Income Growth and Pricing of REITs
by
Shu-chun Lee
B.A. Land Economics
National Chengchi University, Taipei (1995)
SUBMITTED TO THE DEPARTMENT OF ARCHITECTURE IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN REAL ESTATE DEVELOPMENT
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 1998
@ 1998 Shu-chun Lee
All Rights Reserved
Permfssbn tO reptoduc
r o
distribute publcly papran
0eotroNc copies of fLus mu
dOCUmenftn wholeq or In pcst.
Signature of Author........................................................................
Department of Architecture
July 31, 1998
Certified
by ..................................
t ......-...............................................................
William C. Wheaton
Professor of Economics
Thesis Supervisor
Accepted
b y ...........................................................................................................
William C. Wheaton
Chairman
Interdepartmental Degree Program in Real Estate Development
MASSACHUSETTS INSTITU11t
OF TECHNOLOGY
OCT 2 3 1998
LIBRARIES
ROTCH
Income Growth and Pricing of REITs
by
Shu-chun Lee
Submitted to the Department of Architecture on August 7, 1998
In partial fulfillment of the requirements for the Degree of
Master of Science in Real Estate Development
ABSTRACT
Office REITs have been doing well recently in terms of exhibiting high dividends and total
returns. Since the payout dividend depends on a REIT's income, positive income growth
can increase shareholder wealth and therefore keep investors interested in its stock. So far,
the primary channel for REITs to grow fast is by arbitraging the spread between their cost
of capital and their capitalization rate for acquisitions. Because a REIT's cost of capital
reflects investors' pricing of it, positive stock performance enhances investors confidence
and enables the REIT to have continuous access to public capital.
This study examined the components of REIT growth income and the determinants of a
REIT's cost of capital. Evidence of office REIT achievements in the current market is
first introduced, along with the financial details for each office REIT used in the analysis.
Considering the financial data, the primary question was whether the income growth of a
REIT is attributable to its size, its acquisition activities, and/or the rental growth of its
properties. Additionally, several variables were listed and investigated for influences on
the public capitalization rate of a REIT.
The results revealed that the operating income of a REIT is unreliable as an indicator for
evaluating a REIT, and the pricing of a REIT is driven by its debt ratio, its acquisition
activities, the rental growth of its real estate portfolio, and current market conditions.
Finally, it can be concluded that investors and capital providers do not look backward on a
REIT's historical operating income. On the contrary, they are forward-looking and reward
REITs with a lower cost of capital as long as the REIT owns good real estate, which means
that investors expect REITs to display their good acquisition abilities in the markets where
they are likely to experience rental growth in the future.
Thesis Supervisor: William C. Wheaton
Title: Professor of Economics
Chairman, Interdepartmental Degree Program in Real Estate Development
Acknowledgements
I sincerely thank Bill Wheaton for his guidance of completing this thesis and Ross Dom for
his editing assistance. Special thanks go to Tod McGrath and Tim Riddiough for taking
time answering my questions. Also, I want to thank Jay Hooper who has been helping me a
lot both academically and personally. Last but most importantly, I would like to thank my
family for their tremendous support.
Table of Contents
2
ABSTRACT.....................................................
6
CHAPTER ONE: INTRODUCTION............................................................
6
1.1 Overview of REITs.............................................................
1.2 Recent Highlights of the Office REIT Market..........................................7
-- 11
1.3 Purpose of This Study...............................................................--CHAPTER TWO: DATA DESCRIPTION....................................................14
14
......
2.1 Selection of the Data..........................................................
15
2.2 D isclosure of the D ata..................................................................
2.3 The formation of Database..................................................18
CHAPTER THREE: COMPONENTS OF REITs INCOME GROWTH................23
3.1 External Growth.......................................................23
25
3.2 Internal Growth....................................................
3.3 Income Growth Equation and Methodology............................................27
3.4 Description of Regression Variables.....................................................30
3.5 Regression Results and Analysis..........................................................35
...... 39
3.6 Comparison..............................................
CHAPTER FOUR: WALL STREET'S PRICING...............................................41
4.1 Pricing Model and Methodology..................................................41
4.2 Description of Regression Variables......................................................43
4.3 Regression Results and Analysis..........................................................52
4.4 Comparison....................................................59
60
CHAPTER FIVE.................................................
60
5.1 Conclusion..................................................
64
............................
APPENDIX.......................................
Appendix A: Alexandria Statements of Operations and Balance Sheets..................64
Appendix B: Office REITs Financial Database..................................................67
Appendix C: 54 MSA Rental Index.......................................................70
Appendix D: External Growth Performance...................................................73
Appendix E: Internal Growth Performance......................................................76
Appendix F: Set- 1997 Data Only Regression Data and Outputs..........................79
Appendix G: Set- All Available Data Regression Data and Outputs..........................81
Appendix H: Set- Restricted Data Regression Data and Outputs..........................83
Appendix I: Set 1 Regression Data and Outputs.............................................85
Appendix J: Set 2 Regression Data and Outputs................................................89
Appendix K: Set 3 Regression Data and Outputs...............................................94
REFERENCES....................................-...................................................
99
Tables and Figures
Table 1-1: Historical Offerings of Securities by REITs...................................7
Table 1-2: List of Office REIT IPO within last two years.................................8
Figure 1-1: Total Capital Raised by REITs..................................................8
Table 1-3: Performance of REITs...............................................................9
Figure 1-2: Industry Market Capitalization...................................................10
Figure 1-3: Acquisitions by REITs.........................................................10
Table 2-1: Office REIT Study Sample......................................................15
Table 2-2: Alexandria RE Equities' Office Properties...................................19
Table 2-3: Alexandria RE Equities' Financial Information..............................20
Table 3-1: The Description of Income Growth Variables...................................31
Table 3-2: The Summary of Regression Results..........................................36
Table 4-1: The Description of All Variables...............................................46
Table 4-2: The Summary of Set 1 Regression Results...................................53
Table 4-3: The Summary of Set 2 Regression Results...................................55
Table 4-4: The Summary of Set 3 Regression Results...................................56
CHAPTER ONE
1.1 Overview of REITs
In the 1990's, Real Estate Investment Trusts (REITs) have been catching investors'
attention. REITs are tax-exempt entities specializing in real estate investment, and in order
to be qualified as a REIT, they have certain restrictions to follow concerning asset
requirements, income requirements, and, most importantly, dividend payout requirements.
Their stocks are usually publicly traded and have been performing well over the past few
years.
From Table 1-1, we can see the historical offerings of securities by REITs increased from 9
offerings in 1982 to 463 in 1997, the peak in the history of REITs.
Since REITs are
required to pay out 95% of their taxable income to maintain their REIT status, the periodic
trip to the public capital markets becomes their main resource to fund expansion. Because
they face so much competition in acquiring public capital, REITs have to outperform and
distribute high dividends; otherwise, they will be denied the access to capital.
So far,
REITs have done well given the recovering real estate market, normal yield curve and
improving economy, and it is likely that the typical REITs will still be attractively valued
relative to the broader equity market going forward.
Table 1-1
Historical Offerings of Securities by REITs
INITIAL
All
SECONDARY UNSECURED MORTGAGE
DEBT
EQUITY
Total
No.
Total
No.
($ Mil)
($ Mil)
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
9
23
18
59
63
50
37
34
24
35
57
141
145
195
225
463
435
747
1,438
4,271
4,669
2,929
3,069
2,441
1,765
2,289
6,515
18,327
14,721
12,493
17,456
45,271
3
4
6
29
20
12
13
11
10
8
8
50
45
8
6
26
315
159
140
2,792
1,204
634
1,374
1,075
882
808
919
9,335
7,176
939
1,108
6,297
No.
Total
Total
No.
Total
No.
($ Mil)
($ Mil)
($ Mil)
115
5
438
15
173
8
413
17
624
17
733
15
785
13
722
15
389
8
786
20
1,055
24
50 3,856
52 3,945
93 7,321
139 11,201
292 26,378
BACKED
5
1
150
4
4 1,125
939
12
316
5
248
4
335
6
150
3
294
4
169
3
820
9
18 1,353
26 2,094
73 3,444
76 4,754
134 10,568
0
0
0
1
21
19
5
5
2
4
16
23
22
21
4
11
0
0
0
127
2,525
1,314
574
494
200
525
3,722
3,782
1,507
789
328
2,029
Source: from NAREIT on line information
1.2 Recent Highlights of the Office REIT Market
REITs are delineated most typically by the type of product in which they specialize.
Among several categories of REITs, office REITs have performed particularly well in the
competition for new equity capital, as reflected by the growth in number of office REITs
over the last two years shown in and Table 1-21 and Figure 1-1.
1As of June 30, 1998, there are 20 office REITs listed in NAREIT Handbook.
List of Office REIT Initial Public Offerings within last two years.
Table 1-2
as of June 30, 1998
Alexandria RE Equities Inc.
Arden Realty Group, Inc.
Boston Properties, Inc.
Equity Office Prop. Trust
SL Green Realty Corp.
Great Lakes REIT, Inc.
Harvard Property Trust, Inc. Tower Realty Trust Inc.
Kilroy Realty Corp.
Prentiss Properties Trust
Figure 1-1
$ 27.69 Billion Total Capital Raised (January 1, 1998- June 30, 1998)
Omeingets,8264
LodgMngi
Resor"
Owfg.d~B
aIversifoI1
Offfeflngsi2124.2
Hea C"28
Office
Mortgage
~
0ogeringaysgas0o0a
Backed
ofnMes
Resienta41
so
$2,000
88000
"W
2
8.000
$10,000
$12000
Is uanal
Source: from NAREIT on line information
Also, as seen in Table 1-3 and Figure 1-2, office REITs had the second highest return
among all REITs, and had the second highest total market capitalization. In addition, office
REITs show the highest percentage of new acquisitions, as seen in Figure 1-3. Based on
the presented evidence, the office REIT sector appears to be a favorite of investors.
Furthermore, in the current economic and real estate cycle, office remains the most likely
sector to generate significant earnings growth.
Table 1-3
Performance of REITs
Total return 1997 No. of REITs Market capitalization
( $ Thousands)
ALL COMPANIES
DIVERSIFIED
HEALTH CARE
SELF STORAGE
INDUSTRIAL/ OFFICE
---INDUSTRIAL19.02%
---OFFICE
---MIXED
RESIDENTIAL
---APPARTMENT
---MANUFACTURED HOMES
RETAIL
---REGIONAL MALLS
---STRIP CENTERS
---FREE STANDING
---OUTLET CENTERS
LODGING/RESORTS
SPECIALTY27.31%
MORTGAGE BACKED SECUR.
Source: from NAREIT on line information
18.86%
24.48%
13.32%
3.41%
27.48%
29.00%
27.91%
16.33%
16.04%
18.12%
16.99%
21.44%
13.69%
N/A
0.88%
30.05%
-3.05%
215
28
14
6
39
13
20
6
32
28
4
51
30
11
7
3
14
7
24
159,927,494
15,296,489
12,435,2
6,132,260
40,833,700
363,901
23,288,957
7,80,842
26480,594
24,456,670
2,023,924
29,282,647
13,671,393
10,778,480
3,467,701
1,365,073
18,473,783
1974507
9,018,501
Figure 1-2
$ 159.93 Billion Industry Market Capitalization As of June 30, 1998
LodgingRorte
f1.M%
Diversified
9.56%
Healh Care
Residential
1.23%
Retail
1831%
InduawO
&64%
Source: from NAREIT on line information
Figure 1-3
$ 32.46 Billion Acquired by REITs As of June 30, 1998
RU
23.06%
SeUf Storage
0A%
Health Care
0.74%
Source: from NAREIT on line information
IndustristOffice
3&42%
1.3 Purpose of This Study
Wall Street is attracted to real estate because some REITs have been able to generate
significant overall returns and dividends in recent years. The high dividend yields paid by
office REITs in the 1990s are attributable to a recovering office market. The main factor
enabling REITs to pay high dividend yields is capital rate arbitrage. Capital rate arbitrage
is possible where a REIT's cost of capital from Wall Street is lower than the capitalization
rate reflected by its properties.
Nonetheless, past high returns do not guarantee the future growth of an office REIT, or of
any stock for that matter. On the contrary, these past high returns put REITs in a more
uncertain (riskier or dangerous) position of having to keep up the promised or implied
returns of the past. As the opportunities of accretive investment become limited in the
current office market, REITs are losing a primary instrument for earning profit.
Since the real estate market has fully recovered, huge growth opportunities enjoyed by
REITs over the past several years are unlikely to happen in the future. Will investors be
rational enough to adequately adjust their expectations of high growth in the future? Will
REITs therefore be appropriately priced? Or will investors punish REIT stock prices when
they find out the huge growth opportunities for REITs no longer exist? In order to maintain
a low cost of capital in the future, a REIT will have to successfully compete for capital by
exhibiting continuous income growth and consistent payout of expected dividends.
Most investors believe office REITs will likely generate high income growth in the current
real estate cycle. However, questions arise regarding what factors drive a REIT's income
growth and what determinants affect the cost of capital for office REITs. Therefore, it has
become essential to understand what attributes contribute to the evaluation of REITs.
In order to identify the factors involved in office REIT pricing, a framework for examining
and calibrating the pricing of a REIT is presented here. We hypothesize that the dividend
growth is the key component on which investors price a REIT, especially since investors'
interests are heavily related to the dividend. However, the focus should be on looking
deeply into the sources that make up the dividend growth.
To be capable of paying a high dividend, a REIT can grow its income through internal
growth and external growth. Generally speaking, the internal growth depends on a REIT's
size, reputation, management ability and the performance of the existing properties. The
external growth is mainly from acquisitions and/or new development, since buying or
developing profitable properties tends to benefit a REIT's overall income. Hence, this
study also looks into the growth components of REITs, and hopes to find more reliable
evidence to estimate the future performance of office REITs.
In essence, this study examines the valuation and performance issues of office REITs in
today's market, and especially focuses on the determinants of the public capitalization rate
for office REITs. The sample database used to analyze REITs is described in the following
chapter. Then, in Chapter Three one equation is created to explain the reported income
growth components, and in Chapter Four another equation is created to explore a REIT's
cost of capital. Finally, a summary of the findings and thoughts is presented in Chapter
Five.
CHAPTER TWO
2.1 Selection of the data
As stated earlier, our research and analysis focuses on office REITs only. Information was
collected for the years 1992 through 1997. The original sample consisted of all publicly
traded REITs owning office properties and listed in the National Association of Real Estate
Investment Trusts (NAREIT) on-line Database. However, REITs that have over 70% of
their revenues derived from office properties were also considered.
The original sample included one hybrid REIT, Realty Refund Trust, but it was excluded
because it was mainly a mortgage REIT and owned very few properties. A synopsis of the
final sample includes 20 Office REITs and is shown in Table 2-1.
Office REIT Study Sample
Table 2-1
REITs
IPO*
Location
Type
Stock
Ticker
Exchange: Symbol:
Alexandria RE Equities Inc. 6/97 Pasadena, CA
Traditional NYSE
ARE
Arden Realty Group, Inc.
10/96 Beverly Hills, CA UPREIT
NYSE
ARI
Boston Properties, Inc.
