DETERMINANTS OF PERCENTAGE SHARE OF SHADOW BANKING
TO TOTAL ASSETS OF SELECTED PHILIPPINE REAL ESTATE
DEVELOPERS
A Thesis
Presented to the
Faculty of the School of Economics
University of Asia and the Pacific
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science in Industrial Economics
By
Rosemary T. Sia
May 2015
TABLE OF CONTENTS
List of Tables
List of Figures
Acknowledgements
Executive Summary
Page
iii
iii
iv
vi
CHAPTER
I
II
III
IV
V
INTRODUCTION
A. Background of the Study
B. Statement of the Problem
C. Objectives of the Study
D. Significance of the Study
E. Scope and Limitations
F. Definition of Terms
REVIEW OF RELATED LITERATURE
A. What is Shadow Banking?
B. Shadow Banking Growth Factors
C. Size of Shadow Banking
D. Entities and Activities of Shadow Banking
E. Real Estate Adequate Financing
F. In – house Financing as a Quick Path to Ownership
G. Factors and Theories That Affect Shadow Banking
Activities in the Real Estate
1
4
4
5
5
6
8
10
12
14
18
19
21
CONCEPTUAL FRAMEWORK AND METHODOLOGY
A. Theoretical Framework
B. Empirical Methodology
31
36
PRESENTATION, INTERPRETATION, AND ANALYSIS
OF DATA
A. Breakdown of Shadow Banking Activities
B. Regression Analysis
41
45
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
A. Summary of Results
53
B. Conclusions
54
C. Recommendations
54
BIBLIOGRAPHY
57
i
APPENDIX
A.
B.
C.
D.
E.
F.
G.
H.
I.
J.
K.
List of Observed Companies
Breakdown of Supplier Financing
Breakdown of In – house Financing
Breakdown of Private Placement Financing
Total Supplier Financing
Total In-house Financing
Total Private Placement Financing
Total Assets
Total Profit
Panel Regression Data
Results of Regression on %Shadow Financing
With White Cross-Sectional Test
61
62
64
66
68
69
70
70
71
73
79
ii
LIST OF TABLES
Table
Page
1
Factors that Affect Shadow Banking in the Real
Estate Sector
42
2
3
Estimated Shadow Financing Function
Regression Results
53
55
LIST OF FIGURES
Figure
1
2
3
4
5
6
7
8
9
10
The Philippine Financial System
Shadow Bank Liabilities versus Traditional
Bank Liabilities
Shadow Banking in Emerging Markets
Total Supplier Financing
Total In – house Financing
Total Private Placement Financing
Breakdown of Shadow banking growth
Total Assets of Selected Real Estate Developers
in the Philippines
Total Profit of Selected Real Estate Developers
in the Philippines
Percentage Share of Shadow banking to Total Assets
Page
9
19
20
46
iii
ACKNOWLEDGEMENTS
“Good things come to those who believe, better things come to those who are
patient, and the best things come to those who don’t give up.”
It’s been quite a journey. Writing this thesis would not have been possible
without the people who pushed me further. There were days when frustrations and
doubts settled in, and the temptation of giving up was always present. But I’m
truly grateful to the people who took part in the completion of this thesis, this
would have been impossible to fulfill without all of them.
To my thesis adviser, Dr. Stan, your good guidance, along with your
comments and insights has definitely been helpful, I would not have been able to
complete this study without you, thank you for your patience and dedication.
Despite the many meetings that you have within and outside the university, you
still managed to read through my work and set all the consultations with me. For
that I thank you. Thank you also for sharing your knowledge all throughout my
thesis work.
To my internal reader, Sir Greg, you also always made such effort in reading
my drafts, putting comments and suggestions, and answering the many questions
that I had while writing my thesis, thank you for your kindness and great effort.
To my external reader, Sir Manny, thank you for your time in reading through
my work and in being one of the panellists for my final defense. Your comments
were certainly helpful, thank you!
To my dearest friends – Keng, Keren, Thea, Althea, Tin, Julian and Teddy, you
were there in happiest of times and even in those emotional moments that I’ve
iv
been through. Thank you for your prayers and support. Thank you for being such
great friends, I know I could always count on you.
To the IEP 5th years - Althea, Keren, Keng, Chela, Joe, Apple, Sarmie, Ivy,
Lyndon, German, Mon, Francis, Jose, Rey, Mar, Rige, Raf, and Rap, my journey
in taking the Masters would never be complete without you. For all the trials and
challenges that we’ve been through together as a batch, I must say it was
definitely worth spending it with all of you, and I wouldn’t have it any other way.
To my mentors and professors, I will never forget your teachings and words of
wisdom, you always inspire me to strive for excellence, thank you!
To my parents, Richard and Norma, I’m blessed to have you both as my Dad and
Mom. You have raised your daughter well. Thank you for all the encouraging
words and motivation, I deeply appreciate all the love and support.
To my older brothers, Paul, Stephen and Michael, I’m blessed to have such
amazing “ahias”, thank you for the love!
Above all, I offer up this achievement to the One true, loving and faithful God,
Who continued to sustain me all throughout the many battles that I have faced.
Thank you, LORD for despite the many trials and painful experiences, it was in
those moments that you have molded me into what I have become today - a
stronger, more disciplined and mature person.
v
EXECUTIVE SUMMARY
This study aims to examine the growth of shadow banking activities of
listed real estate developers in the Philippines, as well as the main factors behind
shadow banking growth. The observation covers a period of ten years from 2004
to 2013, and includes selected real estate developers in the country. The type of
shadow banking activities that were explored are: supplier financing, in – house
financing and private placement financing. By gathering annual data from
consolidated financial statements of each company, as stated in the liabilities
section, it is confirmed that there has been a tremendous rise of shadow banking
activities within the sector.
The panel regression results reveal the main determinants of shadow
banking growth in this sector. Company variables such as the lagged percentage
share of shadow banking over total assets and profitability had a positive
correlation with the proportion of shadow banking over total assets. While
informal interest rates and company size based on assets had a negative
correlation with the proportion of shadow banking over total assets. Moreover,
external factors such as land values have a negative impact on shadow banking,
while bank loans have a positive effect. Other observed macro variables such as
OFW remittances, exchange rates and gross domestic savings were also
considered and tested, but were dropped as they were proven to be statistically
insignificant. The model only determines the statistical significance of these
variables in relation to the dependent variable that is the percentage share of
shadow banking to total assets of selected real estate companies.
vi
vii
CHAPTER I
INTRODUCTION
A. Background of the Study
Shadow banking has been broadly defined as credit intermediation
outside the conventional banking system, and constitutes around one-fourth or 25%
of the total financial intermediation worldwide (IMF, 2014). The term “shadow
bank” was first coined by economist Paul McCulley in a 2007 speech at the
annual financial symposium hosted by the Kansas City Federal Reserve Bank in
Jackson Hole, Wyoming (Kodres, 2013). As mentioned in his talk, shadow banks
were centered in the US and mainly referred to non-bank financial institutions that
engaged in maturity transformation. Commercial banks engage in maturity
transformation when deposits, which are normally short term, are used to fund
loans that are long term. Shadow banks do relatively the same – that is to borrow
short term funds in the money markets and use these funds to buy assets with long
term maturities. However, since shadow banks are not subject to traditional
banking regulations, they cannot borrow from the Federal Reserve and do not
have traditional depositors whose funds are covered by insurance; they mainly lie
in the “shadows”.
Shadow credit intermediation can be beneficial in its capacity to provide
alternative financing to the real economy, however such activities can also
become a source of systemic risk. During the 2008 global financial crisis, shadow
banking contributed to significant levels of risks in the financial system due to the
lack of adequate regulation. While regulated banks played an important role in the
1
securitization of assets, a large share of the ultimate financing of securitized assets
were provided by the shadow banking system. In advanced economies, shadow
intermediaries such as money market mutual funds (MMFs) and securitization
vehicles were highly leveraged and were vulnerable to runs when investors
withdrew large amounts of funds at short notice. This led to the fire sales of assets,
which further intensified financial risks by reducing asset values and spread the
stress to traditional banks. This eventually led global regulatory reforms to initiate
greater disclosure of asset valuations, improved governance and stricter oversight
and regulation of shadow banks.
In the Philippines, shadow banking activities are not as high compared to
advanced economies. The Philippine Financial system is broadly monitored by
different regulatory agencies in the country (Figure 1). The Bangko Sentral ng
Pilipinas (BSP) generally regulates the country’s banking institutions through the
effective use of monetary policy instruments. Banking institutions consist of
commercial banks, thrift banks, rural banks, cooperative rural banks and
specialized government banks. The banking system accounts for around 80% of
total assets in the entire financial system – all supervised by the BSP. On the
contrary, non-bank institutions such as investment houses, financing companies,
SEC dealers, fund managers, lending investors, pawnshops, government non-bank
financial institutions (NBFIs) and venture capital corporations are institutions that
the BSP has no direct control but are still under its supervision. With the growth
of the economy in the 1960s, the system became complicated as more entities
registered with specific government agencies participated in the credit markets
2
without access to BSP regulation. The completely unregulated sector is
considered as the shadow banking segment or the informal credit markets.
