004 and 2006), which is available from the author
(vghyoot@gmail.com).
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163
Short Communication
DIVERSIFICATION CATEGORIES IN ANNUAL REPORTS
To determine which diversification categories are actually reported on by listed real
estate investment funds in South Africa, the annual reports of the universe of 21 listed funds
were examined for the initial study in 2004. This was updated by examining a systematic
sample of 11 out of the 18 listed fund reports in 2009. Exhibit 1 lists all the funds that existed
in 2004 and in 2009, and their type. For those funds that were included in the study, the date
of the annual report that was evaluated is given.
Exhibit 1. The universe of listed real estate funds in 2004 and 2009
Fund name
2004 study
Type
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Acucap
Alan Gray
PLS
PUT
Report
analysed
2003
2003
ApexHi
Atlas
Capital
Emira
PLS
PLS
PUT
PUT
2003
2003
2003
2003
Growthpoint
PLS
2003
Hyprop
Ifour
PLS
PLS
2003
2003
Martprop
Metboard
Octodec
Pangbourne
Paramount
Premium
Prima
Redefine
Resilient
PUT
PLS
PLS
PLS
PLS
PLS
PUT
PLS
PLS
2003
2003
2003
2004
2003
2003
2003
2003
2003
SA Retail
Spear head
Sycom
PLS
PLS
PUT
2003
2003
2004
Fund name
2009 study
Type
Acucap
PLS
Report
analysed
2008
Ambit
Apexhi
PLS
PLS
Not analysed
2008
Capital
Emira
Fountainhead
Growthpoint
Hospitality
Hyprop
PUT
PUT
PUT
PLS
PLS
PLS
2008
2008
2008
Not analysed
2008
Not analysed
Madison
PLS
Asset managers
Octodec
Pangbourne
PLS
PLS
Not analysed
2008
Premium
PLS
Not analysed
Redefine
Resilient
SA Corporate
PLS
PLS
PUT
2008
2008
2008
Sycom
Vukile
PUT
PLS
Not analysed
2009
Source: Author
The results of both analyses are given in Exhibit 2. The left column lists seventeen
potential real estate portfolio diversification categories identified by researchers.3 The middle
3
The list is based on Anon (2003); Cheng and Liang (2000); De Witt (1997); Del Casino (1995); Dohrmann
(1995:87); Eicholtz and Hoesli (1995); Grissom, Kuhle and Walther (1996); Hartzell, Heckman and Miles
(1986:230, 240, 250); Lee and Devaney (2004); Lieblich (1995:1021); Louargand (1992); Mueller and Laposa
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164
Valmond Ghyoot
column identifies those categories that were actually mentioned in annual reports in the 2004
study, or were implied by the context. The rightmost column lists the categories mentioned or
implied in the annual reports for the 2009 study.
Exhibit 2. Real estate portfolio diversification
categories in research and as reported in South Africa
Potential diversification
Category
Asset quantity
1. Number of properties
Location
2. Geographic region
3. Urban v suburban
4. International
5. Economic region
Asset type
6. Property sector (-type)
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
7. Industry type
Property characteristics
8. Life cycle
9. Property size
10. Building quality
11. Building type
Investment and finance
Investment vehicle
(ownership form)
13. Financing structure
14. Investment period
Tenants
15. Tenant mix
16. Lease expiry profile
17. Lease types
Mentioned or implied in annual report?
2004 study
2009 study
Implicit in all portfolios
Implicit in all portfolios
Standard category, always
mentioned
Standard category, always
mentioned
Implied in a few cases
Sometimes mentioned
Implied in a few cases
Standard category, always
mentioned
Standard category, always
mentioned
Often mentioned
Implied by institutional
investment
Always mentioned
Often mentioned
Mostly implied by nature
of the fund
Sometimes mentioned
Often mentioned
Sometimes mentioned or
implied
Often mentioned
Always mentioned
Always mentioned
Sometimes mentioned
Source: Author.
The table reveals that in 2004 few diversification categories were reported on, or implied
in annual reports. From the sample of reports analysed in 2009, it is clear that the number of
categories reported on has increased substantially. In 2004, two standard categories were
always mentioned: geographic region and property type. By 2009 these standard categories
are joined by three additional categories, which are always mentioned directly or implied by
the data provided: property size, tenant mix and lease expiry profile. International investments
(1995); Mueller and Ziering (1992); Newell and Keng (2003); Pagliari (1990); Peng, Hudson-Wilson and Capps
(2000:137); Pyhrr et al (1989:131,266); Seiler, Webb and Myer (1999); Viezer (2000:75); Wellner and Thomas
(2004:2); Ziering and Hess (1995) and Ziering and McIntosh (1999).
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Short Communication
165
are mentioned for the first time in 2009. Under the Collective Schemes Investment Control
Act, 2002, such investment is now permitted.
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FOCUS AS AN ALTERNATIVE TO DIVERSIFICATION
Diversification is expensive and difficult to implement. For every category invested in,
the fund needs specialist consultants and specialised information (Hartzell, Heckman and
Miles, 1986:246). Diversification is sometimes difficult to implement because a
diversification category may be targeted, but suitable property in that category may not be
available, or capital market conditions at the time may be unsuitable (Hudson-Wilson,
2000:212).
Specialisation per property type allows concentration of resources. John Rainier, CEO of
the now defunct Allan Gray Property Trust, holds the opinion that focused funds are more
predictable than diversified funds, a fact that helps investors. He states that a listed fund may
prefer to invest where there is profit to be made, rather than follow a restrictive strategy
(Anon 2001). This is supported by Hedander (2005: 87), who states that the potential cost of
management of a diversified firm is higher than a focused firm. Focus could increase
unsystematic risk, but in real estate this is also the reason why excess profits are possible. A
portfolio manager who understands the asset class and the specific submarket would be
unwise not to capitalise on market inefficiencies, even at some increased risk. King and
Young (1994: 6) suggest that Modern Portfolio Theory, on which the diversification principle
is based, does not apply in real estate markets. It is better for an investor to apply
underwriting principles (fundamental analysis) and investigate each individual investment
thoroughly. Such an approach would also favour a focused investment strategy.
Within the listed real estate fund industry in the United States, Capozza and Seguin
(1999) found that a focus on property type is associated with an increase in wealth. Similar
results were obtained in a Swedish study (Cronqvist, Högfeldt and Nilsson, 2001), who found
that diversified real estate companies have lower values than focused companies. Hedander
(2005: 88, 89) finds that diversified real estate investment firms are, on average, less
profitable than more focused firms. Australian Listed Property Trusts shifted from a
diversified to a more focused strategy between 1987 and 2004. This followed a general trend
in other industries to decrease diversification (Hedander, 2005: 85, 107). Finally, the large
funds often prefer that smaller funds be focused, to simplify their own portfolio balancing.
CONCLUSION
There has been a marked improvement in the number of diversification categories
reported on over the past five years. Judging by their annual reports, the portfolio managers of
listed real estate investment funds in South Africa are doing what is possible in terms of
diversification.
Evaluation of the annual reports has also provided an opportunity to observe other
matters. The reporting by most South African listed real estate funds at the time of the first
study was described as being of a poor standard (Van Rooyen, 2005:5). Standards have
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166
Valmond Ghyoot
improved dramatically between 2004 and 2009 and transparency has increased. For example,
apart from detailed statements on future strategy, reports typically reflect the vacancy levels
and average rentals achieved per sector and even per property.
Geographic region is still mentioned as diversification category in the annual reports of
listed companies. Effective diversification is essentially economic in nature and politically
defined geographic regions are not necessarily meaningful. Several authors stress economic
diversification and Mueller (1993:61) recommends completely dropping geographic region as
a category. The persistent use of geographic region in listed real estate annual reports is
questionable and should be modified. Goetzmann and Wachter (1995:271,299) warn that
even investing in two widely separated cities such as New York and Los Angeles will not
necessarily have a significant effect on diversification. The cities could be economic twins.