6/97 Boston, MA
UPREIT
NYSE
BXP
Brandywine Realty Trust
86 Newton Sq., PA
UPREIT
NYSE
BDN
CarrAmerica Realty Corp.
10/92 Washington, DC DownREIT NYSE
CRE
Cedar Income Fund, Ltd.
85 Cedar Rapids, IA Traditional Over the counter
CPP
81 New York, NY
UPREIT
NYSE
Cornerstone Properties Inc.
Crescent Real Estate Equity
94 Ft. Worth, TX
UPREIT
NYSE
CEI
UPREIT
NYSE
EOP
Equity Office Prop. Trust
7/97 Chicago, IL
G&L Realty Corp.
12/93 Beverly Hills, CA UPREIT
NYSE
GLR
Great Lakes REIT, Inc.
4/96 Oak Brook, IL
UPREIT
NYSE
GL
Highwoods Properties, Inc.
6/94 Raleigh, NC
UPREIT
NYSE
HIW
Kilroy Realty Corp.
1/97 El Segundo, CA
UPREIT
NYSE
KRC
Koger Equity, Inc.
86 Jacksonville, FL Traditional ASE
KE
Mack-Cali Realty Corp.
8/94 Cranford, NJ
UPREIT
NYSE
CLI
Nooney Realty Trust, Inc.
85 St. Louis, MO
Traditional NASDAQ NRTI
Parkway Properties, Inc.
81 Jackson, MS
UPREIT
NYSE
PKY
Prentiss Properties Trust
10/96 Dallas, TX
UPREIT
NYSE
PP
UPREIT
NYSE
SLG
SL Green Realty Corp.
8/97 New York, NY
Tower Realty Trust Inc.
10/97 New York, NY
UPREIT
NYSE
TOW
* Some companies have changed their status or names so the IPO time of those
companies means their original companies' IPO time or formation time.
Source: from NAREIT analyst database on line and handbook on line
2.2 Disclosure of the Data
We traced each office REIT's historical performance from 1992 through 1997. Since the
IPO date of most office REITs occurred in the past few years, the available data is limited.
Public REITs are governed by the Securities and Exchange Commission (SEC) and are
required to file "10-K" reports every year. The SEC's definition of a 10-K report is as
follows:
"A public file containing material financial and
business information on the company for use by
investors and others, and an obligation of the company
to keep such public information current by filing
periodic reports."2
Because a REIT's share price is related to its shareholders' wealth and reflects investors'
evaluations of a REIT's future income growth, both internal and external, the details of a
REIT's income growth can reveal the factors underlying the pricing a REIT.
The
availability of this kind of information also is a key component of investors' decisionmaking processes.
In order to get more observations, we also looked at the "S-11" report filed by certain
REITs that went public during the 1992 to 1997 period. An S-11 report is basically a
"Prospectus", and the definition by the SEC is as follows:
"This form is used to register securities of certain real
estate companies, including real estate investment
2
From SEC EDGAR (Electronic Data Gathering, Analysis, and Retrieval system) database- form definitions.
16
trusts."3
Because of the detailed reporting requirements of 10-K and S-11 reports, the necessary
information relating to the income growth, as well as the total capitalization, of REITs was
readily available.
This included a list of properties each REIT owns in different
metropolitan areas.
As publicly-traded companies, REITs are required to disclose their financial statements
including consolidated balance sheets, consolidated statements of operations, and
consolidated statements of stockholders' equity. This important information flows from
these public real estate company disclosures and acts as a governor on the industry, because
both investors and capital providers have more bases to price REITs and price risk than
they have when they engage in direct real estate investment where such public disclosure
are not required.
In addition to the pricing advantages, this information flow also changes the valuation base
in the real estate environment from the traditional long-term IRR to the yearly return
revealed from 10-K or 10-Q (quarterly) reports filed by REITs. There is some concern that
these new information flows may be detrimental, because investor emphasis on (short-term)
yearly returns might drive REITs to devote their operations to raising annual returns, and
possibly abandon strategies for long-term return and durability inherent in an asset class
3Also, from EDGAR database- form definitions.
such as real estate. Achievement of a company's long-term goal would be hindered if too
much focus is placed on short-term returns.
Although this short-term focus on information disclosure may drive investors to ignore a
REIT's long-term operational ability and instead focus on short-term profits, information
disclosure enables investors to scrutinize REITs.
This level of accountability adds to
investor confidence levels when investing in REITs, and if REITs can demonstrate good
returns for a few years, they will be rewarded because investors will then begin to
anticipate future earnings growth based on the fact that REITs have to pay out dividends
and cannot retain those earnings to finance further expansion.
The disclosure of REIT income details is critical for investors to understand and invest in
REITs because the risk of uncertainty is reduced. Also, the publicity of REIT financial
statements allows investors to react quickly so that possible losses will be relatively
minimized.
2.3 The Formation of Database
Our data source is mainly the 10-K reports filed by each REIT, and for explanatory
purposes, the 1997 10-K report of Alexandria Real Estate Equities, Inc. is used as an
example to describe the derivation of the items in our database, including office square feet,
operating income (01), debt, total market capitalization, size and market rental index.
Office Square Feet: "Item 2" in Alexandria's report provided the total square feet of office
it owns in all office markets, as well as all major locations of its properties. Since this
REIT was not required to report this information until they went public in the middle of
1997, their S-11 report was reviewed to acquire more available data. Table 2-2 lists the
relevant data.
Table 2-2
Geographic area:
San Diego
San Francisco
Seattle
Washington, D.C.
Total
Total rentable square feet Total rentable square feet
as of 12/31/1997
as of 12/31/1996
314,779
333,311
147,777
208,877
213,397
213,397
992,252
250,117
926,070
1,747,837
Operating Income: Operating income (01) is merely the total revenues minus the total
expenses, plus debt service from the consolidated statements of operations of Alexandria.
A sample statement is attached in Appendix A. Related figures in the statements are shown
in Table 2-3. Interest expense is included in operating income because this item is a major
operating expense and, therefore, tends to cause a variation in operating income. This
calculation of 01 enables us to understand the performance of its property operations
despite its leverage level.
Total Market Capitalization and Debt: The data on debt and total market cap of each
REIT from 1992 to 1997 are mainly from "the S & P Stock Reports, June 1998" .
However, both total market capitalization and debt data for Alexandria in 1997 were not
available in the reference book. By referring to the balance sheets in its 10-K report
attached in Appendix A, the long-term debt5 , including secured notes payable and
unsecured line of credit, can be derived.
On the other hand, we found the closing price of Alexandria's stock on December 31, 1997,
as well as its number of outstanding shares. These were found in "the S&P Daily Stock
Price Records: NYSE, ASE and NASDAQ". Then the stock price was multiplied by the
outstanding shares to get the equity capitalization, and this was added to the long-term debt
to obtain the total market capitalization.
We finally compiled this data and the numerical
example is shown in Table 2-3.
Table 2-3
Alexandria RE Equities- Financial information as of December 31, 1997
Total revenues
$ 34,846,000
Total expenses
$ 37,643,000
Debt service
$ 7,043,000
Operating income
$ 4,246,000
Long-term debt
$ 70,817,000
Stock price
$ 31.56
Outstanding shares
11,405,000
Total market cap
$ 430,759,000
The market cap data from "S&P" does not include the operating partnership units of a REIT.
5 Instead of total debt, long-term debt was used which is, in general, property-related debt.
6 Total market capitalization of a REIT fluctuates due to the changing equity cap caused
by different daily
stock prices.
4
Dropping odd observations: Although financial details and investment performance can
be better monitored with REITs through 10-K reports, the different accounting methods
used by different REITs detracts from the credibility of the information.
Thus, some
observations having unusual numbers were dropped, and the final database containing the
above items for the 20 office REITs was calculated. A copy is shown in Appendix B.
Size: Instead of absolute size, relative size within office REIT market was used for this
variable, and according to the office square feet data in Appendix B, it was calculated as the
equation presented below:
Size: Sij
Sf,
N=
N,Z=1isf,,
i: number of REITs
t: year t
Nt: # of available office space data in year t
Size indicates a REIT's total office square feet divided by the average office square feet
owned by all office REITs. For example, the size of Alexandria in 1997 is 0.1524 meaning
that its total office space is equal to 15.24% of the average of office space owned by all
REITs. As another example, the size of Arden in 1997 is 1.2945 meaning that its office
space is 29.45% more than the average amount of office space owned by all REITs in the
market. The total results are also shown in Appendix B.
21
Rent Index: In addition to the SEC reports, Torto Wheaton Research provided access to
office rent data in 54 Metropolitan areas, matching property locations for each REIT-owned
property from 1991 to 1997, shown in Appendix C. The last city named "Nation" means
the overall office market in this country. One way to measure whether a REIT is successful
is to distinguish its growth from its real estate portfolio.
This rental data enabled
comparison of the differences in performance between each REIT and the office market in
the same MSA (Metropolitan Statistical Area).
In this chapter, the formation of our data was described and the database was completed as
shown in Appendix B. This data enables us to explore and interpret the components of
REITs' income growth in the next chapter, and furthermore examine the determinants of
REITs' pricing in Chapter Four.
CHAPTER THREE
A REIT's stock price is a reflection of investors' valuations of it, so it is understandable
that the return and the performance of a REIT have commonly been the investors' basis to
measure a REIT. In particular, income growth determines how high dividends will be and,
therefore, how much stockholders can benefit from owning the stock.
In addition to the internal growth opportunities of every private or public company, REITs
also have external growth opportunities which are often built into REIT stock prices.
Therefore, we first describe some factors affecting external and internal growth, and then
build an equation to find the relationship between those factors and the overall income
growth through several sets of regressions.
3.1 External Growth
REITs have a distinct advantage over private real estate investment, as REITs are able to
get lower cost public capital due to the external growth opportunities in the market. At the
present time, two of the most feasible ways to realize this significant external growth are
through external asset development and acquisition.
Because REITs have access to public capital and the cost for one dollar of income growth
in REITs is less than that in private real estate companies, primarily due to the external
growth expectations on REITs from the public, the more acquisitions a REIT makes, the
more cost savings a REIT gains in the relative basis compared to a private company. The
REIT's income can therefore grow as long as it does not make bad acquisition decisions.
On the other hand, REITs can not retain their earnings for growth so they have to fund their
acquisitions through issuing stocks in the public market.
Consequently, the continuous
acquisitions is not only beneficial, but necessary for a REIT.
In addition to property acquisition, mergers with and acquisitions of compatible real estate
firms are increasingly common ways for REITs to realize external growth opportunities.
Mergers involve legislative and complicated accounting issues, however, and our purpose is
to focus on a REIT's growth in the quantity of their portfolio, not on the discussion of
which acquisition method helps REITs more.
In order to realize a REIT's acquisition condition, the 20 office REITs were checked for
change in total office square feet (A sf) and change in outstanding shares (A shares). The
results shown in Appendix D indicate that REITs generally expand their existing real estate
portfolio by 77.45% annually, and issue 50.73% new shares for financing their acquisitions
every year. If two REITs (Cedar Income and Nooney) which have not been growing at all
are excluded, the growth rates for REITs become 95.97% and 68.74% respectively. The
standard deviations of the two categories are 121.92% and 81.93% respectively.
These
high variations are reasonable because REITs did their acquisition based on the market
conditions and rental growth rates which were different in those markets. Therefore, you
would see a sudden growth in a REIT's office space, while another REIT has no
acquisitions at all.
The access to public capital has been the REITs' secret to fast grow and, owing to the
current prosperous office market, investors' continuous confidence in office REITs will
further REITs' acquisitions. Only acquisition represents a significant opportunistic way to
derive a REIT's external growth and increase its earnings. Size, management strength and
reputation are auxiliary features helping a REIT to be in an advantageous position to
acquire assets.
3.2 Internal Growth
The internal growth opportunity of a REIT is to increase cash flow from its existing
properties in desirable submarkets that are experiencing rising rents, low vacancy rates and
increasing demand for office. In other words, a REIT can pursue its internal growth by rent
escalation, re-leasing with higher rent or improving occupancy rates of its properties
through its active management. After all, effective and high quality management services
allow a REIT to ask its tenants for higher rent.
Other than active management, a REIT can control its management cost and produce cost
efficiencies by having an experienced work force, or it can capitalize on economies of scale
due to its large size (if, indeed, it is a large REIT), or geographic focus on those of its
property markets that are experiencing increasing rents.
The ultimate goal of internal
growth for a REIT is to achieve rental rates at the higher end of its markets and to offer
higher dividends for stockholders.
Factors that contribute to this growth also include a REIT's control of sites in its core
markets (market power) and the reputation of the REIT's management team gained through
the stability and strength of the REIT's existing portfolio of properties.
A good
management team can benefit a REIT by showing its ability to buy profitable properties to
seize the external growth opportunities in good markets. The ability to acquire existing
underperforming assets caused by poor management or below-market leases can potentially
result in huge growth, and this particular method has contributed largely to REITs' high
growth in the past few years, since REITs bought a lot of distressed properties form RTC in
early 1990s.
Besides, through the in-depth market knowledge and management experience in the
primary markets where it currently operates, a REIT can dominate the markets and become
one of the market leaders in establishing rent and other business terms in markets that are
experiencing growth. Subsequently, a REIT builds better access to competitively priced
capital and is therefore very likely to succeed.
By looking at the 20 office REITs' historical changes in operating income shown in
Appendix E, the first observation is that REITs grow their average income $ 14,426,000
every year (the standard deviation is $ 24,361,000).
We also looked at the change in
operating income per square foot (A OI/ sf), which is calculated by taking total change in
operating income divided by the total office square feet as of December 31 in each year.
The mean of A OI/ sf is negative $ 0.27 (the standard deviation is $ 3.08). The high
standard deviation is presumably caused by the wild range of the change in operating
income and the different size of REITs, which produce different levels of income.
Meanwhile, measuring A OI in per square foot basis rather than per share basis is due to our
major focus on the operating income from REITs' properties not on their equity earnings.
3.3 Income Growth Equation and Methodology
A REIT's total income growth consists of the rental growth rate of its portfolio and the
earnings from its new acquisitions by executing its internal and external growth strategies.
The management of a REIT is also a critical component of income growth in terms of
growing itself in size and making accretive acquisitions in good markets.
Hence, we
summarize the concepts to that "Change in operating income of a REIT is a function of its
properties' performance, management efficiency and acquisition/ development activities."
and form a numerical equation like the following:
Change in 01 =f(outperformance rate, size, % change in sf)
Y= a+bXl+cX2+dX3
A
,
(A R
A Rn,,fS(,,,
t%^sfi)
0 A
Ri,,.: rent of REIT i in market m in year t
Rn: rent of the national market
ASF: average square feet
i: number of REITs
t: year t
The literature explanation indicates that a REIT's ability to grow in income may be
attributable to the following factors:
-
the performance of properties- outperformance rate, implying internal growth;
e
management efficiency, implying both internal and external growth;
e
acquisition and/ or development, implying the external growth;
Meanwhile, the numerical equation is the formation of the following variables (detailed
explanation will be in the next section):
-
Y: the change in operating income per square foot of a REIT between two
years;
-
Xl: an index of a REIT's rental growth at each year minus the overall
markets' rental growth at the correspondent year;
e
X2: the percentage of a REIT's total office square feet at one year to the
average of the sum of all existing REITs' total office square feet at the same
year;
e
X3: the percentage change in a REIT's total office square feet in the given
year compared to its previous year's;
By analyzing the regression results from this equation, we first want to testify that whether
growth in operating income is the key component for investors to value a REIT's
performance.