Figure 1. The Philippine Financial System
Universal and Commercial
Banks (90%)
Banking
Institutions
Bangko Sentral
ng Pilipinas
Insurance
Commission
Non-Banking
Financial
Intermediaries
Thrift Banks (7.5%)
Rural and
Cooperative Banks
(2.5%)
Private
Philippine
Financial System
Govt
Completely
Unregulated
Financial Sector
Bureau of Cooperative
Development
Cooperative
Credit Unions
Source: Philippine Institute for Development Studies; Philippines Country Monitor
Estimating the size of shadow banking has always been difficult because
many its entities do not report to government regulators. Of all the Southeast
Asian Nations, Malaysia has the most information regarding NBFI’s, however, it
still remains spotty. In Malaysia, NBFIs accounted for 20% of total credit
provided to households and grew much faster than the banks. Furthermore, based
on a World Bank study, NBFIs accounted for 37% of financial assets in the
Philippines, 35% in Thailand, and 20% in Indonesia (FSB, 2014). The shadow
banking system in emerging markets, such as the Philippines, is simpler than
those in advanced economies. They are usually composed of financing, leasing
and factoring companies, investment and equity funds, insurance companies,
pawnshops, and underground entities. Such activities are also existent and
3
relatively growing in the real estate sector, particularly in its in – house financing
operations.
B. Statement of the Problem
The main problem to be researched in this study is the factors that
determine the growth of shadow banking or in-house financing activities in the
Philippine real estate market. This is due to recent observations that shadow
banking has been evidently growing in the Philippines, particularly in the real
estate sector, and can be subject to certain financial risks. Despite the advantages
of shadow banking in providing credit, non-bank financial intermediation may
pose even greater systemic risks than traditional banks since they are not subject
to BSP regulation and oversight. Thus, this study aims to answer the question:
What are the main factors that contribute to the growth of shadow
banking activities in the Philippine real estate sector?
C. Research Objectives
This study aims:
1. To explain how shadow banking activities occur in the real estate sector
2. To estimate the share of shadow banking activities of listed real estate
developers in the Philippines
3. To identify the main drivers that contribute to the growth of shadow banking
activities in the real estate sector
4
D. Significance of the Study
This study will be useful in determining the size of shadow financing in
the real estate sector. This will help the real estate sector in monitoring and
assessing the risk levels of their credit intermediation activities. This will also
benefit lenders and investors in the real estate sector as they reassess how much is
being used to finance housing loans. Furthermore, this would also help BSP and
other financial regulators implement strict measures in targeting large credit
exposures from the real estate sector, so as to prevent diversion of systemic risk.
E. Scope and Limitations
This study will only focus on the shadow banking activities of listed and
top real estate developers in the Philippines as indicated in Business World – Top
1000 Corporations in the Philippines. The top real estate developers in the country
which are also publicly listed are: SM Prime Holdings, Inc., Megaworld Corp.,
Ayala Land, Inc., Robinsons Land Corp., Filinvest Land, Inc., Rockwell Land
Corp., Vista Land and Lifescapes, Inc., Sta. Lucia Land, Inc.and Shang Properties.
These companies were selected because it holds 46% market share of the total
real estate industry as of 2013. The period of observation will cover ten years
from 2004 to 2013. However, since Vista Land and Lifescapes, Inc. has only
existed in 2006 and lacks more periods of observation, this company will be
disregarded. In this study, shadow banking activities include supplier financing
through construction loans which consist of accounts payable, liabilities for land
and property development and other payables that are related to construction.
Although, it should be noted that the accounts payable to suppliers may just be
5
working capital financing extended by suppliers. This study will also measure in –
house financing through instalment payables and deposits, as well as public and
private financing through bonds and notes. This study mainly estimates the size of
shadow banking and determines the main drivers that affect the growth of shadow
financing activities held by the major property developers in the country. In
determining the factors, a panel regression model is used. However, due to
limitations in this regression model, it should not be used for forecasting the
future trend of shadow banking.
F. Definition of Terms
The following terms are some important concepts used in the study:
1. Shadow banking system: the system of credit intermediation that involves
entities and activities fully or partially outside the regular banking system, or nonbank credit intermediation in short (Financial Stability Board, 2014).
2. In house – financing: This is defined as a type of seller financing offered by real
estate developers to their buyers who want to purchase a property in a series of
installment payments without resorting to third-party financial institutions
(Lamudi, 2014).
3. Supplier financing: This is where suppliers facilitate trade in meeting the credit
needs of affiliated companies. This is usually made up of construction loans and
liabilities related to property development.
4. Private Placements: This refers to the sale of securities to a relatively small
number of investors as a way of raising capital. The placement is not required to
register with the Securities of Exchange and Commission since it only caters to
6
few investors. Also, since the placements are private, the average investor is only
made aware of the placement after it has already occurred.
5. Non-Bank Financial Institutions (NBFIs): entities that are registered in any
government regulated body as a non-bank financial intermediary, but holds
similar functions where financial intermediation is implied. In general, NBFIs are
not allowed to borrow from the public. However, NBFIs may obtain a license to
have “quasi – banking” functions, in which case they are allowed to borrow from
the public provided that deposits are not among the debt instruments issued.
6. Debt Ratio: A financial ratio that measures the extent of a company’s or
consumer’s leverage. The debt ratio is defined as the ratio of total debt to total
assets, expressed in percentage, and can be interpreted as the proportion of a
company’s assets that are financed by debt. The higher this ratio, the more
leveraged the company and the greater its financial risk.
7
CHAPTER II
REVIEW OF RELATED LITERATURE
A. What is Shadow Banking?
In most studies, shadow banking is mainly defined by the nature of the
entity – that it is usually less regulated than traditional banks and lacks a formal
safety net. In other studies, it is thoroughly defined as activities that “consist of
credit, maturity and liquidity transformation that take place without direct and
explicit access to public sources of liquidity or credit backstops, and are
conducted by specialized financial intermediaries called shadow banks, which are
bounded together in an intermediation chain known as the shadow banking
system (Pozsar, 2013). Based on the Financial Stability Board, there are two
proposed definitions of Shadow banking– broad and narrow. The broad definition
of shadow banking is a system of credit intermediation involving entities and
activities outside the regular banking system. The four key aspects of
intermediation as stated by Kodres (2014) are:
1. Maturity Transformation: obtaining short term funds to invest in long term
assets
2. Liquidity Transformation: similar to maturity transformation that consists of
using cash-like liabilities to buy harder – to – sell assets such as loans
3. Leverage: employing techniques such as borrowing money to buy fixed assets
to magnify potential gains or losses in an investment.
8
4. Credit Risk Transfer: taking the risk of a borrower’s default and transferring it
from the originator of the loan to another party.
The narrow definition further breaks down the broad definition into parts: (1) the
systemic concerns, in particular by maturity/liquidity transformation, leverage and
flawed credit risk transfer, and/or (2) regulatory arbitrage concerns (Jeffers &
Pilhon, 2014).
In addition, Tobias (2013) explains that shadow credit intermediation
happens in an environment where “prudential regulatory standards are either not
applied or are applied to a materially lesser or different degree” compared to
regular banks. Credit intermediation per se is a subset of financial intermediation
that involves borrowing and lending through credit instruments that consists of
credit, maturity and liquidity transformation. These are usually enhanced through
the use of third-party liquidity and credit guarantees, in the form of liquidity or
credit put options (Jeffers & Pilhon, 2014).
A liquidity put option by the private sector is provided in the form of wraps,
guarantees or credit default swaps (CDs) that is offered by insurance companies
or banks.
Tobias (2013) also mentions that financial intermediation activities associated
with public sector guarantees are called to be “officially enhanced”. Official
enhancements to credit intermediation are classified into four categories.
1. A liability with direct official enhancement must be on a financial institution’s
balance sheet, while off-balance sheet liabilities of financial institutions are
indirectly enhanced by the public sector. Direct and explicit official
9
enhancement activities include on-balance sheet funding of depository
institutions, insurance policies and annuity contracts, liabilities of most
pension funds and debt guaranteed through public sector lending programs.
2. Direct and implicit official enhancement activities include debt issued or
guaranteed by the government –sponsored enterprises (GSEs), which benefit
from an implicit credit put to the taxpayer.