On the other hand, some cities that are located close together, differ economically. Investing
in these cities simplifies diversification and lessens the cost.
More research on focus as an investment strategy by portfolio managers is needed. A
combination of focus and diversification is evident in the strategy of the Hospitality Property
Fund. This fund invests only in the hospitality industry, yet its annual report for 2008 has one
of the best ratings for reporting on diversification. The fund’s managers seem to have
captured the best of both worlds.
Hopefully this study will stimulate even more detail about diversification strategies in
annual reports, and stimulate more research in this field.
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REFERENCES
Acucap Properties Limited. (2008). Annual report. Cape Town: Acucap.
Anon. (2001). Diversification vs. focused portfolios. SAPOA Online 6/13/2001.
www.sapoa.org.za.
Anon. (2003). International diversification in property securities by Japanese investors 19732001. www.econ.mq.edu.au/cjes/research/Weston_2003_3.
ApexHi Properties Limited. (2008). Annual report. Johannesburg: ApexHi.
Capital Property Fund. (2008). Annual report. Rivonia:Capital.
Capozza, D. & Seguin, P. (1999). Focus, transparency and value: the REIT evidence. Real
Estate Economics, 27(4), 54-62, quoted in Hedander (2005, 90).
Cheng, P. & Liang, Y. (2000). Optimal diversification: is it really worthwhile? Journal of
Real Estate Portfolio Management, 6(1), 7-16.
Cronqvist, H., Högfeldt, P. & Nilsson, M. (2001). Why agency costs explain diversification
discounts. Real Estate Economics, 29(1), 85-126, quoted in Hedander (2005, 90).
De Witt, P. M. (1997). Real estate diversification benefits. Journal of Real Estate Research,
14(1/2), 117-135.
Del Casino, J. D. (1995). Portfolio diversification considerations. In J. L. Pagliari, (ed.), The
Handbook of Real Estate Portfolio Management. (912-966). Chicago: Irwin.
Dohrmann, G. (1995). The evolution of institutional investment in real estate. In J. L.
Pagliari, (ed.), The Handbook of Real Estate Portfolio Management, (3-116). Chicago:
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Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 169-175
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 9
SHOULD BANKING POWERS EXPAND
INTO REAL ESTATE BROKERAGE
AND MANAGEMENT?
Walter W. Eubanks*
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
ABSTRACT
In late 2000, the Federal Reserve and the Treasury proposed to increase banking
powers. They proposed allowing banking companies to engage in real estate brokerage
and management, as activities that are financial in nature. The substantiative issues are
the respective nature of banking and of real estate activities and the potential impact on
consumers. Procedural questions involve the intent of Congress in P.L. 106-102, which
delegated authority to both agencies to issue new regulations. The reintroduced
Community Choice in Real Estate Act, H.R. 111/S. 413, 110th Congress, would
permanently remove these real estate activities from consideration under the
marketadaptive powers of the regulators. In the mean time, Treasury spending bills have
forestalled any such regulations for six fiscal years, most recently in P.L. 110-5.
SUMMARY
In late 2000, the Federal Reserve and the Treasury proposed to increase banking powers.
They proposed allowing banking companies to engage in real estate brokerage and
management, as activities that are financial in nature. The substantiative issues are the
respective nature of banking and of real estate activities and the potential impact on
consumers. Procedural questions involve the intent of Congress in P.L. 106-102, which
delegated authority to both agencies to issue new regulations. The reintroduced Community
Choice in Real Estate Act, H.R. 111/S. 413, 110th Congress, would permanently remove these
real estate activities from consideration under the market-adaptive powers of the regulators.
*
Email: weubanks@crs.loc.gov, 7-7840
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Walter W. Eubanks
In the mean time, Treasury spending bills have forestalled any such regulations for six fiscal
years, most recently in P.L. 110-5.
FRAMEWORK OF LEGISLATION AND REGULATION
The Gramm-Leach-Bliley Act (GLBA, P.L. 106-102)1 was landmark legislation that
allowed banking, securities, and insurance companies to operate in affiliation with each other
under the organizational form of financial holding companies (FHCs). GLBA also permitted
FHCs, like financial subsidiaries of banks (FSs), to engage in a variety of activities not
previously allowed to banks or companies owning banks.2 Under GLBA, the Federal Reserve
(Fed) and the Treasury Department, which contains the Office of the Comptroller of the
Currency (OCC), have authority to issue regulations expanding activities for FHCs and FSs,
respectively.
In GLBA, §103 requires that the Fed find that new activities for FHCs are financial in
nature, incidental to a financial activity, or, both “complementary” to a financial activity and
not posing a substantial risk to safety and soundness. §121 repeats the standard for the OCC
governing FSs. Congress crafted GLBA as a compromise to allow financial affiliations while
avoiding a general mixing of “banking” with “commerce.” It specifically excluded bank FSs
from underwriting insurance and from real estate investment and development, except as may
already have been authorized by other law.3
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PROPOSED BROKERAGE AND MANAGEMENT REGULATION
In December 2000, the Fed and the Treasury released a proposal to allow banking
companies into new real estate businesses, under §§103 and 121.4 Their proposal would allow
FHCs and FSs to enter real estate brokerage and property management, if these activities
could be considered financial in nature or incidental to a financial activity (not the less
exacting “complementary” test). “Brokerage” includes acting as an intermediary between
parties to a real estate transaction, listing and advertising real estate, soliciting sales,
negotiating terms, and handling closings. It is not purchase or sale of property as an owner,
and it requires state licensing and regulation. “Property management” includes soliciting
tenants, negotiating leases, servicing rents, maintaining security deposits, making operating
payments, and overseeing upkeep. Managers thus need not be owners, and banking firms
could not become owners of real estate through this proposal.
1
113 Stat. 1338-1481.
FHCs hold controlling stock interests in separately incorporated or chartered businesses, such as banks,
mortgagecompanies, stockbrokers and dealers, etc. The Federal Reserve supervises all FHCs, which are not
federally insured.FSs are businesses that banks themselves own. The bank regulators supervise FSs, which,
while not necessarilyfederally insured, are owned directly by insured banks. These structural differences are
important because GLBAallows more latitude for uninsured FHCs to operate in nontraditional lines of
business. FHCs are considered less likely than banks and bank subsidiaries to cause difficulties for the federal
support mechanisms for banks, especially deposit insurance funds, should they encounter losses.
3
113 Stat. 1373, 12 U.S.C. 24a.
4
Board of Governors of the Federal Reserve System and Department of the Treasury, “Bank Holding Companies
and Change in Bank Control,” Federal Register, vol. 66, no. 2, January 3, 2001, pp. 307-314.
2
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The Fed and the OCC historically disallowed real estate brokerage and property
management activities for their regulated institutions. The Office of Thrift Supervision (also
within the Treasury) does allow subsidiaries of federal savings associations to provide real
estate brokerage and property management services. About half the states seem to allow these
activities for the financial institutions that they charter and regulate; however, actual practice
of bank realty powers appears very rare.5 Conversely, real estate brokers and managers cannot
offer essential banking services—accepting deposits and making commercial loans—and are
not seeking to become bank-like. They do not want to form financial holding companies or
obtain bank charters, and especially seek to avoid becoming regulated by the Fed or other
banking agency.
Bankers (American Bankers Association, Financial Services Roundtable, and New York
Clearing House Association) requested this authority. In their view, it would allow financial
institutions to offer a fuller range of financial service, using many skills that banks already
have. They argue that these activities are financial in nature and would lower the costs of
realty transactions. Other supporters are the America’s Community Bankers, Consumer
Bankers Association, Independent Community Bankers of America, Realty Alliance, and
Real Estate Services Providers Council.