As we stated earlier, the database contains 20 office REITs' financial
information and we, therefore, create the regression database.
Continuously, we run the first regression containing only 1997's data7 to see the
relationship between those variables cross different REITs.
Followed by the second
regression with all available data through 1992 to 1997, we would like to see the overall
connections between them not only cross REIT, but also cross years. However, due to the
limitation of obtaining some data two years before the time a REIT went public, we
eliminate some observations which comprise errors affected by incomplete data, and use
the 42 remaining observations as the restricted data of the third regression.
7 Because seven REITs went public in the middle of 1997, and in order to make those observations available,
we assume that those REITs had the same properties in the end of 1995 as of they had in the end of 1996. The
effect of this assumption might be minor because public REITs tend to issue stock or debt to fund big
acquisitions, which are not likely the case in private REITs or private companies.
29
3.4 Description of Regression Variables
AOI/ per square foot
Ahe,
01,,
per ASF
avg. sf 0
0sf
a
where avg.
sf
(sf f+s 0-1)
2 -(
A 01/ per square foot is the change in operating income per averaged square feet (ASF). By
using this as the dependent variable, we want to focus on how much of the change in
operating income is attributable to the three independent variables and understand how
much operating income a REIT extracts out of its properties without considering its
leverage (because operating income included debt services).
Referring to the ASF, we calculated this by assuming the subsequent acquisitions of each
REIT occurred in the middle of each year as a result of the limitations in getting an accurate
acquisition date for each year's acquisitions because acquisition dates are not required to be
reported in 10-K reports submitted by each REIT.
Besides, using change in operating income as the variable is a conservative way to value a
REIT's performance since the 01 includes the depreciation and amortization expenses
(D&A exp.), which is normally exclude from the FFO (Funds From Operations8) because it
is a non-cash flow expenses.
However, due to some controversy about the D&A exp.
excluding from FFO, we believe, the operating income variable is feasible.
A brief description of each independent variables, as well as an explanation of what
variables are intended to measure, follows the Table 3-1:
Table 3-1
Independent
Variables
Expected sign
Description
of coefficient
Xl -Outperformance The difference between total return growth of a REIT's
rate
properties and the national market rental growth.
X2-Size
The percentage of a REIT's total office square feet to
+
+
the average of all REITs' total office square feet.
X3-% change in sf
Percentage of a REIT's yearly change in its total office
+
square feet.
Funds From Operations means net income (computed in accordance with generally accepted accounting
principles), excluding gains (or losses) from debt restructuring and sales of property, plus depreciation of real
property, and after adjustments for unconsolidated entities in which the REIT holds an interest. Adjustments
for these entities are to be calculated to reflect Funds from operations on the same basis. (From NAREIT
White Paper on Funds from Operations, NationalAssociation of Real Estate Investment Trusts, March 1995.
8
31
X1- Outperformance Rate
Outperformance rate = Market Index - National rental growth
R,- R,-1)
R
I
Sf
Z=1
sf.
m=1 t-,
Rn, -Rn-
Rn,-1
m: MSA
n: number of MSAs
We first created the market index of each REIT. This index is the cumulative percentage of
rental growth of a REIT's portfolio in each MSA. All rental data contained in this equation
is the overall market rent in each MSA provided by Torto Wheaton Research, not the actual
rent earned by each REIT. The market index represents a REIT's rental growth in its
existing portfolio. For example, market index 97 of Alexandria revealed the rental growth
of the properties it owned in 1996, excluding the rental growth realized from its new
acquisitions in 1997.
We are deriving the difference between the index and the national office rental growth rate,
and the difference represents how much percentage a REIT outperformed the average
market growth, and we believe this outperformance rate undoubtedly contributes to a
REIT's total income growth; therefore, we are expecting a positive sign of this variable.
X2- Management Efficiency
We translate the management efficiency factor into the size variable, which is calculated in
the previous chapter. And agreed on the following opinion:
"The indirect real estate investment portfolio is based
on the ability of management to profit from the
economies of scale and scope that are created from
multiple property management."
we build economy of scale into this variable, and assume that bigger size captures more
cost savings. Also, being able to exist in a competitive real estate industry today, bigger
size implies superior management. On the other hand, based on the following paragraph:
"The internal rate of return is compared to the cost of
capital to determine whether the acquisition is
beneficial to the corporation. Management controls the
internal elements of any business. The control of the
internal elements allows
management to
create
competitive advantagesor distinctive competencies that
generate strategic advantages over the external
9 By Phillip S. Scherrer, "Valuing Management for Real Estate Investments" , The Real Estate Finance
Journal/Winter 1997.
uncontrollable elements.
Management's ability to
perform in the future and generate shareholder or
investor wealth depends on its ability to implement the
strategies that create competitive advantagesdespite the
external uncontrollable elements. "10
We believe that better management can attract investors' attention, seize their fund for
acquisitions and consequently grow bigger. In other words, larger firms are more efficient
in terms of cost of capital, access to capital, and operating and overhead costs. As a result,
larger REIT can benefit not only in internal growth through relatively lower expenses, but
also contribute to external growth through advantages in access to and cost of public
capital.
Besides, company recognition by investors will tend to rise with size, and large REITs
might be able to have the opportunities, as well as the ability, to buy whole portfolios of
properties when compared to small ones. Also, as a REIT's size increases, it tends to
benefit from an expanding list of potentially profitable transaction offers. Fundamentally,
we are trying to prove that a bigger portfolio of real estate with appropriate configuration
and operation will produce higher operating income than a smaller portfolio, so we expect a
positive sign in this variable shown in the regressions.
10 Same
as footnote 9.
X3- Acquisition and/ or Development
S,0
-1
Acquisition and new development increase the office square footage in a REIT's real estate
portfolio.
Showing the ability to buy good properties, which benefit a REIT's overall
earnings, is far more important than to purely buy properties. After all, the advantage of
buying highly distressed properties in early 90s and turning them into profitable ones no
longer exists due to the recovery of real estate industry. We assume investors are rational
and REITs will discipline themselves from doing "stupid" acquisitions because this will
hurt their accessibility to public capital. Therefore, the higher a REIT's percentage change
in total square feet means it is growing faster, indicating it has access of funding its
acquisitions. Consequently, we are expecting a positive sign in this variable.
3.5 Regression Results and Analysis
As we stated in the methodology section, a summary of the regression results is presented
in Table 3-2, and completed regression outputs are shown in Appendix F, G and H.
Table 3-2
Intercept
Outperf.
Size
rate
% change
R
Adjusted
in sf
square
R square
0.0548
-0.1224
0.0079
-0.0482
0.0827
0.0122
Only 1997
Coeff.
0.3175
-21.5325
-0.3964
0.4037
Data
t Stat
0.2261
- 0.7665
- 0.6859
0.3509
All available
Coeff.
- 1.0366
-0.8973
-0.1631
-0.3831
Data
t Stat
-0.8862
-0.0444
-0.2268
-0.5718
Restricted
Coeff.
0.3321
20.9429
-0.5004
0.2144
Data
t Stat
0.4118
1.5970
-1.0860
0.3879
Surprisingly, all R square in those regression results are low, less than 10%. T statistics are
all insignificant. Size variables have an unexpected coefficient sign in all results and in the
other variables an unexpected coefficient sign appeared once or twice as well. The results
seem to suggest that none of these variables can explain the change in operating income,
and neither internal growth nor external growth factors into AOL.
We should explain the negative sign of Outperformance rate performance variable is caused
by some accounting problems undermining the credibility of change in 01.
That is,
different REITs use different accounting practices and this produces misleading profitability
results. When we look at the database of the regressions, it ought to mention that the
change in operating income is very fluctuant.
We all know that investors would not like this, because their expectations of public REIT
not only fall on income growth but also on the performance of stock prices. The stock
market tends to reward the steady, predictable earnings of property management over the
ups and downs of a REIT. After all, they want to make good investments with possibly
reducing risk to minimum, and producing predictable as well as growing operating
statements decreases uncertainty and helps smooth the volatility of a REIT's stock price,
which is what investors care more about.
As we discussed earlier, with size comes possible cost savings from economies of scale in
operations. It is unfortunately not confirmed from the regression result. Again, the wrong
sign of size variable might be caused the unreliable dependent variable. On the other hand,
if the negative sign of size is correct, it could be attributable to the following reasons. First,
size might not be important in office sector because vacancy rate is far more meaningful
than size. There is no guarantee that higher total office square feet could bring higher
occupancy rate and higher rent revenues.
"Size has never been as advantageous in the real estate
industry as in other types of businesses. We have yet to
see any evidence that adding an extra million square
feet of office space increases the occupancy rate across
the portfolio. None of the office REITs has establisheda
brand name that allows it to charge a rentalpremium.
It is hard to see how size will be a major advantage in
doing the highly customized work of fitting out new
space
Some research results confirm the proposition that office returns are decreasing at an
increasing rate as vacancy rates climb to higher levels 2 . Second, the reason might be that
purchasing power and economies of scale for lager REITs can make only limited
improvements to revenue, but the weakness of bigger size outweigh its benefit, because a
larger REIT might have difficulty maintaining the well-above-average returns and/or maybe
some of the flexible business approaches that exist in smaller companies. However, due to
the insignificance of T statistic, we doubt the credibility of the above opinions.
Regarding the variable of percentage change in square feet, although this one has the
expected coefficient sign, it is not significant. We are suspicious about this phenomenon,
and it will be discussed further in the next chapter. From these regression results, we
conclude that investors do not look only at AOI to judge REITs' performance, because
some accounting problems and financial variety often cause the fluctuation of 01.
"1By John H. Vogel Jr., "Why the New Conventional Wisdom About REITs Is Wrong" , Real Estate Finance!
Summer 1997.
12 From the research by Petros S. Sivitanides, "Predicting Office Returns: 1997-2001",
Real Estate Finance!
Spring 1998.
3.6 Comparison
As expected, the second regression has the lowest R square, 0.0079. This is primarily due
to the unstable operating income caused by the accounting problems contained in the
database, and is why we set up the third regression. As we mentioned earlier, there are
some accounting factors affecting the operating income, and more particularly, a REIT's
operating income always varies when it goes public, engages in mergers and acquisitions,
refinances its properties because of changing interest rates, etc.
Therefore, through eliminating some observations related to the above situations, it is
reasonable to have the third regression indicating the highest R square. And because of the
right sign of the outperformance rate variable with a T statistic of 1.5970, much higher than
others, in this regression, we may hold some expectation on it, however it is still
insignificant. Regarding the regression of 1997 data only, similar unexplainable results
appeared.
It is disappointing to have such low R square results with random signs in all variables.
Other than the fact that the volatility of change in operating income is caused by different
accounting methods, one possible explanation is that rents are different in CBD (Central
Business District) and suburban areas. When a REIT acquired its properties in downtown
New York City last year and acquired another properties in suburban Salt Lake City this
year, a fluctuating operating income is very likely to occur. Another explanation is that
office leases are normally 5 years or longer, and even if the market rent is growing, only a
portion of the growing rent is reflected in a REIT's income change because of the lock-out
leases. Meanwhile, the vacancy rate might play a role in affecting the change in operating
income as well. When the vacancy rate becomes much higher due to certain factors (for
example many tenants' leases expire in the same year and a REIT fails to keep its tenants),
a huge drop in its revenues will happen and damage the stability of operating income.
These reasons lead to the belief that investors do not trust earnings history as a good
indicator of REIT performance because this information is variable and backward looking
in the same way as are appraisals. In the next chapter, we will have further discussion in
order to determine which factors investors do use in evaluating a REIT.
CHAPTER FOUR
After the exploration in the preceding chapter, we realized that investors do not make their
investment decisions by simply looking at the growth in operating income of REITs.
Therefore, we believe the determinants of REIT pricing falls fundamentally on their cost of
capital because cost of capital reflects the pricing of REITs and involves risk which is a
major concern of investors.
We surmised that a predictor of a successful REIT might be the spread between REITs'
equity returns and borrowing costs, and thus the higher spread benefits the earning power
of REITs. As the borrowing costs are related to investors' confidence in a REIT, the lower
its uncertainty risk, the lower its cost of capital, and consequently, the greater its profits. In
this chapter, we propose a model to investigate the determinants of the public capitalization
rate for REITs and hope to find the elements of Wall Street's pricing.
4.1 Pricing Model and Methodology
We conjecture that when Wall Street prices a particular REIT stock, they look at the
following REIT characteristics in addition to returns and performance data: the location of
the REIT's properties relative to local market status, debt ratio, management effectiveness,
economies of scale, ability to grow, and the current capital market condition. The higher
this valuation is, the lower the cost of capital is and the lower the public company
capitalization rate. According to the following equation:
Public Entities Capitalizatio Rate = r
Where r,. denotes required return, gp
1
- 9e
-
AgPu,E
denotes the internal growth rate in cash flows,
and A gPu,E denotes the external growth rate in cash flows13 . A lower cap rate of a REIT
shows it has better performance and is rewarded by capital providers based on the
recognition of its internal and external growth. Thereupon, we connect capitalization rate
and pricing of REITs, and created the following sets of models:
Model A: cap rate =f(debt ratio, size, % A sf, A 01, d92, d93, d94, d95, d96)
Model B: cap rate =f(debt ratio, size, % A sf, market index,
index growth, d92, d93, d94, d95, d96)
Model C: cap rate =f(debt ratio, size, % A sf, outperformance rate,
index growth, d92, d93, d94, d95, d96)
Model D: cap rate =f(debt ratio, size, % A sf, A 01, outperformance rate,
index growth, d92, d93, d94, d95, d96)
These models exhibit that the cap rate is a function of a REIT's debt ratio, size, percentage
change in office square feet, change in operating income per square foot, performance
compared to market benchmark, and time variables. The performance is measured by the
13 The
equation is excerpted from the course notes of Timothy Riddiough, Associate Professor at MIT Center
for Real Estate Development.
market rent index, the growth in this index and the outperformance rate we used in the
previous chapter. For comparison purposes, we replace AOI with market index and growth
of the index in the model B, the outperformance rate and growth of the index in model C,
and put them all together in model D.
Again, as we did in the previous chapter, we set up three standards of database for
regressions: only 1997's data, all available data, and restricted data. By analyzing the three
sets of regression results, we wanted to examine how REITs' performance affects their
capital availability and to identify the link between the pricing of REITs and their
capitalization rate.
4.2 Description of Regression Variables
Cap Rate
Cap Rate = Operating Icome _ OI
Total Market Cap Capj
The dependent variable in the models was the public market capitalization rate, which
implies the external growth investors added in the stock price. A cap rate was used to
convert a yearly income stream into a total market value estimated for a company's overall
real estate portfolio, and here we derived this variable by dividing a REIT's annualized
operating income by its total market capitalization including debt and equity. By using 01
as the numerator to get the cap rate, we wanted to normalize company earnings across its
capital structure and eliminate a distortion, caused by the deduction of interest expenses, in
a company valuation. Also, in order to match the definition of total market cap, including
debt, this measure of operating income was more appropriate and compatible.