3. Liabilities with indirect official enhancement generally include the off balance
sheet activities of depository institutions, such as unfunded credit card loan
commitments and lines of credit to conduits.
4. Indirect and implicit official enhancement include asset management activities,
such as bank – affiliated hedge funds and money market mutual funds
(MMFFs) as well as the securities lending of custodian banks.
Activities that do not benefit from any form of official enhancement are
said to be “unenhanced”, such as guarantees made by monoline insurance
companies or companies operating in one specific financial area. Shadow credit
intermediation includes all credit intermediation activities that are implicitly
enhanced, indirectly enhanced, or unenhanced by official guarantees.
B. Shadow Banking Growth Factors
Shadow banking growth can be attributed to various factors. First, when
government bond yields are low and investors are looking for higher – yielding
assets, it is the shadow banking system that often supplies those assets – the
search for yield effect. Second, tighter bank regulation encourages institutions to
10
circumvent it through non-bank intermediation. Third, growth of shadow banking
can be complementary to the rest of the financial system. In emerging markets,
the growth of pension funds and insurance companies has often come along with
the growth of investment funds and other nonbank intermediaries. In the United
States, shadow banking grew from the demands of so called institutional cash
pools for alternatives to insured deposits and safe assets (Valckx, 2014).
Econometric analysis also supports the role of these factors in determining
the growth of shadow banking. Main findings show that higher growth of shadow
banking is associated with various complementing factors. One is bank regulation
in terms of more stringent capital requirements. Banks have an incentive to shift
activities to the nonbank sector in response to certain regulatory changes. Another
is liquidity conditions, the negative correlation of shadow banking growth with
term spreads and interest rates becomes stronger after 2008. Moreover, there is
the institutional cash pools and financial development. Stronger growth of
institutional investors is associated with shadow banking, consistent with
complementarities and demand side effects (Valckx, 2014). Lastly, there is the
growing banking sector. Higher banking sector growth rates indicate higher
growth of shadow banking, which also suggest complementarities.
Data from flow of funds and sectoral accounts provide a quantitative
approximation of various sources of shadow banking risk. Specifically, in
addition to size, rough approximations of maturity risk (based on whether assets
are of long or short duration, liquidity risk (based on whether assets are liquid and
easy to trade), credit risk (share of loan assets that carry substantial credit risk),
11
leverage (total assets to equity), and interconnectedness (how these entities are
exposed to banks through asset holdings or liabilities) can be inferred from the
flow of funds and sectoral balance sheet breakdowns (Valckx, 2014).
However, the type of risk analysis based on this data has certain
limitations. Aggregation at the sectoral level can cover up important
vulnerabilities at the entity level. Risk scores of individual sectors may
underestimate both interdependence among shadow banking entities and exposure
to common factors. Nevertheless, this level of analysis may still be useful as a
starting point for assessing the magnitude of risks posed by shadow banking
entities and tracking their growth through time.
C. Size of Shadow banking
During the U.S. financial crisis, the gross measure of shadow bank
liabilities grew by $22 trillion as of 2007, according to a study conducted by
Pozsar et al.(2013). This was even higher than the total liabilities in the banking
sector, which was around $14 trillion. The size of shadow banking made a
significant decline after the peak of 2007, whereas total liabilities in the banking
sector continued to increase throughout the crisis.
According to IMF (2014), in its 2014 Global Financial Stability Report,
shadow banking amounts between 15 and 25 trillion dollars in the United States,
between 13.5 and 22.5 trillion in the euro area, between 2.5 and 6 trillion in Japan,
and around 7 trillion in emerging markets. In emerging markets, shadow banking
is seen to grow at a faster pace than the tradition banking system.
12
Figure 2. Shadow Bank Liabilities versus Traditional Bank Liabilities
Source: Board of Governors of the Federal Reserve System, “Flow of Funds accounts of the
United States”(as of 2011:Q3); Federal Reserve Bank of New York
In the Philippines, although non-bank institutions are supervised by the
BSP, they provide alternative source of funding and have a certain degree of
flexibility compared to banks in terms of policies imposed by the central bank.
Based on the BSP’s first Consumer Finance Survey, 8 out 0f 10 Filipinos (78.5%)
of the total respondents do not have deposit accounts. Thus, instead of people
depositing money in a bank, they keep their money in alternative entities such as
“paluwagan”, savings and loans association and other informal lending activities
under shadow banking, which are completely unregulated.1
As financial markets continue to show progress, shadow banking also
develops wherein various forms of credit instruments are formed that are no
longer regulated by the central bank. The figure below shows the World Bank
estimates on the size of shadow banking in the Philippines along with other
1
The survey was conducted between November 2009 and January 2010 using 2008 expenditure
data. Sample size consists of 10,520 households with 3,872 households within NCR and 6,648
households outside NCR.
13
emerging markets in 2010. It also shows that the estimated size of shadow
banking in the Philippines is more than one-third of the whole financial system’s
total assets, and has been gradually increasing similar with other neighboring
countries.
Figure 3. Shadow Banking in Emerging Markets
Source: Ghosh et.al. (2012)
D. Entities and Activities of Shadow Banking
As mentioned previously, the bank-like functions that shadow banks
execute are credit, maturity and liquidity transformation and credit risk transfer.
Activities that contribute to the emergence of shadow banks are securitization and
funding of financial entities and collateral intermediation. Securitization in banks
transforms the risk on a single balance sheet, while securitization in shadow banks
involves a chain of balance sheets with different sources of funding. This activity
is prone to excessive maturity and liquidity transformation, leverage, and evasion
of bank regulations.
14
In the shadow banking system of advanced economies, entities that are
usually involved in the borrowing side are Special Purpose Vehicles (SPV) and
Structured Investment Vehicle (SIV), and on the funding side are Repurchase
Agreement (repo) markets and Money Market Funds (MMF). Aside from these,
other entities that are prone to “runs” and systemic risk are broker-dealers,
investment funds (i.e. hedge funds, equity funds, bond funds, mixed funds),
securities lenders, cash funds and investment banks. Furthermore, European
Economic and Social Committee (EESC, 2012) highlights that some commercial
banks perform shadow banking activities as well (Jeffers and Pilhon, 2014).
The Financial Stability Board’s (FSB, 2014) recent report on Asia’s
Shadow Banking identified the following criteria that exclude financial activities
from shadow banking:

The non-bank financial institutions (NBFIs) that are subject to a proper
regulatory body.

The exposure in risks are not considered systemic

The NBFIs that do not carry out credit intermediation activities.
Credit unions or Cooperatives (CUCs) are classified as shadow banks in
the country, which have the second largest number of 22,555 institutions with
6,395 total assets. The FSB (2014) reported that CUCs are basically involved in
offering financial services that provide convenient access to savings and lending
services. Moreover, its activities hint systemic risks indicated by maturity
transformation, liquidity transformation and leverage. CUCs are also involved in
15
loan provisions that rely on short-term financing and are generally not under a
regulation body.
Another type of informal credit intermediation is the “paluwagan”. A
“paluwagan” is a form of mutual self-help group wherein a group of persons turn
in the same amount of money towards a common fund and then take turns in
using the amount of funds collected. As it is usually managed by one person,
deposits in a “paluwagan” do not earn interest, but each member of the group is
provided with larger funds when it is the person’s turn to collect the “sahod”
(income)(Agabin, 1989).
Pawnshops are also financial institutions that fall under shadow banking
due to minimal leverage, and given that they are involved in loan provision
carried out outside the banking system. Also, loan provision is dependent on
short-term funding and outside the regulation of the banking system. Furthermore,
based from the FSB report, only India and the Philippines identified Microfinance
Institutions (MFIs) as shadow banking because of its many systemic risk
indicators and minimal regulatory arbitrage out of the seven Asian jurisdictions.
Lastly, finance companies are categorized as shadow banking as well because of
their exposure to liquidity and maturity mismatch and its engagement in shortterm loan provision subsidy. Most finance companies are also not strictly
monitored by prudential regulatory policies unlike the banks under the BSP.
Shadow banking activities in the Philippines also take place in the real
estate sector particularly in in-house financing activities of developers. In-house
financing is a common practice among property developers wherein it provides
16
high-interest credit with less rigid requirements for buyers, which serves as an
option to the more conventional mortgage loan through banks and other lending
companies (Kritz, 2015). Winston Conrad B. Padojinog, an economist from
University of Asia and the Pacific supported the claim that the drastic growth of
shadow banks is in the particular sector. He added that most real estate developers
who avail in-house financing do not qualify under the bank standards (Ang, 2013).
The BSP enacted a 20% limit on a bank’s exposure to real estate that further
tightens the availability of credit for real estate. Due to this, real estate developers
are looking for alternative sources of credit to finance their operations especially
since bank lending on real estate tightened recently. Majority of activities in the
shadow banks are from real estate, and the regulators are in the shadows on how
much credit these real estate developers lend.