The National Association of Realtors (NAR) opposes the proposal, arguing that no law,
including GLBA, authorizes banking firms to provide real estate brokerage and property
management, which it argues are nonfinancial in nature. From its perspective, the proposal
would create anticompetitive and anticonsumer concentrations of power dominating the realty
industry and increasing costs to consumers. Other opposing entities are the Building Owners
and Managers Association, Consumers Union, Institute of Real Estate Management,
International Council of Shopping Centers, National Affordable Housing Management
Association, and National Association of Homebuilders.
Arguments Concerning the Nature of the Industries
Favoring the Proposal
(1) Banks, FHCs, and FSs already engage in a variety of other real estate activities:
financing, appraising, leasing, settling, escrowing, and investment advising.
(2) Agency services that FHCs and FSs provide in securities and insurance are similar to
those of real estate brokers and property managers.
(3) FHCs may act as “finders,” bringing together buyers and sellers of non-real-estate
assets generally. (Found parties must negotiate terms, including prices, for
themselves.)6
(4) Bankers already act as intermediaries in arranging commercial real estate equity
financing (transfer of title, control, and risk arrangements for projects) and often
finance the underlying projects.
5
Conference of State Bank Supervisors, “Real Estate Brokerage Chart,” available at http://www.csbs.org/
government/ legislative/realestate/re_chart.htm.
6
12 CFR 225.86(d).
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Walter W. Eubanks
(5) Several diversified financial companies provide realty services beyond their more
traditional banking, securities, and insurance services. Some realty-based companies
offer bank-like services, most visibly mortgages.
(6) Some savings associations and state-chartered banks already provide these real estate
services. Twenty-seven states and the federal Office of Thrift Supervision appear to
allow the activities at issue for deposit-based financial institutions, at least statutorily.
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Opposing the Proposal
(1) GLBA specifically prohibits FSs from engaging in real estate development and
investment. Thus, its intent may have been to restrain new realty powers of bankers.
(2) Real estate brokerage and property management are commercial activities. Their
necessary hands-on sales skills are far different from lending. When bankers
sponsored Real Estate Investment Trusts in the 1970s, most collapsed with large
losses.
(3) Real estate brokerage and property management involve negotiation of realty
transactions. That role has been forbidden to FHC s as “finders.” FHC finders may
not engage in any activity requiring registration or licensing as a realty agent or
broker.
(4) One study states that the real estate industry is highly competitive and efficient, much
more productive than financial services generally.7 If so, bankers would presumably
bring almost no net benefit to real estate brokerage and property management.
(5) Entry of deep-pocket banking companies, which benefit from federal assistance
including deposit insurance, might drive out brokers and property managers, which
typically operate on a much smaller scale.
(6) Competition for lending could decline if buyers believe that one-stop realty
transacting and financing would ease credit approval. Mortgage lenders not involved
with the brokerage part of realty transactions might lose business.
Arguments Concerning Customers (Consumers/Businesses)
Favoring the Proposal
(1) Customers could benefit from lower costs and greater convenience if one
organization provided most realty services bundled together. Transaction details
(paperwork) often overwhelm buyers and sellers of property. Consumers, including
buyers of these services, generally prefer more competitors in a field to fewer.8
(2) Clients of banks need not face complications of start-from-scratch checking of
creditworthiness, which their bankers already know. The credit approval/
7
8
A conclusion of a study by Leonard Zampano of the University of Alabama presented at the NAR Midyear
Legislative Meetings and Trade Expo, Washington, DC, May 17, 2001.
American Bankers Association, “Consumers Want More Real Estate Competition, New Survey Reveals,”
athttp://www.aba.com/Press+Room/051501realestate.htm.
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Should Banking Powers Expand into Real Estate Brokerage and Management?
173
underwriting process is the stage of real estate purchase that is usually the most
delayed.
(3) Laws against forcing customers to obtain both nonlending services and loans from
banking companies (which observers call “tying”) would still restrain market power
of companies providing banking and realty services jointly. Meanwhile, many real
estate brokers seem to have close ties with favorite mortgage lenders, title
companies, etc., making it easy for customers to deal with almost one-stop financial
shopping.
Opposing the Proposal
(1) Customers might believe that obtaining realty brokerage or property management
services from bankers would ease credit approval for their financing. Better,
unbundled deals may be available from competition among multiple providers.
(2) Customer service could suffer with fewer specialized providers. Bank credit
standards might not be appropriate for realty transactions requiring flexibility,
especially when tightening credit quality concerns (“credit crunches”) cut back bank
lending.
(3) Low- and moderate-income households lacking bank relationships might not benefit
from bundled realty services designed for bank clients of greater resources.
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DEVELOPMENTS AND LEGISLATION
The House Subcommittee on Commercial and Administrative Law held its Oversight
Hearing on Proposed Federal Reserve/Treasury Department Real Estate Brokerage and
Management Rule. The Senate Subcommittee on Financial Institutions held its hearing, Bank
and Financial Holding Company Engagement in Real Estate Brokerage and Property
Management, the House Subcommittee on Financial Institutions and Consumer Credit held a
hearing on H.R. 3424. The Community Choice in Real Estate Act of 2001 (which had a
Senate version, S. 1839) would continue to keep banks out of real estate management.
2003
Representative Calvert and Senator Allard reintroduced the Community Choice in Real
Estate Act, now numbered H.R. 111 and S. 98, to prohibit FHCs and national banks from
engaging, directly or indirectly, in real estate brokerage or real estate management activities.
Both measures were identical to their predecessors. The 108th Congress passed the basic
federal spending package, P.L. 108-7. It retained the prohibition amendment, disallowing any
funds for Treasury Department issuance of the bankers’ real estate regulation in FY2003.
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Walter W. Eubanks
2004
Representative Northup reintroduced the amendment into the Transportation
Appropriations bill H.R. 2989. The measure prohibited FY2004 funds from being used to
implement the proposed rule. The House approved that measure.9 Senate approval resulted in
P.L. 108-199, continuing no-spending language.
For the next fiscal year (FY2005), no-spending language reappeared as Section 523 of
H.R. 5025, the Transportation, Treasury, and Independent Agencies Appropriations Act. Its
ban on Treasury regulatory issuance via a spending cutoff was in the original measure, which
cleared the subcommittee. Stronger, permanent, prohibitory language was included in Section
217 of the counterpart S. 2806, which, if Congress had approved it, would have had the force
of law to prevent the proposed activity in the future. Following conference approval, the
FY2005 omnibus spending measure, P.L. 108-447, adopted the House version.10 The final
version of the Treasury appropriations language thus included the third moratorium, until the
end of FY2005.
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2005
Representative Calvert reintroduced the Community Choice in Real Estate Act, H.R. 111.
Senator Allard reintroduced its companion bill, S. 98. Conversely, Representative Oxley
introduced H.R. 2660, the Fair Choice and Competition in Real Estate Act of 2005, on May
26. It would amend the Bank Holding Company Act of 1956 (the foundation for GLBA) to
allow real estate brokerage activities and real estate management activities for financial
holding companies and financial subsidiaries of national banks.11 The House Committee on
Financial Services held a hearing, Protecting Consumers and Promoting Competition in Real
Estate, on June 15.12 In its first report on real estate brokerage, the Government
Accountability Office found that state-chartered bank activity (where permitted) had little
effect on competition or consumers.13
On December 5, the OCC relaxed prohibitions on bank investments in real estate
development projects. The agency wrote two interpretive letters allowing national banks to
develop a hotel and a mixed-use project. The Bank of America proposed to invest in a 150room hotel, and PNC sought to develop a facility with a hotel, retail office space, offices, and
condominiums. A third interpretive letter was written dated December 21, 2005, allowing
Union Bank of California to invest in a wind energy project in which the bank would own
70% of the project, including the land and wind turbines. The OCC defended its approval of
the December 5 interpretive letters, citing 12 U.S.C. § 29 that allows banks to invest in bank
premises. Among the justifications for approval of the wind project was that 12 U.S.C. § 29
provides that national banks may purchase, hold, and convey real estate and that this
acquisition of interests in real estate is not speculative. Those developments would appear
9
Division F, Title II, Section 538. Congressional Record, November 25, 2003, p. H12415.