Due to their qualification requirements, REITs cannot retain earnings for expansion so they
have to acquire funds only through offering stock or issuing debt, and due to their leverage
aversion characteristics, REITs tend to choose the former over the latter. Therefore, the cost
of public capital is critical. Generally, this cost is likely to be dictated by the past and
projected returns of the REITs, management effectiveness, funds from operations, type of
REIT, geographic area of the REIT, potential for improved economies of scale and scope
and the relationships with nationally recognized financial institutions that provide capital to
the real estate industry.
Based on the rule that the worse a REIT performs, the harder it gets to obtain capital, a
REIT with a higher cap rate indicates that it might be less successful so it must pay higher
costs for stock offerings as incentives to public capital providers. On the contrary, a lower
cap rate is a reflection of superior performance and enables a REIT to arbitrage the spread
between the cost of capital and its returns. Supported by the strong performance in the
stock market recently, a REIT could have relatively easy access to capital by way of initial
or secondary stock issues on Wall Street. This approach has been a major source of REIT
growth, and it does not increase leverage, thereby sustaining investors' confidence in the
REIT.
A brief description of each independent variables, as well as an explanation of what
variables are intended to measure, follows the Table 4-1:
Table 4-1
Expected sign
Independent
Variables
Description
Debt ratio
The ratio of a REIT's long-term debt amount to its total
of coefficient
+
market capitalization.
Size
The percentage of a REIT's total office square feet to
-
the average of all REITs' total office square feet.
% change in sf
Percentage of a REIT's yearly change in its total office
-
square feet.
A 01
Dollar change in operating income per square foot.
-
Market index
Cumulative rental growth of all properties in each
-
market.
Index growth
Change in market index, particularly caused by change
-
in office square feet in all markets- sales or acquisitions.
-
Outperformance
The difference between total rental growth of a REIT's
rate
properties and the national market rental growth.
Dummy 92
1 if the data was in 1992.
?
Dummy 93
1 if the data was in 1993.
?
Dummy 94
1 if the data was in 1994.
?
Dummy 95
1 if the data was in 1995.
?
Dummy 96
1 if the data was in 1996.
?
Debt Ratio
Debt ratio = Long - term debt _Dl
Total market cap
Capi,
Debt ratio is the relationship of company debt to the company's value. Debt ratio is one
key factor revealing balance sheet strength because higher debt ratio decreases a REIT's
financial flexibility. Particularly, there is no tax-shelter effect on REITs as they are a taxexempt entity, so debt does not benefit REITs. Although stated by some analysts that
"higher debt ratios can increase returns since the low level of leverage in REIT capital
structures has resulted in an unnecessarily high cost of capital for the companies.", we still
believe that the disadvantages of debt outweigh the advantages. Not only do unsecured
bondholders have a higher priority to claim their rights than the mortgage debt holders, but
also higher leverage will hurt REIT's stock price because once the stock price falls, the debt
ratio rises accordingly, and increases investors' risk concerns.
Size
For consistency with other variables, we used square footage size instead of share size
which might not be stable due to the different daily trade situations in stock market.
It seemed to be true that large capitalization entities tended to have greater access to lower
cost capital in the public markets because people believe that larger portfolios can generate
47
cost savings based on economies of scale and purchasing power as we mentioned in chapter
three.
Also, very large size gives public REITs the privilege to sell off their worst
properties without hurting their REIT status, and also reduces the adverse effects of
geographic property concentration in a few markets that might be experiencing local
recession. On that account, large REIT size could significantly benefit from geographic
and economic diversification.
On the contrary, large sized REITs have difficulty maintaining the high returns desired by
most investors, and if the income cannot grow as fast as investors expect, the share price of
these large REITs will be punished. In addition, it is misleading to value office REITs by
size without consideration for the vacancy rate. Investors view vacancy rate as an indicator
of market conditions, and the risk associated with high vacancy rate may concern investors
as well as deter additional or future investment.
Percentage Change in Square Feet
A change in total office square footage in a REIT portfolio is normally due to new
development or acquisitions. It is also related to a REIT's ability to grow, since a REIT
needs capital to do acquisitions. For that reason, an increasing percentage change in square
footage implies that a REIT has good ability to acquire capital.
Issuing stock in the public market has been the primary method of accessing capital for
acquisitions by REITs. Some might wonder if percentage change in outstanding shares is a
better way to measure REITs' ability to grow. However, REITs can grow through mergers
without affecting their total outstanding shares. UPREIT1 4 configuration would not cause a
variation in REITs' shares in the beginning either. On the other hand, no matter whether
though merger or UPREIT formation, total square footage of the REIT would change.
Moreover, the operating partnership units of an UPREIT can be converted to shares on the
units holders' will. This might undermine the credibility of changes in shares. Thus, we
use change in square feet as our independent variable, not shares.
AOI/ per square foot
The formation of change in operating income is the same as previously stated. As REITs
are now priced by cash flow instead of appraised values, rental revenues are the major
source of office REITs' cash flow, and rental growth is the key component in AOL.
Connected to the dependent variable, there is a negative relationship between change in
operating income and capitalization rates. A negative change in operating income should
provoke an increase in the market cap rates required by investors to compensate their
greater risks, and vice-versa.
UPREIT is an acronym for umbrella partnership real estate investment trust. It basically refers to a REIT
whose real properties are held by and operations are conducted by a single partnership, or by two or more
partnerships that are under the umbrella of a single partnership, referred to as an "operating partnership" (or
OP). The REIT (occasionally through a qualified REIT subsidiary) is typically the general partner of the
operating partnership. (quoted from Garrigan, Richard T., & John F.C. Parsons, "Real Estate Investment
Trusts: Structure, Analysis, and Strategy, 1998")
14
Market Index
(R,
Market Index it =
-=1
-
R,-)
sfiitirn
f
Rs-1
R,,,,,: rent of REIT i in market m in year t
m: MSA
n: number of MSAs
This variable gives us the opportunity to look into the property level performance of REITs:
in which markets are the properties located, what is the rental growth in those markets,
what are the geographic concentration effects and diversification effects. Rental income is
the primary component of the income earned by an office property. If a REIT's properties
are located in a market where there exists a rental growth opportunity, investors are willing
to pay for this growth opportunity in assets and returns once they recognize it. REITs can
therefore benefit from the lower cost of capital.
As REITs become bigger and more
nationwide, it is a good thing to have a broad geographic base portfolio because that
protects the portfolio from being damaged by a regional economic downtrend.
Index Growth
n
Index growth i, t =1 (
m=1
Rmit
-
-
R
R
Ri't'
iil-.m)
Sfitm
s
_
n
IEsf
i'"M1
rMj
tm
Riutm
-
-
R 1 ' -'m)
R
Rejv1 m
Sf i-I'M
s
_
Esf t-1,m
-'
=1
This variable measures REITs' property performance, and involves the overall change in
the index itself. The percentage change in the index from one year to the next allows us to
reveal a REIT's ability to select markets. A REIT can sell one of its properties and still
have positive index growth because that property is in a rent-decreasing market and the
other properties it keeps are located in rent-increasing market; in contrast with that, a REIT
can have negative index growth even it bought new property which is located in a rentdecreasing market. It is also possible that the sold property is in good market. However, as
long as the REIT is capable of buying another property or benefiting from the remaining
portfolio in some markets with even higher rental growth to cover the lost income from the
sold property, the REIT can still have the positive index growth.
Outperformance Rate
As calculated in Chapter Three, the outperformance rate is the difference between a REIT's
performance and the performance of a national property index.
This rate indicates the
extent to which a REIT operates its properties better than the national market, excluding the
possibility that each REIT's income growth increases are caused by the overall rental
growth in the market. Put in the extreme, a bad REIT could still have positive income
growth when "every" market is experiencing rental growth. The income growth of a really
well-operated REIT should lead the market income growth, not because the market is good
and gives a REIT positive income growth.
Time Dummy-using 97 as base year.
We use 1997 as the standard year, and explore the time effect on the public cap rate, which
means the positive (or negative) coefficient sign of dummy 96 indicates the cap rate in 1996
was higher (or lower) than the rate in 1997. Doing so gives us the opportunity to see the
market trend of cap rate in every year. Since a REIT's performance is changing every year,
so is the cap rate.
4.3 Regression Results and Analysis
The various regression outputs are shown in Appendix I, J and K, and the summaries of
three sets of regressions are presented in Tables 4-2, 4-3 and 4-4 respectively.
Table 4-2
Only 1997 data
Model A
Model C
Model D
Model A
Model C
Model D
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
R square
0.5119
0.4695
0.6222
Adjusted R square
0.3817
0.2800
0.4479
Intercept Debt ratio
0.0302
0.0462
2.7382
1.5773
0.0431
0.0940
2.8430
2.8479
0.0433
0.0708
3.2580
2.3090
Size
0.0006
0.2333
-0.0029
-0.6616
-0.0006
-0.1461
% A sf
-0.0066
-1.3541
-0.0037
-0.6829
-0.0045
-0.9385
A 01
0.0032
2.8791
0.0026
2.2928
Outperf. Index
Rate
growth
-0.2998
-1.8518
-0.2450
-1.7045
0.2244
0.4613
0.0314
0.0723
We first look at the set containing only 1997 data, the adjusted R squares are 38.17%,
28.00% and 44.79% respectively for model A, B and D, indicating that just under 45% of
the variation in public cap rate is explained by our variables. Besides, variables of debt
ratio and market index have statistically significant coefficients.
Other variables, though not statistically significant, more or less show the expected
coefficient signs in two or three models, except index growth. However, AOI has the
wrong sign with a surprisingly high significant T Statistic. Again, this further reveals the
fluctuation of AOI and identifies the unreliability of this variable.
Regarding the
unexpected coefficient sign of index growth, the cause might be too few available
observations.
Moving to the all available data regression, the adjusted R squares are 46.74%, 54.30%,
54.30% and 53.39% respectively for model A, B, C and D, indicating around 50% of the
variation in cap rate can be explained by our variables including time series variables. The
results show that all variables have the expected coefficient signs, except for AOI, and the
effects of time variables corresponding the actual market trend that the public cap rate has
been falling over the past few years.
In addition, more variables are statistically significant including debt ratio, percentage
change in square feet, market index, outperformance rate and time dummies. Size and
index growth seem not to have the strong influence on cap rate that we thought, even
though they both appear with the expected coefficient signs.
Table 4-3
All available data
Model A
Model B
Model C
Model D
R square
0.5530
0.6246
0.6246
0.6254
Adjusted R square
0.4674
0.5430
0.5430
0.5339
Market Outperf.
Intercept Debt ratio
Model A
Model B
Model C
Model D
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
0.0354
5.3140
0.0574
5.9906
0.0323
5.1453
0.0322
5.0851
Index
growth
Model A
Model B
Model C
Model D
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
-0.2492
-1.2958
-0.2491
-1.2957
-0.2515
-1.2943
0.0456
3.1899
0.0412
3.1266
0.0412
3.1266
0.0418
3.1100
Dummy
92
0.0086
0.7713
-0.0069
-0.5963
0.0344
2.6283
0.0336
2.4939
Size
% A sf
A 01
-0.0035
-1.6832
-0.0021
-0.9923
-0.0021
-0.9925
-0.0021
-0.9766
-0.0052
-2.5521
-0.0058
-3.1008
-0.0058
-3.1007
-0.0057
-3.0102
0.0002
0.4282
Dummy
93
0.0091
0.9107
-0.0095
-0.8785
0.0205
2.0572
0.0211
2.0609
index
rate
-0.2407
-2.9882
0.0001
0.3191
Dummy
94
0.0219
2.7076
0.0106
1.2641
0.0277
3.5724
0.0278
3.5463
Dummy
00
0.0257
3.5846
0.0217
3.2017
0.0330
4.7099
0.0328
4.6029
-0.2407
-2.9880
-0.2396
-2.9428
Dummy
96
0.0074
1.1745
0.0014
0.2279
0.0118
1.9587
0.0116
1.9110
Regarding the last set, the adjusted R squares are 51.97%, 59.22%, 59.22% and 58.09%
respectively for model A, B, C and D, indicating that nearly 60% of the variation in cap rate
can be explained by our variables. Similar to the results of previous regression set, all
variables, except AOI (with low significance), show the expected coefficient signs. Most
variables, including debt ratio, percentage change in square feet, market index,
outperformance rate and time dummies, have statistically significant coefficients.
Table 4-4
Restricted data
Model A
Model B
Model C
Model D
Model A
Model B
Model C
Model D
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
R square
0.6226
0.6893
0.6893
0.6907
Intercept Debt ratio
0.0466
0.0388
5.2312
3.1193
0.0434
0.0571
3.1426
5.8617
0.0351
0.0434
5.0319
3.1426
0.0347
0.0434
4.8528
3.1000
Index
growth
Model A
Model B
Model C
Model D
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
Coeff.
t Stat
Adjusted R square
0.5197
0.5922
0.5922
0.5809
-0.2932
-1.5669
-0.2932
-1.5667
-0.2849
-1.4907
Dummy
92
0.0060
0.5434
-0.0077
-0.6904
0.0283
2.1557
0.0279
2.0807
Size
-0.0040
-1.9188
-0.0023
-1.1265
-0.0023
-1.1267
-0.0022
-1.0430
% A sf
-0.0062
-2.3413
-0.0069
-2.8127
-0.0069
-2.8126
-0.0069
-2.7855
Dummy
93
-0.0036
-0.3339
-0.0184
-1.6389
0.0078
0.7298
0.0090
0.7933
A 01
0.0001
0.0682
Market Outperf.
index
rate
-0.2103
-2.5976
0.0003
0.3690
Dummy
94
0.0197
2.1457
0.0092
0.9912
0.0241
2.8022
0.0248
2.7791
Dummy
00
0.0209
2.5082
0.0189
2.4386
0.0288
3.4615
0.0289
3.4240
-0.2102
-2.5973
-0.2146
-2.5885
Dummy
96
0.0068
0.9798
0.0019
0.2887
0.0109
1.6896
0.0114
1.7068
An example of the interpretation of the time dummy variables is that, holding all else
constant, the cost of capital for REITs in 1996 was 1.14% higher than it was in 1997.
Holding other variables zero, the standard requirement of capital cost for a REIT in 1997
was 3.47% based on the result in the third set model D. Another example of interpretation
of two variables that have noticeable impacts on the cap rate from the same regression
result: holding all else constant, a 1% increase in outperformance rate or index growth
results 21 and 28 basis point decreases in the cap rate, respectively.
We have had detailed discussions about the influence of our variables on the public
capitalization rate in the previous section, and the regression results verified our
explanations about debt ratio, percentage
change in square feet, market index,
outperformance rate, and time variables. The increased debt ratio of a REIT reduces its
financial flexibility. Therefore, debt ratio has a positive relationship with the public cap
rate of a REIT.