Based on a staff report by the IMF, Bank lending has accelerated since
end-2013, reaching almost 21% in April 2014. Estimated to be around 35% of
GDP, credit penetration of the private sector through banks is low from a regional
perspective, however nonbank financing is seen to grow significantly. Large
companies have been issuing debt securities, which allows them to lock in fixed
rate funding, lengthen maturities and bypass bank exposure limits on individual
borrowers. While domestic bank loans still dominate, other sources of formal
credit (bonds, external loans) are gaining in importance, and total debt of the
nonbank private sector has risen steadily to around 55 percent of GDP. The vast
majority of this debt is extended to corporates. On average, leverage of listed
companies is low at 100 percent in 2012. However, debt is unevenly distributed
17
across firms, and companies with leverage ratios above 200 percent account for
about one-third of total corporate debt. Banks exposure to real estate amounted
21.8 percent of total loan portfolios at end 2013, and has been growing faster than
other segments. Sixty percent of real estate loans are to commercial entities
(developers and construction companies) with the rest to households. Real estate
developers and their affiliated conglomerates are also active in debt and equity
markets, and developers— as well as several public agencies—provide credit
directly to home buyers (estimated to total around 7.5 percent of GDP, including
mortgage lending by banks) (IMF, 2014).
E. Real Estate Adequate Financing
Real estate developers and investors usually rely on leverage funds to finance
their projects. This refers to the minimum amount of equity funds and a maximum
amount of borrowed funds to control large investment. The funds needed for land
acquisition can come from various sources. The seller can back a purchase money
deed of trust, the developer can form a syndicate and raise the needed capital by
selling ownership shares, or the funds can be borrowed from financial institutions
or other private parties. After land acquisition, funds for construction can come
from traditional lenders, such as banks, thrifts institutions, insurance companies,
and mortgage companies. In addition, construction funds can also come from
pension funds, real estate investment trusts, and endowment funds (Mckenzie,
2011).
18
Generally, the loan used for funds to construct buildings and other site
improvements is referred to as a construction loan, or interim, loan. It is mainly
used to fund all the hard costs of construction such as materials and labor for site
improvements. It may also cover some soft costs, such as leasing costs, planning
costs, and management. It may also include finishing the interior space for tenants
through the lease-up stage. Lenders prefer to make loans only for the cost of site
improvements, but competition among many lenders may result in more flexible
lending policies.
F. In-house financing as a quick path to ownership
Most developers target first time buyers among the OFWs and the middle
income segments who do not qualify for more stringently regulated conventional
bank loans or who choose not to undergo a more time – consuming approval
process. Though there are buyers who meet the qualifications for Bank loans,
Social Security System (SSS) or Pag-Ibig financing, the process of availing for
these loans is still very time consuming. In the in-house financing scheme,
developers offer payment schemes that suit the financial capacity of their buyers.
This would also mean that buyers directly pay their monthly amortization to the
developers. This type of financing require less paperwork, background check and
is processed faster compared to bank loans, but has a higher interest rate. The
interest rate is stated to range from 14% to 18% (Rappler, 2014). Developers only
require buyers to pay 10% if the project is preselling or 20%, the higher the down
payment, the lower the balance and monthly amortization.
19
Despite the ease in choosing the in-house financing scheme, this may also
pose as a problem. BSP supervision is limited only to banks. The largest but
hidden part of home loan finance is made of credit receivables extended by
developers to the buyers through contract to sell (CTS) agreements. CTS is
common in emerging economies where there is a shortage of accessible mortgage
market. Developers use deferred sale contracts, which in practice is a credit
facility granted to a purchasing household. The title to the property is transferred
only upon full payment and in case of default, the CTS is cancelled and the unit
can be resold to another client. This lending activity is neither regulated nor
measured by BSP. Although the idea of CTS is positive in terms of expanding
home finance to low and middle income sector, it is possible that these developers
or real estate companies may have been applying less stringent lending standards
and more generous loan terms than required of banks (e.g. zero interest rates, zero
downpayment) which opens up the financial system to trouble (Ballesteros, n.d).
CTS are channeled to the financial system through a refinancing scheme
by banks and government housing finance corporation (e.g. HDMF) but the
amount of CTS retained by developers could still be large. Based on the assets of
the largest developers it is possible that about 15% of assets are made of CTS
considering preselling activities. Other developers could have more since banks
are more reluctant to finance them.
20
G. Factors and Theories that Affect Shadow Financing Activities in the
Real Estate
Interest Rates based on Austrian Business Cycle Theory
Austrian business cycle theory (ABCT) emerges from Austrian school of
thought, which assumes that money is not neutral and financial flows serve as a
mirror of what is happening in the real economy (Zelmanovitz, 2011). The main
proponents of the Austrian business cycle theory historically were Ludwig von
Mises and Friedrich Hayek followed by Murray Rothbard, James Keeler and
Roger Garrison to name a few. Hayek won a Nobel Prize in economics in 1974
(shared with Gunnar Myrdal) in part for his work on this theory. Austrian
business cycle theory, or preferably called credit cycle by Austrian economists,
can be divided into four stages, namely, expansion, crisis, recession and recovery.
Expansion stage starts when low interest rates or expansionary monetary policy
tend to stimulate borrowing or credit expansion from the banking system to
businesses and individual borrowers, which causes an expansion of the money
supply through the money creation process in a fractional reserve banking system.
In this stage production and prices increase. This then leads to unsustainable
credit-sourced boom during, which the artificially stimulated borrowing seeks out
diminishing investment opportunities, and results in widespread mal-investments
causing capital resources to be misallocated into areas that would not attract
investment if the money supply remained stable. Crisis stage starts with a
correction (or credit crunch or recession or bust) that occurs when exponential
credit creation cannot be sustained. The money supply suddenly and sharply
21
contracts. In this stage stock exchanges crash and multiple bankruptcies occur.
Recession stage follows after the crisis when output and prices drop and interest
rates increase. Recovery stage start when markets finally “clear” and causing
resources to be reallocated back towards more efficient uses. In this stage stocks
recover due to the fall in prices and incomes. Recovery and prosperity are
associated with increases in productivity, consumer confidence, aggregate
demand and prices.
Thus, in this case, interest Rates have a major impact on real estate
markets. When interest rates fall, the cost to obtain a mortgage to buy a home
decreases, which creates higher demand for real estate, and eventually pushes
property prices up. Conversely, as interest rates rise, the cost to obtain a mortgage
increases, thereby lowering the demand and prices of real estate (Nguyen, n.d).
However, to determine fully how government-influenced interest rates, capital
flows and financing rates affect property values, the income approach is used.
The income approach focuses on what the potential income for rental
property yields relative to initial investment. The income approach is used
frequently for commercial real estate investing. The income approach relies on
determining the annual capitalization rate for an investment. This rate is simply
the projected annual income from the gross rent multiplier divided by the original
cost or current value of the property (Abraham, n.d).
Interest rates can significantly affect financing costs and mortgage rates,
and can also influence capital flows of investments. In the real estate, the changes
22
in lending rates either add or reduce the amount of capital available for
investment. The amount of capital and the cost of capital affect demand but also
supply, capital available for real estate purchases and development. For example,
when capital availability is tight due to high interest rates, capital providers tend
to lend less as a percentage of intrinsic value, or not as far up the "capital stack."
This means that loans are made at lower loan to value ratios, thus reducing
leveraged cash flows and property values. These changes in capital flows can also
have a direct effect on the supply and demand for property. The cost of capital
and capital availability would determine supply by providing additional capital for
property development (Stammers, n.d).
Land values based on Relative Mobility and Financial Accelerator Theory
Land values also serves as an important factor to consider in financing for
real estate activities. Land values tend to appreciate in varying central locations.
In the Philippines, land values are higher in central business districts (CBDs)
found in Makati, Ortigas Center and Bonifacio Global City. This is due to the
increasing population residing in the area, where the effect of a so called” relative
mobility” takes place (Manganelli, 2015). This then further leads to an increase in
demand of offices and condominiums, and consequently an increase in real estate
financing activities. Moreover, based on the “financial accelerator” framework,
represented by Kiyotaki and Moore (1997), Bernanke et al (1994) and Aoki et al
(2002), borrowing conditions are determined by the total value of real estate
assets (as collateral). Increase in land prices lower external finance premiums and
23
improve credit availability for borrowers, thus causing an increase in demand for
real estate assets and driving property prices even higher (Davis and Zhu, 2004).