Division H, Section 519, Congressional Record, November 20, 2004, p. H10358.
11
Karen L. Werner, “Reps. Oxley, Frank Introduce Measure To Allow Real Estate Brokerage for Banks,” Daily
Report for Executives, May 31, 2005 , p. A-11.
12
See http://financialservices.house.gov/hearings.asp?formmode=detail&hearing=395.
13
Real Estate Brokerage: Factors That May Affect Price Competition, GAO-05-947.
10
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Should Banking Powers Expand into Real Estate Brokerage and Management?
175
essentially to end the stricture against national bank ownership and leasing of real estate,
thereby moving further toward allowing bankers into real estate brokerage, etc.14
2006
In the FY2006 appropriations process for H.R. 3058, covering the Treasury, conferees
adopted House language prohibiting the Treasury from finalizing the contentious rule in
FY2006 (Section 718). Conferees rejected stronger language in the Senate version
(introduced in the form of an amendment)of the measure (Section 723) that might have
permanently prevented a decision on the issue, therefore issuance of any permissive
regulation. President Bush signed this measure into law (P.L. 109-115) on November 30,
2005.
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2007
The Community Choice in Real Estate Act of 2007 was reintroduced in both houses of
the 110th Congress as S. 413 by Senator Hillary Clinton and as H.R. 111 by Representative
Paul Kanjorski. Like the previous versions of these bills, these new proposals would amend
the Bank Holding Company Act of the United States to prohibit financial holding companies
and national banks from engaging, directly or indirectly, in real estate brokerage or real estate
management activities. In the mean time, the rule in FY2006 (Section 718) was continued
under the Revised Continuing Appropriations Resolution, 2007 (P.L. 110-5). In short, the
House appropriations bill, which for the past six years has included a one year prohibition on
funding the Treasury to complete the rulemaking that was authorized by the 1999 GrammLeach-Bliley Act was extended another year.
14
R. Christian Bruce, “OCC Defends Letter on Real Estate Powers While Realtors Call for Action From Congress,”
Daily Report for Executives, February 2, 2006, p. A-31.
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In: Real Estate Investment Market
Editors: Sofia M. Lombardi, pp. 177-180
ISBN: 978-1-61668-395-5
© 2010 Nova Science Publishers, Inc.
Chapter 10
EMERGING ECONOMIES AND SECONDARY
MORTGAGE MARKETS
Raymond T. Abdulai* and Frank Gyamfi-Yeboa
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
ABSTRACT
Access to long-term credit remains one of the major obstacles to solving the
perennial housing problems in many emerging economies. These countries have been
making serious attempts at developing their mortgage markets in recent times. There is a
general consensus on the need for emerging economies to develop housing finance
systems that would ensure easy, affordable and sustainable accessibility to credit. The
exact nature and the elements of such a system are still subject to debate. In this
commentary, we argue for the institution of secondary mortgage markets but recommend
the use of mortgage credit institutions in the short to medium term.
INTRODUCTION
The use of mortgage debt in financing home purchases is a common feature of most
advanced economies. The capital outlay required in home purchases is usually beyond the
amount a typical household can accumulate in savings over a period when the need to own a
home becomes most pressing. Most advanced economies have developed housing finance
systems that allow households to have access to a reliable and sustainable source of funding
for housing. One of such systems is the operation of a secondary mortgage market (SMM),
where existing mortgages are bought and sold. This market provides liquidity to banks and
other mortgage originators by allowing them to replenish funds and to help solve the maturity
mismatch problem that many originators face.
The secondary mortgage market has its origins in the US and has been in operation since
1938 when the Federal National Mortgage Association (FNMA or “Fannie Mae”) was
*
Corresponding author: Email: R.Abdulai@ljmu.ac.uk , Tel: +44 (0)151 321 2573
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Raymond T. Abdulai and Frank Gyamfi-Yeboa
established by an Act of the US Congress. The market has since evolved over the years in the
US and is currently dominated by Fannie Mae and Freddie Mac, the two major government
sponsored agencies (GSEs). The studies of Kolari, Fraser and Anari (1998), Todd (2000),
Ambrose, LaCour-Little and Sanders (2002) and Passmore et al. (2002) have identified
various benefits of a secondary mortgage market; these include reduction in mortgage costs
and easy access to credit. Jaffe and Renaud (1997) note that there are various forms of
secondary mortgage market systems, which are differentiated by the instrument used for the
mortgage sale and the type of investors or institutions who buy the mortgages. The two
prominent SMM systems are the use of mortgage credit institutions (MCIs) to provide longterm loans to depository institutions that hold mortgages and the use of mortgage
securitization. The MCIs raise funds from the capital markets and distribute the proceeds to
participating mortgage lenders. The services and products offered by MCIs provide liquidity
and help mortgage lenders to manage their assets and liabilities. An example of an MCI is the
Federal Home Loan Bank System in the US. Mortgage securitization, on the other hand,
involves the issuance of securities against a pool of mortgages. The securitization process
starts with the purchase of mortgage loans by either the GSEs or private market conduits such
as investment banks. The interest and principal receivable from the mortgage loans in a pool
are then packaged and sold to investors in the form of mortgage backed securities. As at the
end of 2008, about 60% of all home mortgages outstanding had been securitized in the US
(The Federal Reserve System, 2008). Thus, the use of mortgage securitization dominates the
US secondary mortgage market.
The recent turmoil in the financial markets occasioned by the housing crisis in the US
raises concerns about the dark side of an SMM, particularly, using mortgage securitization as
an instrument. It has been argued that low underwriting standards prevalent in subprime
lending, where loans are made to borrowers with weak credit, was facilitated by mortgage
securitization. Since mortgage originators did not have to keep mortgages on their books, they
had little incentive to carefully scrutinize borrowers. Passmore and Sparks (2000), for
instance, show that the use of automated underwriting and mortgage securitization tends to
lead to mortgage originators cherry picking and keeping mortgages of high credit quality
whilst passing on those with low quality to securitizers. The surge in subprime lending
activity in the early part of this decade was largely driven by the ease with which such
mortgages could be sold in the secondary mortgage market. It is now well established that the
recent downturn in the global economy was precipitated by increased delinquency on
mortgages, especially, subprime loans in the US.
Given the near collapse of the financial system and the global recession that resulted from
the excesses in the mortgage market, it is likely that policy makers and governments in
developing countries would be skeptical about the need to institute secondary mortgage
markets in their countries. The critical issues that most policy makers in developing
economies need to consider are: whether or not it is prudent to develop a secondary mortgage
market for a developing country in the first place, given the damage that excesses in this
market can bring to even the most advanced economies like the US; and the parameters on
which a good housing finance system should be based. These issues are important because
most emerging economies have been making serious attempts at developing their mortgage
markets over the past few decades and the decision to institute a secondary mortgage market
as part their housing finance system might be influenced by the recent happenings in the US
mortgage market.