Percentage change in square feet indicates a REIT's external growth and therefore
correlates negatively with the cap rate. Analyzing the internal growth, market index and
outperformance rate reveals that investors are more forward-looking: they care more about
the markets in which properties are located and how the rental growth will be in those
markets than they do about backward-looking information. Investors are rational enough
not to stick to historical AOI when valuing REITs.
The negative coefficient sign of size leads us to believe that the existence of economies of
scale has merit, but only has a limited impact on the cap rate. In addition, it is interesting to
note that in the absence of the market index or outperformance rate in model A, in both the
second and third set of regressions, the size variable increased to close to a statistically
significant level at -1.68 and -1.91 respectively. This could be explained by saying that the
size variable in model A implies there is geographic diversification and therefore is viewed
more seriously by investors. This situation did not happen in other models, because the
index-related variables clearly indicate the effects of geographic diversification and distract
investors' attention about a REIT's size.
It is puzzling that the index growth variable has insignificant T statistics. Our interpretation
is that investors like a REIT that demonstrates its ability to select a good market, but that
investors do not consider this index "growth" as important since this growth is derived by
comparing to the past year's index. We have already shown that investors focus more on
future growth opportunities.
As to the time dummy variables, the results indicate that REITs have been doing well and
investors reward them by lowering their cost of capital year after year. The negative
coefficient sign in dummy 92 and 93 of model B in the second and third sets is caused by
the negative market index rate REITs had in those two years. This situation did not happen
in model C because the way of calculating outperformance rate offset the negative effect
and turned the rate to be positive (for example: -2%-(-3.5%)=1.5%)
even though the
national office market was experiencing recession in those two years and the rental growth
rate was negative at that time.
From those regression results, we now conclude that capital providers and investors price a
REIT based on its debt ratio, acquisition ability, property locations and the market
tendency. Investors are forward-looking, meaning they do not look backward at a REIT's
change in operating income.
4.4 Comparison
Starting from the set of 1997-only data, we got reasonable results as expected. Followed by
the second and third regression sets, the results came to show the same coefficient signs and
similar T statistics.
We noticed that an unexpected sign consistently appeared in all
regression outputs; a change in operating income here gave us additional evidence of its
unreliability.
In the beginning, we compared the public capitalization rate of 20 REITs in 1997 only, and
the regression results first justified our explanations of some variables without time effect
built into the cap rate.
The following regression outputs further confirmed our
interpretations of all variables. All regression outputs are basically consistent with each
other, and increases the credibility of our models overall.
CHAPTER FIVE
5.1 Conclusion
We have empirically examined the components of income growth and determinants of the
public capitalization rate.
Although the R square of the results did not appear to be
persuasive, the fairly strong statistical significance of some variables indicates the
explanatory power of our model. Generally, the results from the restricted data set, the set
that eliminated some observations, appear more conclusive than first two sets. The third set
had the highest adjusted R square, more variables were statistically significant, and these
variables exhibited the expected signs.
In our analyses of income growth component, we concluded that capital providers and
investors did not consider change in historic operating income a key element in making
their investment decisions: they were not backward-looking. Although this variable was
intuitively supposed to be a key component of income growth that determines investors'
returns, and therefore valued meaningfully by investors, the overall regression results
suggested this was not the case. This finding was reflected in investor's indifference
toward the variable.
In the analyses of the public cap rate, we further confirmed the above conclusion and
established that the factors of debt ratio, percentage change in square feet, market index,
outperformance rate and time will affect Wall Street analysts' pricing on REITs. Since the
public cap rate of a REIT reflects its performance and investors' valuation on it, one
conclusion that may be drawn from this is that those factors build up investors' confidence
in REITs, and subsequently influence the cost of capital for REITs.
REITs have been making profits by arbitraging the spread between their cost of capital and
actual returns. The spread is a premium representing investors' confidence in a REIT's
ability to create shareholder wealth and their expectations about a REIT's high growth.
Hence, the better a REIT's performance, the lower its cost of capital due to investors' belief
in its continuous growth in the future.
How to enhance investors' confidence in REITs?
Through our regression analyses, we
found some elements: First, high debt ratio is a warning signal for investors. If debt is too
high, a REIT is likely to have significant financial obstacles that may jeopardize its
survival.
Moreover, percentage change in square feet is a good indicator, showing a
REIT's ability to acquire properties at reasonable prices and achieve desired yields.
A
REIT's ability not to overpay is crucial, because while it may be able to afford to pay more,
it can not afford to make 'stupid' acquisitions which will shaken investors' confidence in the
REIT.
Use of the market index and outperformance rate enable investors to look into property
level performance of office REITs and understand the allocations of those properties. This
helps investors predict the internal growth opportunities of REITs and seize those
opportunities. After all, the rental growth in the properties benefits shareholders' dividends
more than the value of properties themselves does.
"Purchases of real estate generally focus on the
earnings potential of a property, not its past
performance, and cap rates are generally defined as a
consequence of the next-12 months' income1 ." (From
MerrillLynch)
Size does not matter in office REITs, according to the regression results. The quality of a
REIT's income growth and its property is more important than the quantity of its total
office space. Besides, the higher vacancy rate in office buildings is likely to motivate
investors to require higher cap rates even in large office REITs.
The capitalization rate has declined with the returning investor confidence in office space
over the past few years, supported by the outstanding performance of office REITs in the
stock market. Whether the cap rate can stay low depends on a REIT's performance and its
management and operational expertise and ability to achieve good yields. The growth
opportunities of acquiring distressed real estate have become virtually nonexistent. Since
REITs are operating businesses and going concerns, the future of REITs is in the operation
15 Real
Estate/ Equity REITs, 10 April 1996, "Calculating Net Asset Values", Merrill Lynch.
62
of a real estate portfolio. Investors will look at the management effectiveness of REITs as
well as growth potential, so pricing REITs by analyzing their management ability has
become critical.
REITs are no longer priced at the value of their underlying properties. Instead, REITs are
priced based on how they operate their real estate portfolio, and generate investors'
interests through a high dividend payout from the portfolio. The office REITs have to take
good care of their properties, which should allow the REIT to be able to provide a high
dividend to meet investors' required returns.
Doing so consistently will improve and
sustain investors' confidence in the office REITs and consequently benefit the REITs with a
lower cost of capital.
Appendix A
Alexandria Real Estate Equities, Inc. and Subsidiaries
Consolidated Statements of Operations
YEAR ENDED DECEMBER 31
1997
<S>
Revenues:
Rental
Tenant recoveries
Other
<C>
<C>
<C>
1995
1996
$25,622
8,388
836
$12,941
4,169
563
$8,020
1,699
204
34,846
17,673
9,923
8,766
2,476
7,043
4,239
4,356
1,972
6,327
-
2,228
1,608
3,553
-
Post retirement benefit
632
438
-
Special bonus
353
-
6,973
2,295
4,866
-
2,405
1,668
37,643
15,498
9,057
(Loss) income from operations
-2,797
2,175
866
Charge in lieu of income taxes
-
-
105
-$2,797
$2,175
$761
$3,038
$1,590
-$5,835
$585
Expenses:
Rental operations
General and administrative
Interest
Stock compensation
Acquisition LLC financing costs
Write-off of unamortized loan costs
Depreciation and amortization
Net (loss) income
--
Net (loss) income allocated to preferred
stockholders
Net (loss) income allocated to common
stockholders
$
-
$761
Net (loss) income per pro forma share of
common stock - restated for 1996 and
1995 (basic and diluted)
-$0.35
$0.60
$0.43
8,075,864
3,642,131
1,765,923
1997
31-Dec
1996
<C>
<C>
$229,970
2,060
6,799
3,630
$146,960
1,696
5,585
1,332
1,350
4,645
2,502
2,405
$248,454
$160,480
$47,817
$113,182
Pro forma weighted average shares of
common stock outstanding - restated for
1996 and 1995 (basic and diluted)
Consolidated Balance Sheets
<S>
ASSETS
Rental properties, net
Cash and cash equivalents
Tenant security deposits and other restricted cash
Tenant receivables and deferred rent
Loan fees and costs (net of accumulated amortization of
$175 and $131in 1997 and 1996, respectively)
Other assets
Total assets
LIABILITIES AND STOCKHOLDERS' EQUITY
Secured notes payable
Unsecured line of credit
Accounts payable, tenant security deposits and other
23,000
-
6,158
3,650
4,562
-
1,550
2,525
81,537
120,907
liabilities
Dividends payable
Due to Health Science Properties Holding Corporation
Commitments and contingencies
-
Manditorily redeemable Series V cumulative convertible
preferred stock, $0.01 par value, $1,000 stated value per
share, 50,000 shares authorized; 27,500 issued and
outstanding at December 31, 1996
Stockholders' equity:
Preferred stock:
Series T 8.5% preferred stock, $0.01 par value and $100
stated value per share, 12 shares issued and outstanding at
31-Dec-96
Series U 8.5% cumulative convertible preferred stock,
$0.01 par value and $500 stated value per share, 220
shares issued and outstanding at December 31, 1996
Common stock, $0.01 par value per share, 100,000,000
shares authorized; 11,604,631 and 1,765,923 shares
issued and outstanding at December 31, 1997 and 1996,
respectively
Additional paid-in capital
Accumulated deficit
Total stockholders' equity
Total liabilities and stockholders' equity
66
-
25,042
-
1
-
110
114
-
173,735
-6,932
16,195
-1,775
--------------
-----------
166,917
14,531
--------------
-----------
$248,454
$160,480
---------------------------
---------------------
Appendix B: Office REITs Financial Database
Data
Number
Office
01
REITs
(000')
(date)
Alexandria R.E. Equities
1
97/12/31
$4,246
2
96/12/31
$8,502
Arden Realty
3
97/12/31
$63,422
4
96/12/31
$28,925
5
95/12/31
$5,078
Boston Properties
6
97/12/31
$135,306
7
96/12/31
$118,051
Brandywine Realty
97/12/31
8
$22,367
9
96/12/31
$2,660
CarrAmerica
14
97/12/31
$130,014
15
96/12/31
$61,258
16
95/12/31
$40,069
17
94/12/31
$37,894
18
93/12/31
$24,714
Cedar Income
20
97/12/31
$636
21
96/12/31
$700
22
95/12/31
$909
23
94/12/31
$802
24
93/12/31
$611
25
92/12/31
$524
Cornerstone Properties
27
97/12/31
$73,847
28
96/12/31
$41,607
29
95/12/31
$31,427
30
94/12/31
$28,282
Crescent Real Estate Equity
34
97/12/31
$197,722
35
96/12/31
$87,027
36
95/12/31
$49,639
$17,625
37
94/12/31
Equity Office Properties
1 $297,837
39
197/12/31
Long-term
Debt
(000')
Total
cap
(Million')
Total office
sf
Size
$70,817
$55,400
$431
$206
1,747,837
926,070
0.1524
0.1548
$477,566
$155,000
$104,000
$1,752
$533
$429
14,842,099
5,443,124
4,041,792
1.2945
0.9101
1.2570
$1,332,000
$1,442,476
$2,611
$1,997
10,010,183
5,662,748
0.8730
0.9468
$163,900
$36,600
$604
$171
5,639,948
1,745,986
0.4919
0.2919
$986,000
$642,000
$295,000
$246,000
$170,000
$2,613
$1,429
$391
$391
$255
19,104,034
12,430,437
3,326,009
2,705,734
1,992,054
1.6662
2.0784
1.0344
1.1073
0.9199
$1,400
$1,423
$1,445
$1,464
$1,481
$1,497
$16
$11
$11
$10
$12
$11
163,960
163,960
163,960
163,960
163,960
163,960
0.0143
0.0274
0.0510
0.0671
0.0757
0.1029
$893,000
$400,000
$370,000
$367,000
$2,004 10,270,000
$579 4,725,000
$559 3,836,000
$451 3,372,000
0.8957
0.7900
1.1930
1.3800
$1,710,000
$626,000
$371,000
$195,000
$3,714 30,801,159
$1,640 16,319,459
$858 8,772,823
$523
5,227,736
2.6863
2.7286
2.7283
2.1395
$2,243,000
$11,419 65,291,790
5.6944
40
G&L Realty
196/12/31
41
97/12/31
42
96/12/31
43
95/12/31
44
94/12/31
45
93/12/31
Great Lakes REIT
47
97/12/31
48
96/12/31
Highwoods
$187,675 $1,358,000
$4,662 29,127,289
4.8701
Corp.
$15,155
$9,403
$10,655
$7,339
$6,169
$95,172
$86,700
$83,100
$45,200
$45,500
$182
$109
$101
$67
$71
697,456
725,347
562,942
543,307
437,104
0.0608
0.1213
0.1751
0.2224
0.2018
$16,414
$8,487
$95,000
$16,900
$380
$196
3,988,486
2,800,529
0.3479
0.4682
$139,978
$74,852
$42,654
$13,852
$5,030
$979,000
$556,000
$179,000
$66,600
$74,700
$2,690 23,841,565
$1,326 12,350,593
$532 4,921,370
$219 3,764,392
$203
757,090
2.0793
2.0650
1.5305
1.5406
0.3496
$36,581
$15,025
$131,000
$96,000
$528
$291
4,200,734
2,037,414
0.3664
0.3407
$37,159
$31,403
$53,018
$30,314
$13,923
$12,463
$182,000
$203,000
$257,000
$319,000
$327,000
$94,400
$624
$567
$563
$600
$603
$330
8,480,320
7,661,350
7,672,390
7,906,370
7,906,370
4,058,380
0.7396
1.2810
2.3861
3.2357
3.6510
2.5471
$75,445
$45,279
$27,263
$20,256
$973,000
$262,000
$135,000
$77,000
$2,799 18,526,067
$963 6,372,879
$321
3,387,968
$302 2,304,522
1.6158
1.0656
1.0536
0.9431
$277
$628
$601
$495
$627
$755
$4,741
$4,830
$4,912
$4,988
$4,845
$4,915
$15
$13
$11
$11
$10
$9
159,000
159,000
159,000
159,000
159,000
159,000
0.0139
0.0266
0.0494
0.0651
0.0734
0.0998
$17,165
$7,439
$3,158
$2,083
$110,693
$62,800
$34,700
$27,000
$411
$141
$84
$34
4,988,207
2,172,256
3,017,000
477,144
0.4350
0.3632
0.9383
$58,341
$490,000
$1,432
9,151,466
0.7981
Properties
50
97/12/31
51
96/12/31
52
95/12/31
53
94/12/31
54
93/12/31
Kilroy Realty
56
97/12/31
57
96/12/31
Koger Equity
59
97/12/31
60
96/12/31
61
95/12/31
62
94/12/31
63
93/12/31
64
92/12/31
Mack-Cali Realty
65
97/12/31
66
96/12/31
67
95/12/31
68
94/12/31
Nooney Realty Trust
71
97/12/31
72
96/12/31
73
95/12/31
74
94/12/31
75
93/12/31
76
92/12/31
Parkway Properties
97/12/31
77
96/12/31
78
95/12/31
79
80
94/12/31
Prentiss Properties
|97/12/31
82
83
96/12/31
84
95/12/31
SL Green Realty Corp.