Real Estate Loans from Banks based on Credit Rationing
The supply of real estate loans from banks may also affect shadow
financing activities. According to the theory of credit rationing that has been
modelled by Bell (1990) based on the study by Haugen (2005), there is a form of
interaction between formal and informal credit institutions. His model shows that
when formal credit is rationed or tightened, and the informal lender is able to
offer contract loans that are preferred by the borrowers, then there would be a
spilled over demand in the market. This means that when formal banking
institutions do not give as much credit as the borrowers desire, the borrowers
would turn to informal credit intermediaries.
Based on Circular No. 600 that was implemented in 2012, BSP set a limit
on bank real estate loans (RELs) of twenty percent (20%) of total loan portfolio.
However, latest central bank data show that banks’ real estate exposure went up
23 percent to P1.159 trillion as of September 2014. This exposure was made up of
P977.085 billion in loans and P181.882 billion in investments to the sector
(Martin,2015), which is above the minimum requirement. This further allowed
banks to implement stricter measures in handing out loans. The tightening of the
supply of loans from banks consequently leads to more demand for loans from
non-financial institutions, as the theory suggests that the excess demand is catered
by the informal sector. Thus, a decreasing supply of real estate loans from banks
24
result to more financing activities among real estate companies and its partner
institutions.
Based on a study on “Factors Affecting Leveraging for Quoted Real Estate
Development in China” by Simiyu and Huo (2013), there are also other
significant variables that greatly determines the financing growth in the real estate.
These are company size and profitability.
Company size (Assets)
Company size determines financial leverage for virtually all sectors. All
theories explaining capital structures have dwelled much on the existing
connection between financial leverage and company size (Schoubben and van
Hulle, 2004, p. 595). Company size is calculated by transforming total assets to
natural logarithm. Measuring size of company as log of sales or use of absolute
value of assets was supported by Huang and Song (2002). The reason for use of
absolute value of total assets or natural log of sales as a preferred computation of
firm size is because the two are highly correlated with a coefficient of 0.79.
However, in a recent study of Pandey (2004), it applies natural log of total assets
to compute for firm size. Low risks brought about by the finance distress are
demystified by the fact that large firms have more developed portfolio, great
monitoring system and transparency. (Ang et al. 1982; Myers 1984; Myers &
Majluf, 1984 and Bevan and Danbolt, 1999). Large companies are advantaged in
comparison with small ventures because their credit rating structure is favored by
the size of its assets.
25
Profitability based on Minsky’s Theory and Pecking Order Theory
According to Minsky’s Theory, as the economy perform better and
businesses grow, expected profits increase and firms tend to raise their level of
debt beyond their ability to repay. However, companies believe that profits will
rise and the debt will eventually be repaid without much trouble. The rising profit
attracts other firms or entrepreneurs to join in and encourages them to raise their
level of debt. More debt leads to more investment, borrowers’ financial health
show visible improvement, the economy grows further, and this makes lenders
more eager to lend to firms even without full guarantees of success. Through time,
the pace of debt accumulation starts to rise much faster than borrowers' ability to
repay and serve the debt (Ascarya, 2013).
This is referred to as Ponzi financing. In this way, the economy has taken
on much risky credit. At this stage, the foundation for an economic bust is set in
motion, started with the default of some big firms, which make lenders realize the
actual risks in the economy and stop giving credit. Refinancing becomes
impossible for many, and more firms default. If no new money comes into the
economy to allow the refinancing process, a real economic crisis begins. During
the recession, firms start to hedge again, and the cycle is closed. Many economists,
such as Wray (2009a and 2009b) and Prychitko (2010), argue that current global
financial crisis could be called the ‘Minsky Moment’ or ‘Minsky Crisis’.
Profitability determines income shield and velocity of cash flow.
According to Brealey & Allen (2008) in their works, it states that the commonly
26
applied determinant of company profits is return on assets (ROA).Highly
profitable firms have high income shield with high velocity of cash flow. As
postulated by Pecking Order Theory, firms with higher profits (high internally
generated funds) prefer to borrow less because it is easier and more cost effective
to finance from internal fund sources. So, as per this theory, there will be an
expected negative relation between profitability and leverage. On the contrary,
Trade-off theory suggests that this relationship would be positive. Since profitable
firms are less likely to go bankrupt, and hence can avail more debt at cheaper
rates of interest. Based on the results of the study, both company size and
profitability greatly influence the behavior of supply of funds (leverage) for real
estate developers.
Macroeconomic factors
Other macroeconomic factors are also considered to affect the financing
activities in the real estate sector. With higher interest rates, it is said to attract
more remittances and a further appreciation of the exchange rate (Bayangos,
2012).
Philippines is considered to be one of the world’s largest remittance
recipients, with over 10.5 million Overseas Filipino Workers (OFWs) living and
working in 210 countries and territories worldwide. In 2013, OFW remittances
grew 7.4% to US$22.9 billion, or around 8.4% of GDP. It has been estimated that
60% of these remittances go directly or indirectly to the real estate sector,
according to World Bank (Global Property Guide, 2014). These OFW remittances
generate the low-end to mid-range residential property market, housing projects
27
and mid – scale subdivisions in regions near Metro Manila, such as the provinces
like Cavite, Batangas and Laguna.
Remittances based on the Dutch Disease Effect
According to BSP, based on the “Dutch Disease Effect”, higher flows in
remittances will increase liquidity in financial markets which may allow the
interest rate to decrease and lead to an expansion of credit. The lower interest rate
may invite an increase in expenditure. Increased investment of remittances in real
estate or the stock market can push up asset prices which may exert a wealth
effect. The total demand impact of an increase in remittances is the sum of these
various effects: the direct expenditure effect, the multiplier effect and the interest
rate effect will have a positive impact while the exchange rate appreciation could
have a negative impact.
Exchange Rates based on the Exchange Rate Effect
Foreign exchange rate movements play a significant role on OFW demand
for housing. Against the dollar, the peso has appreciated from around PHP55
(2003-2005), to an average of PhP51 in 2006 and PHP46 in 2007 (Global
Property Guide, 2008). Because of this, Overseas Filipinos’ foreign earnings buy
less and less in the Philippines during this period. An appreciation of exchange
rates can then lead to a decrease in demand in real estate investments covered by
most OFWs.
Based on the exchange rate effect, an increase in the nominal peso‐dollar
rate corresponds to depreciation rather an appreciation of the peso. The BSP
maintains a freely floating peso, whose value is determined greatly by supply and
28
demand factors. The nominal exchange rate is sensitive to the level of the current
account balance and to the interest rate differential. The direct effect of an
increase in remittances on the current account is positive leading to an
appreciation, but there are also indirect effects on exports (through the impact of
remittances on competitiveness) and on imports (through the increase in domestic
demand that remittances generate) and the overall impact of an increase in
remittances could be a deterioration of the current account balance which would
lead to a depreciation of the nominal exchange rate (Bayangos, 2012).
The large flow of remittances in the country further promotes economic
growth in the consequently leading to more credit intermediation in the real estate
sector. An increase in income allows more households to increase spending and
borrowings. The macroeconomic condition of the country is better measured by
its gross domestic savings, as also mentioned by Dr. Victor Abola. Based on the
World Bank, gross domestic savings is calculated as GDP less final consumption
expenditure (total consumption).
This is also supported in Mankiw’s Loanable funds theory, where the sum
of private and government savings are the “flows into the financial markets” and
investments the “flow out of the financial markets” (Mankiw, 1997). In this
manner, he interprets the national accounting identity between saving and
investment as a budget constraint that is no flows of loanable funds would be
available without prior saving. In short, the higher saving is, the more credit will
be supplied which given all things constant, lowers interest rates.
29
However, in a study conducted by the Macroeconomic Policy Institute of
of Germany, a critique was made to argue the loanable funds theory. It explained
three cases regarding the households saving behaviour and the consequences for
the firm sector.
First, if households save financially, the revenues of the firm during the
period will be lower than its expenditures, such that its total financial assets
decrease and the household’s net financial assets to increase. In this case, it is
clear that household savings reduced the investing firm’s financial means. The
firm would have to borrow in order to maintain its liquidity position.
Second, if households did not save financially, the same amount of
revenues would flow into the firm that it had previously left in the form of
expenditures (given the assumption that revenues and expenditures are equal to
receipts and payments), the firm would not have to borrow.
Third, if the household spent more than it received in revenues from the
firm, the firm would be able to increase its revenues and would have more
financial means than it initially started.
In all three cases, it would then conclude that the households financial
saving extracts financial means from the firm and does not add to it - as claimed
by loanable funds theorists (Lindner, 2013). The author also tries to assert that the
amount of debts and financial assets do not necessarily depend on savings, but on
the frequency of money or funds that is transferred. Thus, in principle, the amount
of money in the economy could approach zero while the amount of debts and
financial assets could reach with no limits.