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179
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PARAMETERS OF A GOOD HOUSING FINANCE SYSTEM
A good housing finance system should address three key issues. Firstly, it should
integrate the mortgage market and the broader financial market. The competition for capital
among different investments and users is often keen in most emerging economies. In any
fairly efficient and liberalized economic environment, capital will typically be allocated to
uses that promise the highest return on a risk-adjusted basis. It is important to stress that
mortgage debt is only one of many assets available to banks and other investors in debt
instruments. One way to ensure that the housing sector attracts adequate amount of capital is
to make mortgages part of the mainstream assets on the capital market by integrating the
mortgage and capital markets. A mortgage market that is segmented from the broader capital
markets could potentially constrain the supply of credit for home purchases. According to
Devaney and Pickerill (1990), McGarvey and Meador (1991) and Goebel and Ma (1993) the
introduction of a secondary mortgage market and deregulation of financial markets are
catalysts for integration; the authors have identified deregulation of financial markets to be
the dominant cause for integration. One could, therefore, argue that if the aim is to integrate
mortgage and capital markets, then emerging economies would only need to deregulate their
financial markets. Instituting a secondary mortgage market might only be considered as an
unnecessary complication. However, as we argue below, albeit deregulation of financial
markets is an important step towards integration, it is not sufficient to reduce or eliminate the
constraints on the supply of mortgage credit.
The second issue that a good housing finance system should address is to ensure that any
constraints on mortgage credit are eliminated or reduced. This issue is somewhat related to
the first except that as shown by Gyamfi-Yeboah and Ziobrowski (2009), the constraints on
mortgage credit could remain significant even when the mortgage and capital markets are
integrated. Using data from South Africa, the authors show that even though the South
African mortgage market was well integrated into the capital markets prior to the year 2000,
the constraints on mortgage credit persisted until the introduction of the secondary mortgage
market in 2001 when the constraints began to ease significantly. The implication of this
finding is that a formal mechanism is required to help channel funds to the housing sector. A
mechanism that could be used is mortgage securitization but given the problems it has created
for even an advanced economy like the US, alternative mechanisms such as the use of MCIs
may better suit the peculiar situations of emerging economies.
Lastly, a good housing finance system should ensure that there is a reduction in mortgage
costs to borrowers. The ultimate objective of any good housing finance system should not
only be to make funds available to households, but the costs of such funds must be
reasonable. Any efficiency that results from the institution of a formal housing finance system
must benefit households.
CONCLUSION
In this commentary, the possibility of instituting SMMs in developing economies is
examined. The excesses in the US mortgage market in the recent past caused in part by
mortgage securitization, raises legitimate questions on whether SMMs should play a
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180
Raymond T. Abdulai and Frank Gyamfi-Yeboa
significant role in the development of mortgage markets in emerging economies. In spite of
the role played by the secondary mortgage market in the recent housing crisis in the US, it
still remains a very essential tool that can be used to channel long-term funds to the housing
market. We argue that emerging economies would need to institute SMMs as part of a formal
housing finance system in order to ensure affordable and sustainable access to long-term
capital. The nature and form of the SMM systems that such economies institute should be
dictated by the peculiar needs of the country, the depth of its capital market and the lessons
from the experiences of other countries notably the US. It appears that the use of MCIs would
be the more feasible option for most developing countries given the complex nature of
mortgage securitization and the underdeveloped nature of the capital markets in these
economies.
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
REFERENCES
Ambrose, B. W., LaCour-Little, M. & Sanders, A. B. (2004). The Effect of Conforming Loan
Status on Mortgage Yield Spreads: A Loan Level Analysis. Real Estate Economics,
32(4), 541-69.
Devaney, M. & Pickerill, K. (1990). The Integration of Mortgage and Capital Markets. The
Appraisal Journal, January, 109-113.
Goebel, P. R. & Ma, C. K. (1993). The Integration of Mortgage Markets and Capital
Markets. Journal of the American Real Estate and Urban Economics Association, 21,
511-538.
Gyamfi-Yeboah, F. & Ziobrowski, A. J. (2009). The Integration of Mortgage and Capital
Markets in Emerging Economies – Evidence from South Africa. Journal of Real Estate
Finance and Economics, DOI 10.1007/s11146-009-9166-2.
Jaffe, D. M. & Renaud, B. (1997). Strategies to Develop Mortgage Markets in Transition
Economies. In J. Doukas, V. Murinde and C. Wihlborg (Eds.). Financial Sector Reform
and Privatization in Transition Economies, Amsterdam, Elsevier Science Publication.
Kolari, J. W., Fraser, D. R. & Anari, A. (1998). The Effect of Securitization on Mortgage
Market Yields: A Cointegration Analysis. Real Estate Economics 26(4), 677-93.
McGarvey, M., & Meador, M. (1991). Mortgage Credit Availability, Housing Starts and the
Integration of Mortgage and Capital Markets: New Evidence Using Linear Feedback
Journal of the American Real Estate and Urban Economics Association, 19, 25-40.
Passmore, W. & Sparks, R., (2000). Automated Underwriting and the Profitability of
Mortgage Securitization. Real Estate Economics, 28(2), 285-305
Passmore, W., Sparks, R. & Ingpen, J. (2002). GSEs, Mortgage Rates, and the Long-Run
Effects of Mortgage Securitization. Journal of Real Estate Finance and Economics,
25(3), 215-42
Todd, S. (2001). The Effects of Securitization on Consumer Mortgage Costs. Real Estate
Economics, 29(1), 29-54
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
CHAPTER SOURCES
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
The following chapters have been previously published:
Chapter 1 – This is an edited, excerpted and augmented edition of a United States
Congressional Budget Office testimony, given by Douglas W. Elmendorf, dated January 28,
2009.
Chapter 2 – This is an edited, excerpted and augmented edition of a National University
of Singapore Department of Real Estate Publication.
Chapter 3 – This is an edited, excerpted and augmented edition of a Liverpool John
Moores University and Wolverhampton University publication.
Chapter 4 – This is an edited, excerpted and augmented edition of a publication written
by Chihiro Shimizu, on July 31, 2009.
Chapter 5 – This is an edited, excerpted and augmented edition of a United States
Congressional Research Service publication, Report #RL34236, dated October 28, 2008.
Chapter 6 – This is an edited, excerpted and augmented edition of a United States
Congressional Research Service publication, Report #R40498, dated April 8, 2009.
Chapter 7 – This is an edited, excerpted and augmented edition of a testimony given
before Oversight and Government Reform Committee, dated November 2, 2009.
Chapter 8 – This is an edited, excerpted and augmented edition of a publication written
by Valmond Ghyoot, for the FPD Business School in South Africa.
Chapter 9 – This is an edited, excerpted and augmented edition of a United States
Congressional Research Service publication, Report #RS21104, dated April 24, 2007.
Chapter 10 –This is an edited, excerpted and augmented edition of a publication written
by Raymond T. Abduali and Frank Gyamfi-Yeboa.