85
97/12/31
86
96/12/31
Tower Realty
87
97/12/31
88
96/12/31
$14,443
$13,742
$129,000
$66,200
$508
$400
4,672,340
3,438,415
0.7812
1.0693
$12,1661
$2,057
$128,800
$16,600
$371
$251
3,322,000
1,186,000
0.2897
0.1983
$12,639
$7,585
$228,900
$203,000
$614
$468
4,075,613
2,935,254
0.3555
0.4908
Appendix C: 54 MSA Rental Index
BAKERS
BALTIM
ALBUQU
ATLANT
AUSTIN
RENTINDEX
RENTINDEX
RENTINDEX
RENTINDEX RENTINDEX
12.27
14.47
10.63
1991
9.70
12.92
12.58
14.50
10.10
12.66
12.05
1992
13.46
12.61
12.57
11.77
1993
11.25
1994
11.22
13.55
14.25
11.79
14.48
16.17
11.17
15.42
1995
12.20
14.64
1996
13.59
16.08
16.64
12.50
15.34
1997
14.25
16.95
17.89
12.56
16.27
BOSTON
CHRLTE
CHICAG
CINCIN
CLEVEL
RENTINDEX RENTINDEX RENTINDEX RENTINDEX RENTINDEX
1991
14.50
13.23
16.16
12.58
11.98
12.68
15.58
13.82
12.28
1992
14.43
1993
15.75
12.87
15.05
13.00
12.80
12.82
13.04
15.67
13.68
15.29
1994
13.90
13.50
16.53
1995
15.97
14.76
14.80
17.66
14.11
15.93
1996
16.83
15.06
14.68
16.27
19.31
20.45
1997
FORTLA
DENVER
DETROI
DALLAS
COLUMB
RENTINDEX RENTINDEX RENTINDEX RENTINDEX RENTINDEX
12.66
14.34
10.50
11.22
13.69
1991
13.26
10.20
10.55
12.87
11.01
1992
12.29
13.85
10.87
11.27
13.10
1993
11.98
14.30
11.21
11.31
13.65
1994
14.77
15.41
13.12
11.74
14.36
1995
15.66
15.79
14.24
14.18
15.53
1996
17.49
16.92
16.90
15.24
16.00
1997
HOUSTO
HONOLU
HARTFO
FRESNO
FORTWO
RENTINDEX RENTINDEX RENTINDEX RENTINDEX RENTINDEX
30.54
11.35
15.10
13.18
9.38
1991
11.40
31.83
15.36
9.78
13.29
1992
11.10
27.67
14.76
13.31
10.67
1993
24.81
11.21
14.21
12.85
11.45
1994
21.45
11.25
15.68
12.56
10.81
1995
20.86
11.94
16.19
12.88
11.33
1996
20.66
12.66
15.18
12.96
12.91
1997
LANGEL
KANSAS
JERSEY
JACKSO
INDIAN
RENTINDEX RENTINDEX RENTINDEX RENTINDEX RENTINDEX
20.30
12.82
16.34
13.26
12.94
1991
18.68
12.80
15.50
13.59
10.92
1992
17.43
12.10
15.40
14.34
11.59
1993
1994
1995
1996
1997
11.80
12.43
12.32
13.89
14.58
15.41
15.37
16.04
LISLAN
LVEGAS
RENTINDEX RENTINDEX
1991
17.35
14.95
1992
16.73
16.60
1993
16.96
16.81
1994
15.02
17.20
1995
16.09
18.23
1996
19.62
18.14
1997
18.17
18.69
NYORK
OAKLAN
RENTINDEX RENTINDEX
1991
24.63
16.13
1992
20.93
16.00
1993
19.37
15.97
1994
20.97
16.34
1995
22.16
16.67
1996
23.01
17.69
1997
25.09
19.44
OXNARD
PHILAD
RENTINDEX RENTINDEX
15.64
15.24
1991
1992
14.93
14.28
1993
14.17
14.37
14.34
1994
13.65
14.64
1995
14.36
15.09
1996
15.36
16.80
15.59
1997
SJOSE
SACRAM
RENTINDEX RENTINDEX
16.69
15.53
1991
16.07
14.75
1992
14.71
16.62
1993
16.94
1994
14.72
17.00
15.17
1995
19.90
15.74
1996
23.23
16.49
1997
SEATTL
SFRANC
RENTINDEX RENTINDEX
15.00
18.06
1991
14.70
1992
16.96
14.20
17.44
1993
15.34
15.94
15.79
17.08
13.46
16.76
14.04
17.21
14.40
18.03
15.84
19.19
MIAMI
MINNEA
NASHVI
RENTINDEX RENTINDEX RENTINDEX
14.63
12.25
11.74
13.85
12.04
11.69
15.54
12.22
12.39
14.95
13.12
12.45
17.53
14.64
13.39
16.74
16.23
14.17
18.67
17.98
14.53
OKLAHO
ORANGE
ORLAND
RENTINDEX RENTINDEX RENTINDEX
8.74
16.99
12.64
9.11
15.57
12.82
8.64
14.77
12.35
8.58
15.09
13.75
9.10
15.48
13.63
9.54
16.30
14.76
11.53
17.73
16.27
PHOENI
PORTLA
RIVERS
RENTINDEX RENTINDEX RENTINDEX
12.55
14.72
11.55
11.28
12.05
14.07
11.87
12.59
13.98
13.26
12.77
12.96
13.57
13.41
13.48
14.01
15.34
14.55
14.27
15.30
17.25
SALTLA
SDIEGO
SLOUIS
RENTINDEX RENTINDEX RENTINDEX
14.49
9.56
13.78
13.83
10.51
12.40
10.38
13.04
13.03
13.23
12.99
11.89
13.65
13.03
14.38
14.84
13.43
15.25
16.47
13.84
16.49
TAMPA
TUCSON
STAMFO
RENTINDEX RENTINDEX RENTINDEX
12.28
11.65
17.00
11.85
15.42
12.61
12.80
12.34
14.85
1994
1995
1996
1997
1991
1992
1993
1994
1995
1996
1997
12.76
12.26
15.90
14.79
17.78
14.57
12.83
15.33
17.58
19.29
14.02
16.87
14.13
16.15
20.99
16.13
14.36
26.07
17.52
19.62
WASHIN
WESTCH
WILMIN
WBEACH
NATION
RENTINDEX RENTINDEX RENTINDEX RENTINDEX RENTINDEX
12.40
16.58
13.43
17.08
17.33
15.47
12.25
10.02
16.53
16.46
15.16
13.87
10.35
17.27
16.64
15.67
12.69
16.96
14.64
17.32
14.19
16.57
14.72
18.54
14.74
17.59
16.57
14.58
18.38
19.34
19.43
17.50
16.42
17.66
21.08
Source: Torto Wheaton Research
Appendix D: External Growth Performance
Data
Office
number
REITs (date)
Alexandria R.E. Equities
1
97/12/31
96/12/31
2
Arden Realty
3
97/12/31
4
96/12/31
95/12/31
5
Boston Properties
6
97/12/31
Office square
ft.
% change in
sf
Outstanding
Shares (000)
% change
in shares
1,747,837
926,070
88.74%
11,405
-
N/A
14,842,099
5,443,124
4,041,792
172.68%
34.67%
35,443
18,853
-
88.00%
N/A
10,010,183
76.77%
38,694
N/A
5,662,748
96/12/31
7
Brandywine Realty
8
97/12/31
5,639,948
9
96/12/31
1,745,986
CarrAmerica
14
97/12/31
19,104,034
15
96/12/31
12,430,437
16
95/12/31
3,326,009
17
94/12/31
2,705,734
18
93/12/31
1,992,054
Cedar Income
20
97/12/31
163,960
21
96/12/31
163,960
22
95/12/31
163,960
23
94/12/31
163,960
24
93/12/31
163,960
25
92/12/31
163,960
Cornerstone Properties
27
97/12/31
10,270,000
28
96/12/31
4,725,000
29
95/12/31
3,836,000
30
94/12/31
3,372,000
Crescent Real Estate Equity
30,801,159
34
97/12/31
16,319,459
96/12/31
35
8,772,823
95/12/31
36
5,227,736
94/12/31
37
Equity Office Properties
65,291,790
97/12/31
39
29,127,289
96/12/31
40
223.02%
585.91%
23,336
8,419
177.18%
353.61%
53.69%
273.73%
22.92%
35.83%
0.00%
58,182
35,462
13,362
13,244
13,232
64.07%
165.39%
0.89%
0.09%
N/A
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
2,245
2,245
2,245
2,245
2,329
2,329
0.00%
0.00%
0.00%
-3.61%
0.00%
0.00%
117.35%
23.18%
13.76%
0.00%
83,177
20,610
19,960
13,241
303.58%
3.26%
50.74%
0.00%
88.74%
86.02%
67.81%
112,556
36,146
23,144
16,047
211.39%
56.18%
44.23%
N/A
124.16%
241,118
-
N/A
G&L
Realty
Corp.
41
42
43
44
97/12/31
96/12/31
95/12/31
94/12/31
697,456
725,347
562,942
543,307
45
93/12/31
437,104
Great Lakes REIT
47
97/12/31
96/12/31
48
-3.85%
28.85%
3.61%
24.30%
4,001
4,063
4,062
4,159
-1.53%
0.02%
-2.33%
0.00%
4,159
N/A
3,988,486
2,800,529-
42.42%
15,685
N/A
93.04%
150.96%
30.73%
397.22%
46,698
35,132
19,401
8,986
32.92%
81.08%
115.90%
N/A
Highwoods Properties
50
51
52
53
97/12/31
96/12/31
95/12/31
94/12/31
23,841,565
12,350,593
4,921,370
3,764,392
54
93/12/31
757,090.00
Kilroy Realty
56
57
97/12/31
96/12/31
4,200,734
2,037,414
106.18%
39.26%
24,475
-
N/A
59
97/12/31
60
96/12/31
61
95/12/31
62
94/12/31
63
93/12/31
64
92/12/31
Mack-Cali Realty
65
97/12/31
66
96/12/31
67
95/12/31
68
94/12/31
8,480,320
7,661,350
7,672,390
7,906,370
7,906,370
4,058,380
10.69%
-0.14%
-2.96%
0.00%
94.82%
0.00%
25,387
20,886
17,751
17,602
13,220
13,220
21.55%
17.66%
0.85%
33.15%
0.00%
0.00%
18,526,067
6,372,879
3,387,968
2,304,522
190.70%
88.10%
47.01%
49,665
36,319
14,494
13,302
36.75%
150.58%
8.96%
N/A
159,000
159,000
159,000
159,000
159,000
159,000
0.00%
0.00%
0.00%
0.00%
0.00%
867
867
867
867
867
867
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
4,988,207
2,172,256
3,017,000
477,144
129.63%
-28.00%
532.30%
9,759
4,169
2,008
1,563
134.08%
107.62%
28.47%
N/A
9,151,466
4,672,340
95.86%
35.89%
32,289
21,148
52.68%
N/A
-
Koger Equity
Nooney Realty Trust
71
97/12/31
72
96/12/31
73
95/12/31
74
94/12/31
75
93/12/31
76
92/12/31
Parkway Properties
97/12/31
77
96/12/31
78
95/12/31
79
80
94/12/31
Prentiss Properties
97/12/31
82
83
96/12/31
84
|7
SL Green Realty
85
86
Tower Realty
95/12/31T 3,43 8,41
Corp.
97/12/31
3,322,000
1,186,000
96/12/3 1
87
97/12/31
4,075,613
88
96/12/31
2,935,254
Mean
St. Dev.
|
-
180.10%
12,292
-
N/A
38.85%
16,920
N/A
-
77.45%
121.92%
50.73%
81.93%
Appendix E: Internal Growth Performance
Data
number
Office
REITs
01
(000')
Change in
01
01
/ per sf
Change in
OI/ per sf
97/12/31
$4,246
($4,256)
$2.43
($6.75)
96/12/31
$8,502
97/12/31
96/12/31
95/12/31
$63,422
$28,925
$5,078
$34,497
$23,847
$4.27
$5.31
$1.26
($1.04)
$4.06
97/12/311
$135,306
_$17,255
$13.52
($7.33)
7
96/12/31
Brandywine Realty
8
97/12/31
9
96/12/31
CarrAmerica
14
97/12/31
15
96/12/31
16
95/12/31
17
94/12/31
18
93/12/31
Cedar Income
20
97/12/31
21
96/12/31
22
95/12/31
23
94/12/31
24
93/12/3 1
25
92/12/31
Cornerstone Properties
27
97/12/31
28
96/12/31
29
95/12/31
30
94/12/31
Crescent Real Estate Equity
34
97/12/31
35
96/12/31
36
95/12/31
37
94/12/31
Equity Office Properties
97/12/311
391
$118,051
Alexandria R.E. Equities
1
2
Arden Realty
3
4
5
Boston Properties
6
$9.18
$20.85
$22367
$2,660
$707
$2,686
$3.97
$1.52
$2.44
$1.63
$130914
$61,258
$40,069
$37,894
$24,714
$68,756
$21,189
$2,175
$13,180
($6,531)
$6.81
$4.93
$12.05
$14.01
$12.41
$1.88
($7.12)
($1.96)
$1.60
($3.28)
$636
$700
$909
$802
$611
$524
($64)
($209)
$107
$191
$87
$65
$3.88
$4.27
$5.54
$4.89
$3.73
$3.20
($0.39)
($1.27)
$0.65
$1.16
$0.53
$0.40
$73,847
$41,07
$31,427
$28,282
$32,240
$10,180
$3,145
$1,164
$7.19
$8.81
$8.19
$8.39
($1.62)
$0.61
($0.19)
$0.35
$197,722
$87,027
$49,639
$17,625
$110,695
$37,388
$32,014
$41,423
$6.42
$5.33
$5.66
$3.37
$1.09
($0.33)
$2.29
$297,837
1$110,162
$4.56
1($1.88)
1
401
96/12/31
$187,675
97/12/31
96/12/31
95/12/31
94/12/31
93/12/31
$15,155
$9,403
$10,655
$7,339
$6,169
47
97/12/31
48
96/12/31
$6.44
G&L Realty Corp.
41
42
43
44
45
Great Lakes REIT
$5,752
($1,252)
$3,316
$1,170
$956
$21.73
$12.96
$18.93
$13.51
$14.11
$8.77
($5.96)
$5.42
($0.61)
$16,414
$7,927
$4.12
$1.08
$8,487
$2,991
$3.03
Highwoods Properties
50
51
52
53
97/12/31
96/12/31
95/12/31
94/12/31
$139,978
$74,852
$42,654
$13,852
$65,126
$32,198
$28,802
$8,822
$5.87
$6.06
$8.67
$3.68
54
93/12/31
$5,030
$210
$6.64
97/12/31
96/12/31
$36,581
$15,025
$21,556
($6,481)
$8.71
$7.37
$1.33
($7.33)
97/12/31
96/12/31
95/12/31
94/12/31
93/12/31
$37,159
$31,403
$53,018
$30,314
$13,923
$5,756
($21,615)
$22,704
$16,391
$1,460
$4.38
$4.10
$6.91
$3.83
$1.76
$0.28
($2.81)
$3.08
$2.07
($1.31)
92/12/31
$12,463
97/12/31
96/12/31
95/12/31
94/12/31
$75,445
$45,279
$27,263
$20,256
$30,166
$18,016
$7,007
($387)
$4.07
$7.10
$8.05
$8.79
($3.03)
($0.94)
($0.74)
($351)
$27
$106
($132)
($128)
$1.74
$3.95
$3.78
$3.11
$3.94
($2.21)
$0.17
$0.67
($0.83)
($0.81)
Kilroy Realty
56
57
Koger Equity
59
60
61
62
63
64
Mack-Cali Realty
65
66
67
68
1
($0.19)
($2.61)
$4.99
($2.96)
$3.07
Nooney Realty Trust
71
72
73
74
75
97/12/31
96/12/31
95/12/31
94/12/31
93/12/31
$277
$628
$601
$495
$627
76
92/12/31
$755
Parkway Properties
77
97/12/31
78
96/12/31
79
95/12/31
80
94/12/31
Prentiss Properties
82|
97/12/31
$4.75
$17,165
$7,439
$3,158
$2,083
$9,726
$4,281
$1,075
($1,613)
$3.44
$3.42
$1.05
$4.37
$58,341
$43,898
$6.38
$0.02
$2.38
($3.32)
|
$3.28
83
96/12/31
84
95/12/31
SL Green Realty Corp.