30
CHAPTER III
THEORETICAL FRAMEWORK AND EMPIRICAL
METHODOLOGY
Theoretical Diagram
A. Theoretical Framework
In using the IS-LM model with both an informal credit market and
regulated banking sector (Carpenter 1999), the economy has only three assets:
money, bank loans and informal market credit. The agents and firms only have
two options for credit access, which are formal credit market through the banking
sector and informal credit market. In this economy, it is assumed that there are no
alternative sources of funds. Loans from banks are from both deposits and credit
extended by the central bank. It has a fixed interest rate, which is usually below
market cost that results into an excess demand for credit. This results to an excess
demand for credit, which is captured by the informal markets. The demand for
deposits is represented by the money demand function, which is expressed as:
31
𝐷(𝑖, 𝑦)
Demand for bank deposits have a negative relationship with interest rate 𝑖
in the informal sector and have a positive relationship with income 𝑦 because of
transactions demand. On the other hand, supply is determined by the reserve
requirement 𝜏 and reserves in the banking sector R. By setting money demand
equal to money supply, the expression will be as follows:
𝐷(𝑖, 𝑦) = 𝜏 −1 𝑅
The reserve requirement set by the central bank and the amount of
reserves in banks are the determinants of the quantity of loans 𝑞, which is given
by:
𝑞 = 𝐷(𝑖, 𝑦)(1 − 𝜏) + 𝐶
The loans are deposits not in the reserves plus the credit extended by the
central bank, which is captured by C.
The residuals represent the demand for informal credit. The excess
demand for credit is catered by the informal market and is represented by the
difference between the aggregate demand for credit and quantity of loans supplied.
In theory, formal interest rate is always less than the informal interest rate. Thus,
interest rates charged by banks do not affect the demand for credit in the informal
market. It is the quantity of loans supplied, which is represented by 𝑞 that affects
the informal market.
On the contrary, the demand function for informal markets is a function of
interest rate 𝑖, income 𝑦 and quantity of bank loans 𝑞. Informal money demand
negatively depends on interest rate 𝑖 and quantity of bank loans 𝑞 , while it
32
positively depends on income 𝑦 . The informal money demand function is
expressed as:
𝐿(𝑖, 𝑦, 𝑞)
If the effect of informal credit demand on interest rate
𝝏𝑳
𝝏𝒊
is small, the
market is its only source of credit, which makes it a necessity for the firm or agent.
𝝏𝑳
The response of informal market demand on output is represented by 𝝏𝒚, which
represents the substitutability between the formal and informal markets. If the two
markets are highly segregated, an increase in bank credit has an insignificant
effect on the demand for informal credit. Lastly,
𝝏𝑳
𝝏𝒒
is the measure of
interconnectedness of the two markets.
The supply of informal markets is a portfolio decision, and is represented
by 𝜆(𝑖, 𝑞). A higher return stimulates more lending, with
𝝏𝝀
𝝏𝒊
> 0. Similarly, an
increase in income 𝑦 will also encourage more lending.
Informal interest rate is determined by equating the informal money
demand and informal money supply, which is called the IM curve.
𝐿(𝑖, 𝑦, 𝑞) = 𝜆(𝑖, 𝑞)
𝑖 = 𝐼(𝑦, 𝐶, 𝑅)
In comparison with the IS-LM model, the monetary policy shift both IS
and IM curves. In this model, IM curve is upward sloping, and the steepness
𝝏𝑳
𝝏𝝀
depends on the derivatives of informal credit, which are 𝝏𝒚and 𝝏𝒚. A high demand
for credit due to an increase in output results into a steeper IM curve. A shift in
the IM curve is intuitive because for a given output, higher credit or reserves
33
lowers the interest rate, which shifts the IM curve downward. On the contrary,
lower credit or reserves increases the interest rate, which shifts the IM curve
upward. This is because bank loans and informal loans are considered substitutes.
Shifts in the IS curve represents the formal credit channel in the economy. When
IS curve shifts to the right, more credit is allocated in the economy, which reduces
the interest rate in the informal market. This results into a downward shift in the
IM curve.
34
Table 1. Factors that Affect Shadow Banking in the Real Estate Sector
Theory
Variable
Expected
Sign
Informal Interest Rates (%)
IR
(-)
LV
(-)
COSIZE
(+)
PROF
(+)/(-)
REL
(-)
REM
(+)
EX
(-)
GDS
(+)
“Austrian Business Cycle Theory” or “Credit Cycle
Theory”
Land Values
“Relative Mobility” and “Financial Accelerator
Framework”
Company Size (log of assets)
“Pandey’s Theory”
Profitability
“Minsky’s Theory” or “Peking Order Theory”
Real Estate Loans
“Credit Rationing”
OFW Remittances
“Dutch Disease effect in the Philippines”
Exchange Rates
“Exchange Rate Effect”
Gross Domestic Savings
“loanable Funds Theory” by Mankiw
35
As based on the literature, the above framework is used as determinants that drive
shadow financing growth in the real estate sector.
B. Empirical Methodology
Data Collection
This study aims to examine the growth of shadow banking or shadow
financing activities in the real estate sector and to identify whether it has been
growing significantly than the traditional banking system. In estimating the size of
shadow financing among real estate developers, data of financial assets will be
gathered from the consolidated financial statements (2003-2013) of selected real
estate developers in the Philippines (Annex 1). These listed companies, which
serve as the backbone on the real estate industry, reflect the development of the
industry as a whole. Specifically, this study will look into the debt ratio or the
percentage of loans lended by real estate companies from their overall assets.
Real estate financing activities can be financially examined in the
liabilities section in the balance sheet. Shadow banking can be examined in terms
of supplier financing through construction loans which consist of accounts
payable, liabilities for land and property development and other payables that are
related to construction. This study will also measure in – house financing through
instalment payables and deposits, and private placement financing through bonds
and notes.
To get the proportion of the companies’ assets that are financed by debt,
total value of shadow financing activities is divided by the total assets of the real
36
estate companies. The higher the ratio, the more leveraged the companies and the
greater its financial risk.
Shadow financing
Total Assets
% Shadow Financing
After getting the total values of % Shadow Financing, a panel regression
analysis is done to evaluate how the significant factors affect the growth of
shadow financing in the real estate. A similar method was also done by a previous
study done by Simiyu and Huo (2013).
Panel Regression
The analysis is conducted by using the panel regression model to examine
empirically the main determinants that affect shadow financing. The dependent
variable is the debt ratio or the percentage share of shadow financing activities
during the time period from 2003 to 2013. The general regression equation is of
the form:
yitΣxit it, i t 
In this model, yit represents the leverage of real estate firm i in year t; is
the constant term; Σxit is a vector of independent variables which serve as
explanatory indicators; it, is the unobserved error term or disturbance term.
Based on the literature review, the regression model will be constructed in the
following expression:
%SF = f (IR,LV, COSIZE, PROF, REL, REM, EX, GDS)
37
%SF serves as the dependent variable that represents the short term debt ratio
with the size of total assets. The independent variables are: IR - interest rates, LV
-land values, COSIZE - company size, PROF – profitability, REL - real estate
loans from banks, REM - OFW remittances, EX - exchange rates, and GDS gross domestic savings.
The table below serves as a summary of independent variables to be tested with
the corresponding signs.
Hausman Test
In determining whether to use fixed effects or random effect in running the
model, the Hausman test is performed. The Hausman test serves as a statistical
hypothesis test that evaluates the consistency of an estimator when compared to
an alternative estimator. Under the null hypothesis, random effects is appropriate,
while under the alternative hypothesis, fixed effects is appropriate.
Structural Vector Autoregression Model
In this section, the structured vector autoregression model (SVAR) is also
used to analyze the factors that affect shadow financing. VAR serves as an nequation with n-endogenous variables, where each variable is explained by its
own lag, as well as current and past values of other endogenous variables in the
model. Therefore, in the context of modern econometrics, VAR is considered as
multivariate time series that treats all variables endogenous, since there is no
confidence that a variable is actually exogenous, and VAR allows the data to tell
what actually happen. SVAR is said to be useful for two following reasons. First,
relative to a quantitative model, SVARs allow more flexible specifications, and
38
the integration of a larger number of shocks. Second, relative to a panel regression,
the SVARs explicitly assume that all variables are endogenous and interact
between them. SVARs are immune to arguments of reverse causality, and to
incorrectly assuming that a variable is exogenous when it is not. This same
methodology was applied in a study conducted on analyzing the drivers of
housing markets in China by Yang Bian and Gete (2015) and also in a working
paper entitled “What drives Shadow Banking System in the Short Run and Long
Run?” conducted by Duca (2014) in the Federal Reserve Bank of Dallas. Based
on the study, short run changes of shadow banking was modelled that includes a
function of an error-correction term (EC = actual minus equilibrium log-levels of
shadow banking). The error-correction coefficients were seen to be highly
significant, with an expected negative sign.