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Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
INDEX
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
A
accessibility, xi, 177
accounting, ix, 8, 9, 11, 20, 27, 33, 34, 127, 129,
130, 133, 147, 149
accounting standards, 9, 33
adjustment, ix, 21, 105, 106, 107, 108, 115, 117,
118, 119, 121, 123, 133
Africa, x, 76, 80, 103, 161
age, ix, 105, 107, 108, 109, 115, 122
appraisals, 157, 158
arbitrage, 35, 100
Asia, viii, 31, 36
assessment, 11, 22, 82, 157
assets, vii, 1, 2, 5, 7, 8, 9, 11, 12, 13, 19, 20, 21, 22,
23, 24, 25, 26, 27, 29, 30, 33, 37, 107, 121, 123,
124, 128, 129, 142, 143, 146, 153, 154, 162, 171,
178, 179
assumptions, 56, 57, 117, 158
asymmetric information, 84, 99
asymmetry, 81, 83, 98, 99, 100
Australia, 36, 37, 76, 102, 161, 167
authority, x, 21, 25, 26, 27, 82, 128, 134, 141, 143,
145, 149, 151, 152, 169, 170, 171
authors, 42, 44, 166, 179
availability, vii, x, 1, 2, 6, 7, 13, 14, 18, 81, 86, 151,
153, 156, 157, 159
B
background, 107, 115
balance sheet, 6, 8, 9, 11, 12, 13, 17, 18, 24, 156
bank failure, 9, 152
bank ownership, 175
bankers, 158, 172, 173, 175
banking, x, 3, 8, 9, 10, 131, 151, 152, 153, 154, 155,
156, 158, 169, 170, 171, 172, 173
banking industry, 151, 158
bankruptcy, 8, 12, 18, 106
banks, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 22,
23, 24, 29, 144, 151, 152, 153, 154, 155, 156,
157, 158, 170, 171, 172, 173, 174, 175, 177, 179
basis points, 28, 129
behavior, 12, 34, 36
Beijing, 37, 68, 72
bond market, 84
bondholders, 136
bonds, 4, 5, 24, 131, 132, 152
borrowers, vii, 1, 2, 4, 5, 8, 9, 13, 15, 16, 17, 18, 76,
77, 78, 100, 132, 134, 135, 136, 153, 156, 157,
158, 178, 179
borrowing, vii, 1, 2, 4, 10, 14, 18, 24, 28, 30, 132,
134, 152
Britain, viii, 75, 76
business cycle, 76, 121
buyer, 14, 24, 25, 78, 79, 145
C
Canada, 76
capital markets, 14, 178, 179, 180
cash flow, ix, 21, 22, 32, 33, 34, 36, 105, 143, 155,
156, 157
causation, 79
central bank, 2, 9, 19, 27, 108, 121
certificates of deposit, 25
City, 68, 70, 106
civil servants, 22
classes, 132, 154, 156
classification, 34, 37, 157
coefficient of variation, 38, 49, 53, 64
collateral, 12, 20, 23, 24, 25, 29, 77, 78, 80, 131,
155, 156, 157, 158
commerce, 96, 170
commercial bank, 3, 5, 139, 151
commodity, 80, 98
common law, 80, 81
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Index
communication, 81, 148
community, 38, 153, 154, 155, 156, 168
comparative advantage, 32, 64
compensation, 17, 49, 52, 64
competing interests, 13
competition, 77, 173, 174, 179
complications, 172
concentration, 165
conceptual model, x, 161
concrete, 78, 123, 136
confidence, 3, 4, 5, 9, 78
Congress, 22, 127, 128, 159, 169, 170, 173, 174,
175, 178
Congressional Budget Office, 8, 20, 27, 28, 30, 181
consensus, xi, 37, 177
Constitution, 80
construction, ix, 2, 100, 105, 115, 122, 153, 154,
155, 156
consumer price index, 107
consumer surplus, 100
consumers, vii, x, 1, 13, 19, 23, 107, 151, 169, 171,
174
consumption, vii, 1, 49, 78, 82
control, x, 3, 22, 24, 106, 128, 137, 148, 171
correlation, 63, 121, 162
correlation coefficient, 121
costs, ix, 2, 8, 9, 14, 16, 17, 19, 20, 21, 22, 78, 79,
81, 88, 89, 93, 99, 100, 121, 122, 127, 134, 136,
166, 171, 172, 178, 179
covering, 175
credit, vii, x, xi, 1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 16, 20,
21, 22, 23, 24, 26, 27, 28, 30, 76, 78, 79, 98, 130,
132, 137, 142, 143, 151, 152, 153, 155, 156, 157,
158, 172, 173, 177, 178, 179
credit market, vii, 1, 2, 14, 98, 153, 157
credit rating, vii, 1, 4
creditors, 7, 12, 13, 18, 19, 76, 77, 78, 99
cumulative distribution function, 39
currency, 2, 9, 19, 27
D
database, 35, 81, 82, 117
debt, x, 3, 4, 6, 11, 12, 13, 14, 18, 20, 21, 24, 26, 29,
30, 128, 131, 137, 152, 153, 155, 156, 177, 179
debts, 3, 18, 23
decision-making process, 32
decisions, viii, 13, 75, 77, 78, 79, 82, 133, 143
defects, 99, 100
deficit, 20, 78
definition, 143, 148
delinquency, 2, 134, 153, 178
delivery, 102
demand curve, 100
deposit accounts, 3
deposits, 8, 19, 21, 23, 29, 170, 171
depreciation, 33, 108, 115, 122, 146
deregulation, 179
derivatives, 132, 133
developing countries, 76, 178, 180
developing nations, 76
differentiation, 106
directors, 144, 147
disclosure, 99, 140, 142, 143, 144
discounting, 22
discourse, 83
distortions, 33, 100
distribution, 39, 66, 95, 117, 118, 119
diversification, x, 132, 161, 162, 163, 164, 165, 166,
167, 168
dominance, viii, 31, 36, 38, 39, 47, 64
duration, 41, 119
E
earnings, 8, 19, 21, 29, 32, 33, 34, 35, 129, 130, 136,
154, 155, 156
East Asia, 52, 80
economic activity, vii, 1, 2, 6, 7
economic boom, 8
economic crisis, 52
economic cycle, ix, 106
economic development, viii, 75, 76, 77, 80, 81
economic downturn, 15, 154
economic efficiency, 15
economic growth, vii, 1, 8, 10, 76, 152
economic theory, 100
economics, 39
Education, 14, 65
employees, 22, 136
employment, 5, 96, 99, 153
energy, 12, 135, 174
environment, ix, 9, 106, 121, 123, 130, 152, 157,
158, 179
equality, 38, 51, 61
equilibrium, 52, 96
equity, 3, 6, 10, 11, 13, 16, 17, 28, 29, 33, 171
exercise, 36, 148
expenditures, 21, 99
expertise, 78
exposure, 19, 156
extrapolation, 32, 40, 53, 56, 61, 62, 63
F
failure, 4, 15, 140, 146
family income, 15
federal funds, 2, 3, 4, 23
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Index
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Federal Reserve Board, 157
finance, vii, viii, xi, 1, 12, 14, 15, 25, 31, 32, 34, 35,
37, 39, 56, 64, 132, 133, 156, 164, 171, 177, 178,
179, 180
financial crisis, 2, 4, 9, 21, 153, 158
financial distress, 2
financial institutions, vii, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11,
12, 19, 23, 25, 80, 81, 133, 151, 154, 156, 171,
172
financial markets, vii, x, 1, 2, 3, 4, 6, 7, 9, 10, 19, 24,
129, 131, 151, 152, 154, 156, 158, 178, 179
financial performance, 134
financial resources, 30, 78
financial sector, vii, 1, 6, 8, 9, 15, 16, 19, 136, 156