$14,443
$13,742
$701
$3.09
$4.00
($0.91)
$10,109
$3.66
$1.93
85
97/12/31
$12,166
86
Tower Realty
87
88
96/12/31
$2,057
97/12/31
96/12/31
$12,639
$7,585
$1.73
$5,054
$3.10
$2.58
$0.52
Mean
$14,426
($0.27)
St. Dev.
$24,361
$3.08
M
Appendix F: Set 1997 Data Only
Data for Set- 1997 data only
data #
A OI
(6.00)
0.15
(3.58)
3.40
0.47
(0.39)
0.13
1.46
(0.13)
6.71
1.80
(0.93)
3.14
0.51
(3.22)
(2.21)
1.93
4.88
size
0.1524
1.2950
0.8730
0.4919
1.6662
0.0143
0.8957
2.6863
5.6944
0.0608
0.3479
2.0793
0.3664
0.7396
1.6158
0.0139
0.4350
0.7981
0.2897
0.3555
* shadow number have been adjusted.
outperformance rate
1.52%
-3.64%
2.66%
0.56%
0.49%
-5.81%
-0.24%
0.76%
-3.27%
-4.03%
-1.66%
-7.40%
-3.25%
-4.19%
-1.89%
-1.33%
-3.62%
1.11%
-1.42%
0.60%
% change in sf
88.74%
172.68%
76.77%
223.02%
53.69%
0.00%
117.35%
88.74%
124.16%
-3.85%
42.42%
93.04%
106.18%
10.69%
190.70%
0.00%
129.63%
95.86%
180.10%
38.85%
Set- 1997 data only
SUMMARY OUTPUT
Regression Statistics
0.234177369
Multiple R
0.05483904
R Square
-0.12237864
Adjusted R Square
3.18039929
Standard Error
20
Observations
ANOVA
df
Regression
Residual
Total
Intercept
outperformance rate
size
%change insf
SS
9.390038008
161.8390343
171.2290723
MS
3.130012669
10.11493964
F
0.309444523
Significance F
0.818241583
Coefficients
Standard Error
1.404129534
0.317487722
28.09085579
-21.53253932
-0.396417014
0.577954295
0.403674027
1.150511057
t Stat
0.226109995
-0.76653198
-0.685896821
0.35086497
P-Value
0.823979028
0.454521706
0.502596729
0.730266789
Lower 95%
-2.659133246
-81.08247993
-1.621625111
-2.035299909
3
16
19
UPPER 95%
3.294108689
38.0174013
0.828791083
2.842647963
Lower 95%
-2.659133246
-81.08247993
-1.621625111
-2.035299909
UPPER 95%
3.294108689
38.0174013
0.828791083
2.842647963
Appendix G: Set- All Available Data
Data for Set- all available data
data #
1
3
4
6
8
9
14
15
16
17
18
20
21
22
23
24
25
27
28
29
30
34
35
36
39
41
42
43
44
47
50
51
52
53
56
59
60
61
62
63
64
65
66
67
71
72
73
74
75
76
77
78
79
82
83
85
87
A 01
(15.19)
0.15
3.59
(24.43)
3.40
2.76
0.47
(5.51)
(2.85)
3.73
(18.96)
(0.39)
(1.27)
0.65
1.16
0.53
(2.40)
0.13
1.00
0.33
(7.70)
1.46
(0.15)
0.35
(6.58)
6.71
(4.67)
4.29
(13.26)
(1.23)
(0.93)
(1.15)
2.46
(7.16)
3.14
0.51
(2.71)
2.97
1.51
(0.74)
3.07
(3.22)
(0.30)
(8.00)
(2.21)
0.17
0.67
(0.83)
(5.55)
9.50
1.93
1.06
(6.92)
4.88
(4.43)
1.93
(1.56)
size
outperformance rate
0.1524
1.52%
1.2945
-3.64%
0.9101
-0.83%
0.8730
2.66%
0.4919
0.56%
0.2919
-1.68%
1.6662
0.49%
2.0784
-1.84%
1.0344
1.30%
0.72%
1.1073
0.9683
3.10%
0.0143
-5.81%
-4.11%
0.0274
0.0510
1.34%
0.0671
3.38%
3.69%
0.0797
10.96%
0.1029
0.8957
-0.24%
0.7900
1.79%
5.25%
1.1930
1.3800
1.49%
2.6863
0.76%
2.7286
8.13%
2.7283
-2.20%
-3.27%
5.6944
-4.03%
0.0608
0.1213
-1.39%
0.1751
-3.06%
0.2224
-7.21%
0.3479
-1.66%
-7.40%
2.0793
2.0650
1.58%
1.5305
2.14%
1.5406
2.93%
0.3664
-3.25%
-4.19%
0.7396
1.2810
-0.41%
2.3861
1.54%
3.2357
2.66%
2.86%
3.8430
2.5471
10.08%
-1.89%
1.6158
-6.84%
1.0655
-1.83%
1.0536
-1.33%
0.0139
0.0266
3.68%
0.0494
8.24%
2.14%
0.0651
4.18%
0.0773
0.0998
5.94%
0.4350
-3.62%
0.3632
-5.53%
-0.45%
0.9383
1.11%
0.7981
4.25%
0.7812
-1.42%
0.2897
0.60%
0.3555
% change in sf
88.74%
172.68%
34.67%
76.77%
223.02%
585.91%
53.69%
273.73%
22.92%
35.83%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
117.35%
23.18%
13.76%
0.00%
88.74%
86.02%
67.81%
124.16%
-3.85%
28.85%
3.61%
24.30%
42.42%
93.04%
150.96%
30.73%
397.22%
106.18%
10.69%
-0.14%
-2.96%
0.00%
94.82%
0.00%
190.70%
88.10%
47.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
129.63%
-28.00%
532.30%
95.86%
35.89%
180.10%
38.85%
Set- all available data
SUMMARY OUTPUT
Regression Statistics
0.089090338
Multiple R
0.007937088
R Square
Adjusted R Square
-0.048217416
5.998410997
Standard Error
57
Observations
ANOVA
df
Regression
Residual
Total
Intercept
outperformance rate
size
%change insf
SS
15.25704086
1906.989528
1922.246569
MS
5.085680288
35.98093449
Coefficients
Standard Error
-1.036565816
1.169681586
-0.897256862
20.20796459
-0.163083715
0.719003171
-0.383127895
0.669991621
t Stat
-0.886194865
-0.04440115
-0.226819187
-0.571839831
3
53
56
F
Significance F
0.141343752
0.934758631
P-Value
0.379515975
0.964751514
0.821436937
0.569847977
Lower 95%
-3.382648863
-41.42928173
-1.605220763
-1.726960269
UPPER 95%
1.309517231
39.634768
1.279053334
0.960704479
Lower 95%
UPPER 95%
-3.382648863
1.309517231
-41.42928173
39.634768
-1.605220763
1.279053334
-1.726960269 0.960704479
Appendix H: Set- Restricted Data
Data for Set- eliminating some data
data #
3
8
9
14
15
16
17
20
21
22
23
24
25
27
28
29
34
35
36
39
41
42
43
44
50
51
59
60
61
62
63
64
65
66
71
72
73
74
75
76
77
78
82
A OI
0.15
3.40
2.76
0.47
(5.51)
(2.85)
3.73
(0.39)
(1.27)
0.65
1.16
0.53
(2.40)
0.13
1.00
0.33
1.46
(0.15)
0.35
(6.58)
6.71
(4.67)
4.29
(13.26)
(0.93)
(1.15)
0.51
(2.71)
2.97
1.51
(0.74)
3.07
(3.22)
(0.30)
(2.21)
0.17
0.67
(0.83)
(5.55)
9.50
1.93
1.06
4.88
size
outperformance rate
1.2945
-3.64%
0.56%
0.4919
0.2919
-1.68%
1.6662
0.49%
-1.84%
2.0784
1.0344
1.30%
1.1073
0.72%
-5.81%
0.0143
-4.11%
0.0274
1.34%
0.0510
0.0671
3.38%
3.69%
0.0797
0.1029
10.96%
0.8957
-0.24%
1.79%
0.7900
5.25%
1.1930
2.6863
0.76%
8.13%
2.7286
-2.20%
2.7283
-3.27%
5.6944
-4.03%
0.0608
-1.39%
0.1213
0.1751
-3.06%
0.2224
-7.21%
2.0793
-7.40%
1.58%
2.0650
-4.19%
0.7396
1.2810
-0.41%
2.3861
1.54%
2.66%
3.2357
2.86%
3.8430
2.5471
10.08%
-1.89%
1.6158
-6.84%
1.0655
-1.33%
0.0139
3.68%
0.0266
8.24%
0.0494
2.14%
0.0651
4.18%
0.0773
5.94%
0.0998
-3.62%
0.4350
-5.53%
0.3632
1.11%
0.7981
% change in sf
172.68%
223.02%
585.91%
53.69%
273.73%
22.92%
35.83%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
117.35%
23.18%
13.76%
88.74%
86.02%
67.81%
124.16%
-3.85%
28.85%
3.61%
24.30%
93.04%
150.96%
10.69%
-0.14%
-2.96%
0.00%
94.82%
0.00%
190.70%
88.10%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
129.63%
-28.00%
95.86%
Set- restricted data
SUMMARY OUTPUT
Regression Statistics
0.287616097
Multiple R
0.082723019
R Square
0.012163252
Adjusted R Square
3.712202976
Standard Error
43
Observations
ANOVA
df
Regression
Residual
Total
Intercept
outperformance rate
size
%change insf
SS
48.46786827
537.4375866
585.9054549
MS
16.15595609
13.78045094
F
Significance F
0.332638067
1.172382251
Coefficients
Standard Error
0.806447751
0.332070512
20.94292602
13.11397714
-0.500444316
0.460828189
0.214379156
0.552613594
t Stat
0.411769407
1.59699272
-1.085967238
0.387936811
P-Value
0.682762839
0.118339481
0.284160471
0.700172231
3
39
42
Lower 95%
UPPER 95%
-1.299122428
1.963263452
-5.582570397
47.46842244
-1.432556393 0.431667762
1.332144556
-0.903386245
Lower 95%
-1.299122428
-5.582570397
-1.432556393
-0.903386245
UPPER 95%
1.963263452
47.46842244
0.431667762
1.332144556
Appendix I: Set 1
Data for Set 1
data # cap rate debt ratio
0.99% 16.44%
1
27.26%
3.62%
3
6
5.18% 51.01%
8
3.70% 27.14%
37.73%
14
4.98%
8.75%
20
3.98%
27
3.68% 44.56%
5.32% 46.04%
34
2.61%
19.64%
39
52.29%
41
8.33%
25.00%
47
4.32%
50
5.20% 36.39%
24.81%
6.93%
56
29.17%
59
5.95%
2.70%
34.76%
65
1.85%
31.61%
71
26.93%
4.18%
77
34.22%
4.07%
82
3.28%
34.72%
85
2.06% 37.28%
87
size
0.1524
1.2945
0.8730
0.4919
1.6662
0.0143
0.8957
2.6863
5.6944
0.0608
0.3479
2.0793
0.3664
0.7396
1.6158
0.0139
0.4350
0.7981
0.2897
0.3555
* shadow number have been adjusted.