For this study, the model is mainly used to test whether past shadow
financing activities affect present values of shadow financing. The model is
constructed as follows:
%𝑆𝐹𝑡 = 0 + 1 %𝑆𝐹𝑡−2 + 2 𝑎𝑠𝑠𝑒𝑡𝑠𝑡−2 + 3 𝑝𝑟𝑜𝑓𝑖𝑡𝑡−2 + 4REL +
5 𝑝𝑟𝑖𝑐𝑒𝑡−2+ 6 𝐼𝑅𝑡−1
𝜀𝑡
The model includes a lagged dependent variable to control for residuals
autocorrelation and account for the adjustment delay of the observed %SF.
White Cross-sectional Method
The White cross-sectional method is used get a more accurate and
unbiased results from regressions, in this case, there cannot be any correlations
between error terms. Thus, to remove possible correlations and reducing standard
39
error estimates, this method must be applied. The white cross-sectional method
assumes that the errors are contemporaneously correlated. If there are fixed
effects in the periods this method is used to correct for correlations such as
heteroscedasticity.
40
CHAPTER IV
PRESENTATION, INTERPRETATION, AND ANALYSIS OF DATA
This section presents the results on the data gathered from the
consolidated financial statements of the observed real estate companies. It also
presents significant results from the regression model that determines the factors
that could affect the growth of shadow banking for listed real estate companies.
A. Breakdown of Shadow Banking Activities
Total supplier financing (figure 4) portrays an upward trend with an
estimated value of Php 17 billion in 2004 to Php 111 billion in 2013. A steady
growth is seen from 2004 to 2010, while a rapid increase began in 2011 to 2013.
This explains that there has been an increasing amount of construction loans or
loans related to land and property development incurred by the listed companies.
(See Appendix 2)
Figure 4. Total Supplier Financing
Supplier Financing (in thousand Php)
120,000,000
100,000,000
80,000,000
Supplier
Financing
60,000,000
40,000,000
20,000,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
41
Total in – house financing (figure 5) also reveals an upward growth, although not
as high and as rapid as supplier financing. But similar to supplier financing, there
has also been a great rise of in – house financing activities that occurred in 2012
to 2013. Still, it shows that financing coming from instalment contracts and
customer deposits post a significant portion of shadow banking. For full details of
Supplier financing activities see Appendix 2.
Figure 5. Total In-house Financing
In - house Financing (in thousand Php)
60,000,000
50,000,000
40,000,000
In - house
Financing
30,000,000
20,000,000
10,000,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Financing made through private placements (figure 6) show a more
stunted growth from 2004 to 2011 with an average amount of Php 13 billion.
However, there has also been a sudden increase in private placement financing
that occurred from 2011 to 2013. This means that there has been an increase in
financing through bonds and notes payables. (See Appendix 3)
42
Figure 6. Total Private Placement Financing
Private Placements (in thousand Php)
120,000,000
100,000,000
80,000,000
Private
Placements
60,000,000
40,000,000
20,000,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
The combined data (figure 7) reveals that shadow banking activities as a
whole have been continuously increasing from an estimated value of Php 43
billion in 2004 to Php 268 billion in 2013. The largest contributor to shadow
banking throughout the years mainly comes from supplier financing. This is
followed by in – house financing which contributed a larger growth from 2004 to
2010, with a value of Php 16 billion to Php 29 billion. However, during 2011 to
2013, private placements had a more significant increase compared to in – house
financing, which grew from Php 32 billion to Php101 billion.
43
Figure 7. Breakdown of Shadow banking growth
Breakdown of Shadow Banking Activiites
(in thousand Php)
300,000,000
250,000,000
Private Placements
200,000,000
In-House Financing
150,000,000
Supplier Financing
100,000,000
50,000,000
2004200520062007200820092010201120122013
Total assets of listed real estate developers similarly portray an increasing
trend from a total amount of Php 2.8 billion in 2004 to Php 1.1 trillion in 2013
(Figure 8). This shows the increasing flow of investments made among the
developers through the years. The significant portion of this is derived from the
net operating income that the developers get from tenants. (See Appendix 8).
Figure 8. Total Assets of Selected Real Estate Developers in the Philippines
Total Assets (in thousand Php)
1,200,000,000
1,000,000,000
800,000,000
600,000,000
Total
Assets
400,000,000
200,000,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
44
The net income of listed real estate developers also shows an increasing
trend from 2004 to 2013 (figure 9), indicating a strong and healthy performance in
the sector. Net profit reached an average amount of Php 50 billion in 2013 from a
value of Php 10 billion in 2004 (See Appendix 9). The increase in profit implies
an increasing return on investments from real estate operations. With increasing
profits, this would allow the companies to engage in more debt financing
activities, or the companies could make use of profit generated resources for the
funding of their projects.
Figure 9. Total Profit of Selected Real Estate Developers in the Philippines
Total Profit (in thousand Php)
60,000,000
50,000,000
40,000,000
30,000,000
Total Profit
20,000,000
10,000,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
In terms of determining the proportion of shadow banking to total assets
(figure 10), the shadow banking ratio portrayed a steady growth with an average
of 15% from 2004 to 2011. However, as of 2012 – 2013, the percentage share of
shadow banking over total assets greatly rose to an average of 24%. In this case,
the rapid growth of shadow banking could contribute to greater risks if certain
measures are not taken keep debt levels at minimum.
45
Figure 10. Percentage Share of Shadow banking to Total Assets
% Share of Shadow banking to Total Assets
30.00%
9.38%
25.00%
Private Placements
20.00%
15.00%
2.90%
5.12%
10.00% 5.93%
5.00%
6.28%
In-house
Supplier
10.31%
0.00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
B. Regression Analysis
In applying the Hausman test, results show that the p-value is greater than
0.05, which indicates that random effects is more appropriate in running the
model. This also means that the statistical model corresponds to the data
presented.
In running the panel regression under random effects, various
combinations of lags and leads were used to get the most fitted model. Other
explanatory variables were dropped in the model, since they were estimated to be
insignificant, and did not exactly match a more fitted model. The variables that
were dropped were: OFW remittances, exchange rates and Gross domestic
savings.
As seen in Table 2, the most fitted model that came out was in lagging the
dependent variable, %SF, to two years, along with a combination of 2 leads for
assets, profit and price (land values) and a 1 year lag for interest rates. Certain
46
adjustments were also applied by using the White test in consideration for the
existence of autocorrelation.
Table 2. Estimated Shadow Financing Function
Source: Author’s computation
The above empirical results show that shadow financing as a lagged
variable is statistically significant at 0.2% level, which confirms the theory that
past shadow financing activities have a positive correlation with the present
condition of shadow financing operations. The coefficient of lagged shadow
financing displayed values of 0.56, which means that if past values of shadow
financing increased by 1%, the current share of shadow financing are likely to
increase by 0.55%.
The company size, which is determined by the total assets of the
companies, is seen significant at 13% degree of error. The variable displayed a
coefficient value of -3.26, which means that if total assets increase by 1 unit, the
47
percentage share of shadow financing would decrease by 3.26%. In comparison
with the study on factors affecting leveraging for quoted real estate development
companies in China, the results were different, where company size had a positive
correlation with leverage and significant at 5% level. The negative correlation in
this model goes against Pandey’s theory that there is a positive correlation
between company size based on assets and shadow banking.
This may be due to the reason that the total assets of selected real estate
developers grew at a much faster rate compared to shadow banking, which
explains the negative sign. In the study conducted in China, total assets had a
positive correlation wit the debt financing activitites of real estate developers, this
may be because their debt financing activities are larger compared to their net
assets.
Profitability was positive and statistically significant at 0.8%, as claimed
by trade off theory that this should have a positive relationship with debt
financing. It had a coefficient value of 1.51, which means that if annual profit
increased by 1 unit, the percentage share of shadow financing would increase by
1.51%. The positive correlation follows Minsky’s theory which explains that
profitable firms are less likely to go bankrupt, and can avail for more debt at
cheaper rates of interest. Based on the trend of net profit among the companies
(figure 9), it shows that company profits have been continually increasing. Thus,
the higher the profit the companies make the more likely that the companies
would go into shadow banking activities, as it gains more confidence that these
firms would be able to pay back.
48
Moreover, the interest rates variable with a lag of two years was negative
and statistically significant at 2%. The variable had a coefficient value of – 2, this
implies that a 1% decrease in interest rates would cause a rise of shadow
financing by 2%. The negative relationship follows the credit cycle theory that
low interest rates stimulate an increase in borrowings from the shadow banking
system. The general increase in shadow banking among the real estate developers
may be due to the observation that the average informal interest rates of real estate
companies across the period have been declining over the years.