financial support, 4, 129, 131, 134
financial system, vii, ix, 1, 2, 7, 9, 10, 12, 16, 76,
106, 124, 127, 136, 151, 157, 158, 178
financing, 14, 132, 134, 171, 172, 173, 177
firms, 4, 6, 8, 11, 13, 15, 36, 154, 155, 165, 170, 171
focusing, 95, 113, 123
Ford, 53, 68, 135
forecasting, 37
foreclosure, ix, 2, 5, 15, 16, 18, 127, 128
fraud, 140, 145, 149
funding, 6, 13, 14, 15, 18, 19, 24, 25, 27, 29, 30, 154,
175, 177
funds, vii, x, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 15, 18, 19,
25, 28, 106, 107, 108, 121, 128, 131, 132, 137,
154, 161, 163, 165, 170, 173, 174, 177, 178, 179,
180
G
GDP, 8, 9, 76, 95
General Motors, 15
Generally Accepted Accounting Principles, 130
generation, ix, 105
Georgia, x, 151, 154
Germany, 9
global economy, 178
goals, 124, 135, 136, 159
goods and services, vii, 1, 117
government, iv, viii, ix, x, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 19, 20, 21, 22, 26, 29, 76, 79, 82,
83, 84, 93, 95, 100, 127, 128, 131, 134, 136, 137,
139, 140, 141, 154, 156, 171, 178
government intervention, 83, 100
government securities, 13
Great Depression, 9
gross domestic product, 8
gross national product, 9
growth, vii, viii, 5, 6, 9, 10, 31, 32, 33, 34, 35, 36,
37, 38, 40, 42, 44, 45, 49, 52, 53, 56, 57, 60, 61,
62, 63, 64, 130, 134, 135, 154
growth rate, 32, 34, 40, 53, 57, 60, 61, 62, 63, 134
guidance, 7, 143, 152, 155, 157, 158
guidelines, 158
H
holding company, 151
homeowners, ix, 13, 18, 127, 128, 132, 134, 135
Hong Kong, 37, 56
House, 16, 17, 66, 108, 109, 114, 117, 125, 171, 173,
174, 175
households, vii, 1, 2, 13, 152, 156, 173, 177, 179
housing, vii, ix, x, xi, 1, 2, 14, 15, 17, 20, 21, 26, 28,
76, 105, 106, 107, 108, 109, 112, 115, 117, 118,
119, 120, 121, 122, 123, 124, 128, 132, 136, 153,
154, 155, 177, 178, 179, 180
hypothesis, 33, 35, 36
I
ideal, 33, 106
images, 115
IMF, 9, 124
impairments, 5
implementation, viii, 75, 83
incentives, 7, 16, 18, 36, 99
income, 2, 17, 19, 30, 32, 36, 40, 53, 56, 57, 60, 61,
78, 95, 121, 129, 135, 136, 144, 155, 173
indicators, 33, 34
indices, 109, 111, 113, 115
induced bias, 35
industrial sectors, 37
industry, x, 139, 152, 162, 165, 166, 167, 171, 172
inefficiencies, 165
inefficiency, 33, 99, 101
inertia, viii, 31, 32, 63
inflation, ix, 105, 106, 107, 108, 121, 124
inflation target, 108, 121
information sharing, 81
inspections, 78, 151
institutions, vii, xi, 1, 3, 6, 7, 8, 11, 12, 19, 21, 23,
25, 29, 30, 80, 81, 98, 133, 144, 151, 152, 153,
154, 155, 157, 158, 171, 177, 178
instruments, 19, 24, 82, 179
insurance, 3, 11, 12, 21, 24, 29, 80, 132, 144, 145,
152, 170, 171, 172
integration, 179
intentions, 158
interbank market, 3
interest rates, x, 3, 4, 5, 8, 9, 17, 18, 108, 121, 128,
129, 132, 133, 136
intermediaries, 79, 171
internal controls, 133
International Monetary Fund, 9
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
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Index
investment bank, 3, 4, 35, 178
investors, x, 6, 11, 12, 16, 17, 25, 28, 32, 33, 35, 36,
37, 38, 39, 40, 56, 61, 64, 77, 78, 107, 115, 124,
128, 132, 134, 135, 136, 141, 143, 144, 145, 146,
155, 156, 161, 165, 166, 178, 179
Italy, 167, 168
J
Japan, viii, 8, 9, 65, 105, 106, 108, 109, 117, 119,
123, 124, 125
Jordan, x, 151
judges, 18
judgment, 22, 146
justification, 131
K
Korea, 9
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
L
labour, 89, 93
lack of confidence, 8, 131
land, viii, 75, 77, 79, 80, 81, 82, 83, 85, 86, 88, 89,
95, 96, 98, 99, 100, 102, 103, 111, 125, 153, 154,
174
Land Use Policy, 102
language, 174, 175
laws, 18, 77, 82, 117, 131, 140
lawyers, 13, 78
legislation, 13, 21, 80, 128, 170
lending, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 19, 20, 26,
27, 30, 136, 153, 154, 155, 157, 158, 172, 173,
178
likelihood, 7, 18, 24
limitation, 88
limited liability, 3, 23
line, 3, 88
liquid assets, 3
liquidity, x, 2, 3, 9, 13, 19, 21, 25, 129, 133, 151,
156, 157, 158, 159, 167, 177, 178
litigation, 78, 79
loans, iv, vii, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 30, 76, 80,
98, 128, 129, 131, 132, 135, 136, 137, 139, 146,
152, 153, 154, 155, 156, 157, 158, 171, 173, 178
local government, 30
M
management, viii, ix, x, 8, 9, 11, 12, 22, 32, 75, 83,
103, 107, 117, 119, 121, 123, 127, 129, 130, 133,
143, 152, 155, 157, 158, 165, 169, 170, 171, 172,
173, 174, 175
marginal utility, 38, 49
market segment, 125
markets, vii, viii, ix, xi, 1, 2, 3, 4, 6, 9, 13, 15, 18, 19,
20, 21, 32, 36, 37, 38, 75, 76, 77, 81, 96, 105,
106, 125, 128, 129, 154, 155, 156, 157, 158, 162,
165, 177, 178, 179, 180
marriage, ix, 106, 121, 123
measures, 8, 9, 33, 34, 38, 41, 49, 53, 101, 173
media, 32, 35, 132
Miami, 69, 70, 72
mixing, 170
model, x, 33, 39, 40, 53, 61, 62, 63, 83, 86, 87, 88,
161
models, 34, 66, 67, 81, 110
modernization, 82
monetary policy, 4, 156, 158
money, vii, 1, 2, 4, 7, 10, 11, 12, 17, 25, 29, 35, 77,
78, 85, 99, 130, 132
money markets, 25
moral hazard, 7, 81
moratorium, 174
mortgage-backed securities, vii, x, 3, 14, 16, 17, 19,
20, 24, 26, 28, 128, 132, 135, 137, 139, 155
Mozambique, 80
multiples, 34
multiplier, 32
N
Namibia, 80
nation, 1, 80, 81, 136, 153
negative equity, 18
Netherlands, 102
New South Wales, 59, 72
New Zealand, 36, 37, 76
Nigeria, 80, 102
null hypothesis, 38, 52, 64
O
observations, 41, 84
OECD, 124
Office of Management and Budget, 21, 22
Oklahoma, 70
omission, 145, 146, 147, 148
order, viii, 45, 75, 76, 82, 84, 89, 141, 142, 143, 144,
145, 146, 148, 149, 180
ownership, viii, 3, 6, 10, 12, 75, 78, 79, 80, 81, 82,
83, 84, 85, 101, 148, 162, 164
P
Pacific, viii, 31, 52, 65, 167
parameters, 162, 178
participant observation, 84
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Index
pensions, 107, 121
performance indicator, 49
Perth, 68
planning, 77, 155
plausibility, 56
PLS, 162, 163
policy makers, 178
policy responses, 19
poor, 34, 96, 154, 165
portfolio, x, 3, 24, 34, 37, 38, 39, 42, 44, 45, 52, 53,
56, 60, 61, 62, 63, 64, 107, 123, 128, 129, 130,