Macroeconomic variables were also considered in the regression and
posted significant results. Real estate loans from banks was positive and
statistically significant at 0.1%. It had a coefficient value of 0.21, which means
that if loans from banks increase by 1 unit, the percentage share of shadow
financing would increase by 0.21%. The negative correlation goes against the
theory that shadow financing activities are less likely to occur as more loans are
allowed to be lended out by banks at cheaper interest rates. In the case of the
Philippines, there is still an increasing growth of shadow financing activities
despite the increasing trend of real estate loans from banks. This may be due to
the actual observation that shadow banks are not subject to bank limits on loans
(Elliott, Kroeber and Qiao, 2015).
Lastly, land values had a negative relationship with shadow financing,
which goes against the theory that an appreciation of land values would indicate
high financing costs, thus causing companies to borrow more from other nonfinancial institutions. The negative correlation between land prices and shadow
49
financing in the real estate could mean that the developers mostly finance for mid
– income projects, instead of high-end segments where land prices are high. As
stated by KMC MAG Group Managing Director Michael McCulllogh (2014), it is
the middle income and low cost demand in Metro Manila that has kept the market
demand buoyant and occupancy rates high, while slower growth is observed in
the high – end segments. He also added that there is a growing middle – class that
is fueling the retail market, encouraging retail developers to pursue aggressive
expansion plans.
As of 2014, the SM Group planned to invest Php 38.8 billion,
concentrating on areas outside of Metro Manila. The group aimed at expanding its
mall portfolio to 7.5 million square meters, which is nearly half of the total retail
space in the country. Ayala Land Inc. planned at operating 500 convenience stores
within the next five years and has also entered a joint venture with Rustan's to
open the first Wellworth department store in Fairview Terraces (PR Newswire,
2014).
Projects that cater to mid – income groups usually have lower land prices.
In this case, land values had a coefficient value of -0.002, which means that a 1
unit decrease in the price of land would lead to an increase of shadow financing
by only 0.002%.
50
Table 3. Regression Results
In analyzing the factors that affect shadow banking in the real estate sector,
the panel regression reveals significant results (Appendix 10).
The R – squared serves as a statistical measure of how close the data are to
the fitted regression model. It can range between the value of 0 and 1, where a
value closer to 1 indicates that a greater proportion of variance is accounted for by
the model. In this case, the regression analysis displayed an R-squared value of
0.618, thereby explaining that about 61.8% of the total variations of shadow
financing can be explained by the independent variables. The remaining 38.2%
may be explained by the error terms. In this case, since the r-squared value is
small, it should be noted that this model cannot be used for forecasting future
trends of shadow banking. Thus, it mainly serves its purpose of determining the
factors that affect the percentage share of shadow banking to total assets.
The F – Stat tests the overall significance of the regression model. It tries
to evaluate the null hypothesis that all regression coefficients are equal to zero
against the alternative that at least one coefficient is not. The F-value is the ratio
of the mean regression sum of squares divided by the mean sum of squares. A
51
significant F – test indicates that the observed R – squared is reliable, and does
not contain any spurious results from the data set. Thus, the F – test determines
whether the proposed relationship between the dependent variable and the
explanatory variables is statistically significant. The Prob(F) is the probability that
the null hypothesis for the entire model is true ( that all regression coefficients are
zero). In this model, the F – stat having a value of 11 is greater than Prob (F) with
a value of zero. This would mean that the regression equation is valid enough in
fitting the data.
The Durbin – Watson statistic serves as a test for autocorrelation of
residuals (error terms) in a regression model. A DW value near 0 indicates that
there is a strong positive autocorrelation that exists, a DW value near 2 indicates
that there is minimal amount of autocorrelation, and a DW value near 4 indicates
that there is strong negative autocorrelation. Based on the result of the model, the
DW value is 1.93 which proves that the white test has minimized the presence of
autocorrelation in the model.
52
CHAPTER V
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
This thesis aimed to answer the questions: (1) How do shadow banking
activities occur in the real estate sector? (2) What is the estimated the share of
shadow banking activities among listed real estate developers in the Philippines?
(3) What are the main drivers that contribute to the growth of shadow banking
activities in the real estate sector?
This study revolved around these three questions which are rooted from
the notion that there is an emerging growth of unmonitored debt financing or
shadow banking particularly in the real estate sector. There have been various
studies as discussed in Chapter II explaining how shadow banking have occurred
in other countries and how its continuing growth led to certain systemic risks.
There has also been a similar study done in another country that tries to examine
the factors that affect the capital structure of their real estate developments. The
variables that were seen greatly significant were company size, as measured by
the total assets of each company and profitability. Other economists suggest that
certain macroeconomic conditions may also impact the shadow financing in the
real estate industry such as OFW remittances and gross domestic savings (GDS).
In determining the growth trend of shadow banking of listed developers in
the Philippines, the consolidated financial statements of the observed companies
were gathered to examine the values behind their debt financing activities from
2004 to 2013.
In analyzing the factors that could affect the growth of the
53
companies’ shadow banking activities, a panel regression random effects model
was used, along with structural vector autoregression (SVAR) which showed
significant results.
A. Summary of Results
Increasing Growth of Shadow Banking Activities
Based on the data collected, shadow banking activities which are
associated with supplier financing, in – house financing and private placement
financing have been generally increasing throughout the years. However, a more
striking growth occurred from periods 2011 to 2013. This may be due to the surge
of demand on residential properties that occurred during this period. This then
confirms the notion that there is indeed an increasing trend of shadow banking
activities in the real estate sector.
In generating the model that determines the factors behind shadow
banking growth, the variables (assets, profit, real estate loans from banks, land
values and interest rates) were seen statistically significant. The model also has an
r – squared value of 0.618, which means that 61.8% of the variance of shadow
financing can be explained by these variables.
Other macro variables such as remittances, exchange rates and
gross
domestic savings were dropped as they were statistically insignificant, which
means that these variables may not have an impact or have lesser effect on the
shadow financing activities of real estate developers.
54
B. Conclusion
This study has proven that there is indeed a rising trend of shadow
banking activities in the real estate sector, particularly visible among listed real
estate developers in the country. This study has also empirically proven that
industry factors such as profit and interest rates post significant effects toward the
growth of shadow banking. Past values of shadow banking can also influence the
present shadow banking activities in the sector. Other factors such as land values
and loans from banks are also seen significant. Whereas in the Philippine setting,
most developers associate their financing activities to mid – income segments
where the value of land is low, thus having low property prices. Moreover, the
supply of loans from banks can also affect shadow banking, although it has also
been observed that shadow banking activities are not subject to bank limits on
loans. Other macroeconomic factors such as OFW remittances, exchange rates
and gross domestic savings were seen to be insignificant, which means that these
variables may not have that much impact on shadow banking in the real estate.
C. Recommendation
The research that has been done for this thesis has revealed significant
findings on which future studies could find it beneficial. Further studies on
shadow banking, particularly in the real estate sector, may include a larger sample
size, or longer periods of observation to fully capture the shadow banking
conditions in the sector. Future studies may also try using only shadow banking
(not as a portion of total assets) as a dependent variable in running a regression to
55
accurately determine the factors behind its growth. Future researchers can also try
other methods in determining the factors that drive shadow banking.
Real estate firms around the country could also make use of this study in
analyzing the risk factors that affect the real estate market. Given the increasing
trend of shadow banking in the real estate industry, the firms could re-evaluate
their debt financing schemes and take precautionary measures in facilitating credit
intermediation. Furthermore, the BSP could implement more stringent policy
measures that would contain the build up of shadow banking activities
particularly in the real estate market, so as to prevent certain systemic risks.
56
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APPENDIX
Appendix 1: List of Observed Companies
SM Prime Holdings, Inc., (SMPH)
Megaworld Corp., (MEG)
Ayala Land, Inc., (ALI)
Robinsons Land Corp.,
Filinvest Land, Inc., (FLI)
Rockwell Land Corp., (RLC)
Vista Land and Lifescapes, Inc., (VLLI)
Sta. Lucia Land, Inc.
Shang Properties
61
Appendix 2: Breakdown of Supplier Financing
62
63
Appendix 3: Breakdown of In-house Financing
64
65
Appendix 4: Breakdown of Private Placement Financing
66
67
Appendix 5: Total Supplier Financing
68
Appendix 6: Total In-house Financing
69
Appendix 7: Total Private Placement Financing
70
Appendix 8: Total Assets
71
Appendix 9: Total Profit
72
Appendix 10: Panel Regression Data
73
74
75
76
77
78
79
Appendix 11: Results of Regression on %Shadow Financing with White
Cross-Sectional Test
80