132, 133, 134, 135, 136, 137, 155, 161, 162, 163,
164, 165, 166, 167, 168
portfolio investment, 45
portfolio management, 162, 167
portfolios, ix, x, 12, 32, 34, 37, 38, 40, 45, 47, 52,
53, 56, 57, 61, 63, 64, 127, 128, 129, 130, 131,
132, 133, 134, 135, 136, 137, 154, 155, 164, 166,
167
positive relation, 33
positive relationship, 33
poverty, viii, 75, 76
poverty alleviation, viii, 75, 76
power, 110, 141, 149, 171, 173
prediction, 107, 125
preference, 78, 84, 132
present value, 21, 29
pressure, 155, 156
price changes, 117
price elasticity, 107
price index, 109, 110, 111, 112
prices, ix, 2, 5, 12, 13, 14, 15, 16, 17, 18, 32, 33, 34,
76, 82, 96, 99, 107, 109, 112, 127, 128, 132, 153,
155, 156, 171
private sector, 7, 15, 18
probability, ix, 39, 45, 105, 106, 117, 118, 120, 123,
135
probability density function, 39
production, 153
productivity, 15
profit, 7, 19, 135, 136, 165
profitability, 32, 34, 106, 134, 136
profits, x, 8, 12, 13, 15, 23, 34, 77, 99, 128, 165
program, 3, 10, 13, 14, 17, 18, 21, 24, 25, 27, 29, 30,
156
property rights, 77, 78, 101
public capital, 11
public goods, 77
public interest, 141, 145
public policy, 80
public service, 85, 95
Q
quantitative research, viii, 75, 84
R
random walk, 41
range, 2, 16, 19, 89, 117, 152, 171
ratings, 4, 142, 152, 166
real estate, vii, viii, x, 5, 8, 9, 12, 31, 32, 36, 37, 52,
56, 64, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
88, 93, 95, 96, 98, 99, 100, 101, 107, 119, 123,
124, 151, 153, 154, 157, 158, 161, 162, 163, 165,
166, 167, 168, 169, 170, 171, 172, 173, 174, 175
real terms, 95
reality, 61, 83, 85, 89
reason, 10, 83, 109, 136, 165
recession, 2, 4, 5, 9, 10, 11, 15, 178
recognition, 80, 82, 88, 157
reconstruction, 124
recovery, 2, 6, 7, 8, 9, 10
region, 83, 95, 154, 162, 164, 166, 167
Registry, 80, 82, 83, 86, 88, 91, 96, 97, 102
regression, 34, 86, 87, 108, 109, 113, 115
regression analysis, 86, 108, 109, 113, 115
regression method, 34
regulation, vii, ix, 8, 127, 134, 141, 143, 145, 152,
170, 173, 175
regulations, x, 141, 143, 149, 152, 157, 169, 170
regulators, xi, 8, 128, 169, 170
regulatory requirements, 157
relationship, 34, 40, 87, 108, 112, 136
rent, ix, 99, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 117, 118, 119, 120, 121, 122, 123
repair, 121, 122, 158
reserves, 2, 19, 154
resources, 7, 15, 76, 88, 93, 98, 99, 100, 133, 155,
165, 173
retail, viii, 31, 36, 37, 42, 44, 52, 53, 60, 61, 62, 63,
64, 72, 174
returns, viii, 16, 17, 31, 33, 34, 35, 36, 37, 38, 41, 42,
44, 52, 53, 56, 63, 64, 77, 98, 99, 167, 168
revenue, ix, 95, 105, 106, 107, 109, 121, 123, 124
risk, vii, ix, 1, 2, 3, 5, 11, 12, 18, 20, 21, 22, 27, 34,
35, 38, 39, 40, 45, 49, 52, 53, 64, 78, 84, 105,
106, 107, 118, 122, 123, 129, 131, 132, 133, 135,
136, 137, 152, 157, 158, 162, 165, 167, 168, 170,
171, 179
risk management, 133, 152, 157, 167
S
safety, x, 151, 152, 154, 158, 170
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
188
Index
sales, 2, 34, 77, 80, 107, 109, 141, 143, 144, 153,
155, 158, 170, 172
savannah, 95
savings, 8, 9, 76, 99, 133, 139, 171, 172, 177
savings rate, 76
search, 78, 82, 85
securities, x, 3, 5, 8, 13, 14, 16, 18, 19, 20, 22, 24,
25, 26, 28, 29, 33, 139, 140, 141, 142, 143, 144,
145, 146, 148, 149, 152, 156, 157, 166, 170, 171,
172, 178
Securities Exchange Act, 140
security, 28, 33, 79, 140, 141, 142, 143, 145, 146,
148, 170
selecting, 37
self-interest, 35
seller, 25, 78, 148
Senate, 131, 133, 173, 174, 175
Senate approval, 174
shape, 10
shaping, 76
shareholders, 6, 11
shares, 3, 10, 28, 29, 146
side effects, 77
Singapore, 31, 37, 69, 72, 181
skills, 78, 171, 172
skin, 82
social costs, 99, 100
sole proprietor, 23
solvency, 3, 6, 8, 11, 16
South Africa, v, x, 80, 161, 162, 163, 164, 165, 167,
168, 179, 180, 181
space, 110, 111, 174
speed, 85, 101
spillover effects, 16
Spring, 65
stability, 124, 151, 154
standard deviation, 38, 49, 53
standards, x, 4, 5, 136, 146, 151, 152, 153, 154, 157,
173, 178
statistics, 39, 52, 85, 162
stock, 3, 6, 8, 22, 28, 29, 32, 33, 34, 36, 38, 52, 77,
84, 106, 129, 130, 131, 132, 143, 148, 170
stock price, 22, 106
strategies, x, 7, 16, 32, 33, 34, 35, 38, 49, 53, 64, 66,
161, 162, 166, 167, 168
stress, 152, 154, 155, 166, 179
structural changes, 125
subprime loans, 5, 178
sub-Saharan Africa, vii, 76, 101, 102
subsidy, 11, 12, 18, 20, 21, 22, 27, 28
superiority, vii, viii, 31, 32, 35, 37, 38, 40, 42, 44,
47, 49, 56, 64
supervision, 152, 155
supervisor, 151, 152
supply, 2, 6, 7, 11, 14, 99, 100, 152, 179
supply curve, 99, 100
Supreme Court, 18, 149
surplus, 100
sustainability, 32, 64
systemic risk, 129
T
takeover, 20, 23
Tanzania, 80
targets, 16
tax credit, 27
taxation, 81
tenants, 106, 115, 116, 119, 122, 123, 170
threat, vii, 1, 151
threshold, 100
threshold level, 100
thrifts, 8, 155
time periods, 118
time series, 41
timing, 111, 117, 133
Title I, 128, 174
Title II, 174
total costs, 93
total revenue, 106, 123
trade, 13, 25, 77, 79
trading, 35, 80, 140
transaction costs, 77
transactions, 19, 20, 22, 37, 76, 78, 79, 80, 81, 83,
85, 95, 100, 108, 111, 133, 143, 145, 151, 171,
172, 173
Treasury bills, 4
trust, viii, 79, 105, 136, 144, 147
turnover, ix, 106, 120, 121, 123
U
U.S. Treasury, 131
UK, 66, 81, 101, 102, 109
UN, 103
uncertainty, 6, 7, 12, 106, 154
unemployment, 152
unemployment rate, 152
United Kingdom, 80
United Nations, 95, 103
United States, 8, 9, 10, 135, 154, 158, 165, 175, 181
universe, x, 161, 163
urban areas, 84
V
Valencia, 9
variables, 33, 86, 87
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,
189
Index
variance, 38, 39, 40, 41, 45, 46, 51, 52, 63, 64, 162
volatility, 22, 53
W
Z
Zimbabwe, 80
Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.
war, 34, 109, 110, 113, 117, 121
warrants, 22, 28, 129, 131
weakness, 76, 154, 156
wealth, vii, viii, 1, 6, 38, 39, 75, 76, 81, 165
West Africa, 95
wind turbines, 174
workers, vii, 1, 7, 15
World Bank, 79, 80, 84, 85, 95, 101, 102, 103
Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,