%change in sf
88.74%
172.68%
76.77%
223.02%
53.69%
0.00%
117.35%
88.74%
124.16%
-3.85%
42.42%
93.04%
106.18%
10.69%
190.70%
0.00%
129.63%
95.86%
180.10%
38.85%
D 01
(6.00)
0.15
(3.58)
3.40
0.47
(0.39)
0.13
1.46
(0.13)
6.71
1.80
(0.93)
3.14
0.51
(3.22)
(2.21)
1.93
4.88
1.98
0.92
market index
11.98%
6.82%
13.12%
11.02%
10.95%
4.65%
10.22%
11.22%
7.19%
6.43%
8.80%
3.06%
7.21%
6.27%
8.57%
9.13%
6.84%
11.57%
9.04%
11.06%
outperformance rate index growth
1.52%
-0.85%
-3.64%
0.33%
2.66%
-1.94%
0.56%
0.18%
0.49%
1.22%
0.00%
-5.81%
-0.24%
0.88%
0.98%
0.76%
4.12%
-3.27%
-4.03%
0.20%
-1.66%
-0.62%
1.44%
-7.40%
1.58%
-3.25%
-0.06%
-4.19%
-1.89%
0.56%
-1.33%
0.00%
-0.40%
-3.62%
1.11%
-1.55%
-1.42%
0.00%
0.60%
-0.57%
Set 1- model A
SUMMARY OUTPUT
Regression Statistics
0.715470485
Multiple R
0.511898015
R Square
0.381737486
Adjusted R Square
0.013767096
Standard Error
20
Observations
ANOVA
df
Regression
Residual
Total
Intercept
debt ratio
size
% change in sf
AOI
SS
0.002981596
0.002842994
0.00582459
MS
0.000745399
0.000189533
F
3.932820628
Significance F
0.022315469
Coefficients
Standard Error
0.030244359
0.011045538
0.046205382
0.029293708
0.000581496
0.002492244
-0.006638296
0.004902371
0.003200193
0.001111544
t Stat
2.738151826
1.577314219
0.233322441
-1.354099119
2.879052491
P-Value
0.015245147
0.135575665
0.818665151
0.195754624
0.011471549
Lower 95%
0.006701338
-0.016232717
-0.004730599
-0.017087458
0.000830992
4
15
19
UPPER 95%
0.05378738
0.108643482
0.005893591
0.003810866
0.005569394
Lower 95%
0.006701338
-0.016232717
-0.004730599
-0.017087458
0.000830992
UPPER 95%
0.05378738
0.108643482
0.005893591
0.003810866
0.005569394
Set 1- model C
SUMMARY OUTPUT
Regression Statistics
0.685192726
Multiple R
0.469489071
R Square
Adjusted R Square
0.280020883
Standard Error
0.014856477
20
Observations
ANOVA
SS
0.002734581
0.003090009
0.00582459
MS
0.000546916
0.000220715
F
2.477930857
Significance F
0.08294851
Coefficients
Standard Error
0.015168358
0.043123898
0.094022935
0.033014969
-0.002865001
0.004330178
-0.003720801
0.005448142
0.161875495
-0.299756077
0.48658363
0.224444409
t Stat
2.843016847
2.847888058
-0.661635864
-0.682948543
-1.851769337
0.461265845
P-Value
0.013028115
0.012903563
0.518946771
0.505784487
0.085263087
0.651692734
Lower 95%
0.010590976
0.023212807
-0.012152317
-0.015405913
-0.646944792
-0.819174611
df
Regression
Residual
Total
Intercept
debt ratio
size
%change insf
outperformance rate
index growth
5
14
19
UPPER 95%
0.07565682
0.164833063
0.006422315
0.007964312
0.047432638
1.268063429
Lower 95%
0.010590976
0.023212807
-0.012152317
-0.015405913
-0.646944792
-0.819174611
UPPER 95%
0.07565682
0.164833063
0.006422315
0.007964312
0.047432638
1.268063429
Set 1- model D
SUMMARY OUTPUT
Regression Statistics
0.788825864
Multiple R
0.622246244
R Square
Adjusted R Square
0.447898357
0.013009643
Standard Error
20
Observations
ANOVA
SS
0.003624329
0.002200261
0.00582459
MS
0.000604055
0.000169251
F
3.568992171
Standard Error
Coefficients
0.043275798
0.01328292
0.030640874
0.070750354
-0.000572735
0.00392147
-0.004488445
0.004782608
0.002565015
0.001118721
0.143748591
-0.245021235
0.031411697
0.434333401
t Stat
3.258003469
2.30901883
-0.146051157
-0.938493337
2.292810105
-1.704512254
0.072321624
P-Value
0.0062315
0.038017024
0.886121423
0.365101267
0.039177849
0.112052293
0.943446813
df
Regression
Residual
Total
Intercept
debt ratio
size
%change in sf
A 01
market index
index growth
6
13
19
Significance F
0.025864058
UPPER 95%
Lower 95%
0.014579801
0.071971796
0.004554784 0.136945925
-0.009044555
0.007899084
-0.014820639
0.005843748
0.004981865
0.000148166
0.065528656
-0.555571126
0.969731783
-0.906908389
Lower 95%
0.014579801
0.004554784
-0.009044555
-0.014820639
0.000148166
-0.555571126
-0.906908389
UPPER 95%
0.071971796
0.136945925
0.007899084
0.005843748
0.004981865
0.065528656
0.969731783
Appendix J: Set 2
Data for Set 2
data #
1
3
4
6
8
9
14
15
16
17
18
20
21
22
23
24
25
27
28
29
30
34
35
36
39
41
42
43
44
47
50
51
52
53
56
59
60
61
62
63
64
65
66
67
71
72
73
74
75
76
77
78
79
82
83
85
87
cap rate
0.99%
3.62%
5.43%
5.18%
3.70%
1.56%
4.98%
4.29%
10.25%
9.69%
9.69%
3.98%
6.36%
8.26%
8.02%
5.09%
4.76%
3.68%
7.19%
5.62%
6.27%
5.32%
5.31%
5.79%
2.61%
8.33%
8.63%
10.55%
11.04%
4.32%
5.20%
5.64%
8.02%
6.33%
6.93%
5.95%
5.54%
9.42%
5.05%
2.31%
3.78%
2.70%
4.70%
8.49%
1.85%
4.83%
5.46%
4.50%
6.27%
8.36%
4.18%
5.28%
3.76%
4.07%
2.84%
3.28%
2.06%
debt ratio
16.44%
27.26%
29.08%
51.01%
27.14%
21.40%
37.73%
44.93%
75.45%
62.92%
66.67%
8.75%
12.94%
13.14%
14.64%
12.34%
13.61%
44.56%
69.08%
66.19%
81.37%
46.04%
38.17%
43.24%
19.64%
52.29%
79.54%
82.28%
67.97%
25.00%
36.39%
41.93%
33.65%
30.41%
24.81%
29.17%
35.80%
45.65%
53.17%
54.23%
28.61%
34.76%
27.21%
42.06%
31.61%
37.15%
44.65%
45.35%
48.45%
54.41%
26.93%
44.54%
41.36%
34.22%
25.39%
34.72%
37.28%
size
0.1524
1.2945
0.9101
0.8730
0.4919
0.2919
1.6662
2.0784
1.0344
1.1073
0.9683
0.0143
0.0274
0.0510
0.0671
0.0797
0.1029
0.8957
0.7900
1.1930
1.3800
2.6863
2.7286
2.7283
5.6944
0.0608
0.1213
0.1751
0.2224
0.3479
2.0793
2.0650
1.5305
1.5406
0.3664
0.7396
1.2810
2.3861
3.2357
3.8430
2.5471
1.6158
1.0655
1.0536
0.0139
0.0266
0.0494
0.0651
0.0773
0.0998
0.4350
0.3632
0.9383
0.7981
0.7812
0.2897
0.3555
% change in sf
88.74%
172.68%
34.67%
76.77%
223.02%
585.91%
53.69%
273.73%
22.92%
35.83%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
117.35%
23.18%
13.76%
0.00%
88.74%
86.02%
67.81%
124.16%
-3.85%
28.85%
3.61%
24.30%
42.42%
93.04%
150.96%
30.73%
397.22%
106.18%
10.69%
-0.14%
-2.96%
0.00%
94.82%
0.00%
190.70%
88.10%
47.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
129.63%
-28.00%
532.30%
95.86%
35.89%
180.10%
38.85%
A 01
(15.19)
0.15
3.59
(24.43)
3.40
2.76
0.47
(5.51)
(2.85)
3.73
(18.96)
(0.39)
(1.27)
0.65
1.16
0.53
(2.40)
0.13
1.00
0.33
(7.70)
1.46
(0.15)
0.35
(6.58)
6.71
(4.67)
4.29
(13.26)
(1.23)
(0.93)
(1.15)
2.46
(7.16)
3.14
0.51
(2.71)
2.97
1.51
(3.81)
6.14
(3.22)
(0.30)
(8.00)
(2.21)
0.17
0.67
(0.83)
(5.55)
9.50
1.93
1.06
(6.92)
4.88
(4.43)
1.93
(1.56)
market index outperformance rate index growth dummy 92 dummy 93 dummy 94 dummy 95 dummy 96
-0.85%
11.98%
1.52%
0.33%
6.82%
-3.64%
-0.08%
-0.83%
5.33%
-1.94%
2.66%
13.12%
0.18%
11.02%
0.56%
-1.44%
-1.68%
4.47%
1.22%
0.49%
10.95%
4.12%
-1.84%
4.31%
0.00%
1.30%
7.04%
0.00%
0.72%
4.09%
0.00%
3.10%
1.09%
0.000/
-5.81%
4.65%
0.00%
-4.11%
2.04%
0.00%
1.34%
7.08%
0.00%
3.38%
6.74%
0.00%
3.69%
1.69%
0.00%
10.96%
4.27%
0.88%
-0.24%
10.22%
-0.61%
1.79%
7.95%
-0.79%
5.25%
11.00%
0.00%
1.49%
4.86%
0.98%
0.76%
11.22%
-3.52%
8.13%
14.28%
2.00%
-2.20%
3.55%
4.12%
-3.27%
7.19%
0.20%
6.43%
-4.03%
0.00%
-1.39%
4.76%
0.00%
-3.06%
2.68%
0.00%
-7.21%
-3.84%
-0.62%
-1.66%
8.80%
1.44%
-7.40%
3.06%
-0.11%
1.58%
7.74%
-0.03%
2.14%
7.88%
-0.04%
2.93%
6.29%
1.58%
-3.25%
7.21%
-0.06%
-4.19%
6.27%
0.01%
-0.41%
5.75%
-0.16%
1.54%
7.29%
0.00%
2.66%
6.03%
1.20%
2.86%
0.85%
0.00%
10.08%
3.39%
0.56%
-1.89%
8.57%
0.36%
-6.84%
-0.69%
0.09%
-1.83%
3.91%
0.00%
-1.33%
9.13%
0.00%
3.68%
9.84%
0.00%
8.24%
13.99%
0.00%
2.14%
5.50%
0.00%
4.18%
2.17%
0.00%
5.94%
-0.75%
-0.40%
-3.62%
6.84%
6.15%
-5.53%
0.63%
0.13%
-0.45%
5.30%
-1.55%
1.11%
11.57%
0.12%
4.25%
10.41%
0.00%
-1.42%
9.04%
-0.57%
0.60%
11.06%
Set 2- model A
SUMMARY OUTPUT
Regression Statistics
0.743624535
R
Multiple
0.552977449
R Square
0.467377386
Adjusted R Square
0.017445625
Standard Error
57
Observations
ANOVA
SS
0.017694933
0.014304443
0.031999376
MS
0.001966104
0.00030435
F
6.460012169
Significance F
6.37374E-06
Standard Error
Coefficients
0.006667974
0.035433288
0.014306227
0.045635414
0.002102956
-0.003539683
0.002025237
-0.005168583
0.0004286
0.000183543
0.011198026
0.008637274
0.010038122
0.009141548
0.00809238
0.021911053
0.007165745
0.025686081
0.006312938
0.007414564
t Stat
5.313951063
3.189898679
-1.683194393
-2.552087491
0.428239705
0.77132111
0.910683083
2.707615528
3.584565244
1.174502842
P-Value
2.88859E-06
0.002535137
0.098969584
0.014018499
0.670432756
0.444377299
0.36710947
0.009419684
0.000800035
0.246110301
Lower 95%
0.022019067
0.016855025
-0.00777028
-0.009242831
-0.000678687
-0.013890228
-0.01105253
0.0056313
0.011270475
-0.005285417
df
Regression
Residual
Total
Intercept
debt ratio
size
% change insf
AOI
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
9
47
56
UPPER 95%
0.048847509
0.074415804
0.000690914
-0.001094335
0.001045774
0.031164776
0.029335627
0.038190807
0.040101687
0.020114545
Lower 95%
0.022019067
0.016855025
-0.00777028
-0.009242831
-0.000678687
-0.013890228
-0.01105253
0.0056313
0.011270475
-0.005285417
UPPER 95%
0.048847509
0.074415804
0.000690914
-0.001094335
0.001045774
0.031164776
0.029335627
0.038190807
0.040101687
0.020114545
Set 2- model B
SUMMARY OUTPUT
Regression Statistics
0.790315902
Multiple R
0.624599225
R Square
0.54299036
Adjusted R Square
0.016159928
Standard Error
57
Observations
ANOVA
SS
0.019986786
0.012012591
0.031999376
MS
0.001998679
0.000261143
F
7.653570855
Significance F
4.83625E-07
Standard Error
Coefficients
0.00958778
0.057436137
0.013186463
0.041228504
0.00211875
-0.002102431
0.001877448
-0.005821523
0.080557449
-0.240720596
0.192287867
-0.249173016
0.011589312
-0.006911118
0.010841968
-0.009525105
0.008390404
0.010606468
0.006771155
0.021679075
0.006189247
0.001410564
tStat
5.990556652
3.126577969
-0.992297378
-3.100764705
-2.988185442
-1.295833272
-0.596335547
-0.878540234
1.264118968
3.201680414
0.227905551
P-Value
2.9917E-07
0.003062905
0.326243718
0.003291801
0.00449121
0.201497541
0.553874555
0.384216702
0.212555531
0.002479253
0.82072969
Lower 95%
0.038136956
0.014685557
-0.00636725
-0.009600625
-0.402874176
-0.636228048
-0.03023917
-0.031348834
-0.006282522
0.008049459
-0.011047733
df
Regression
Residual
Total
Intercept
debt ratio
size
% change insf
market index
index growth
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
10
46
56
UPPER 95%
0.076735318
0.067771452
0.002162389
-0.002042421
-0.078567017
0.137882015
0.016416934
0.012298624
0.027495459
0.03530869
0.01386886
Lower 95%
0.038136956
0.014685557
-0.00636725
-0.009600625
-0.402874176
-0.636228048
-0.03023917
-0.031348834
-0.006282522
0.008049459
-0.011047733
UPPER 95%
0.076735318
0.067771452
0.002162389
-0.002042421
-0.078567017
0.137882015
0.016416934
0.012298624
0.027495459
0.03530869
0.01386886
SUMMARY OUTPUT
Set 2- model C
Regression Statistics
0.790310894
Multiple R
0.624591309
R Square
0.542980724
Adjusted R Square
0.016160098
Standard Error
57
Observations
ANOVA
df
Regression
Residual
Total
Intercept
debt ratio
size
% change insf
outperformance rate
index growth
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
SS
0.019986532
0.012012844
0.031999376
MS
0.001998653
0.000261149
F
7.653312484
Significance F
4.83836E-07
Coefficients
Standard Error
0.006269045
0.032255863
0.041229167
0.013186587
-0.00210283
0.002118746
0.001877465
-0.005821425
-0.240707044 0.080558138
0.192288498
-0.249148995
0.034380022
0.013080557
0.02047855
0.009954494
0.027688516
0.007750645
0.033033525
0.007013682
0.011772764
0.006010403
t Stat
5.145259222
3.126598704
-0.992488092
-3.100683584
-2.987991638
-1.2957041
2.628330025
2.057216522
3.572414383
4.709868959
1.958731214
P-Value
5.39035E-06
0.003062728
0.326151723
0.003292545
0.004493589
0.201541685
0.011623903
0.045360491
0.000842977
2.31099E-05
0.056222993
Lower 95%
0.019636941
0.014685969
-0.006367642
-0.009600563
-0.402862012
-0.636205297
0.008050251
0.000441212
0.012087292
0.018915728
-0.000325538
10
46
56
UPPER 95%
0.044874785
0.067772366
0.002161981
-0.002042288
-0.078552077
0.137907306
0.060709793
0.040515888
0.043289741
0.047151322
0.023871067
Lower 95%
0.019636941
0.014685969
-0.006367642
-0.009600563
-0.402862012
-0.636205297
0.008050251
0.000441212
0.012087292
0.018915728
-0.000325538
UPPER 95%
0.044874785
0.067772366
0.002161981
-0.002042288
-0.078552077
0.137907306
0.060709793
0.040515888
0.043289741
0.047151322
0.023871067
Set 2- model D
SUMMARY OUTPUT
Regression Statistics
0.790846837
Multiple R
0.62543872
R Square
0.533879296
Adjusted R Square
0.016320217
Standard Error
57
Observations
ANOVA
SS
0.020013649
0.011985727
0.031999376
MS
0.001819423
0.000266349
F
6.830959526
Significance F
1.3401E-06
Coefficients
Standard Error
0.032204945
0.006333172
0.041833444
0.013451232
-0.002090042
0.002140115
-0.005748671
0.001909729
0.000128349
0.000402254
-0.239624959
0.081426985
-0.251539661
0.194338235
0.033560987
0.013457245
0.010230453
0.021083684
0.007832098
0.027774684
0.007123945
0.03279069
0.011630663
0.006086272
t Stat
5.085121146
3.110008467
-0.976602744
-3.010203035
0.319074302
-2.942819989
-1.294339536
2.493897332
2.060874866
3.546263569
4.60288346
1.910966742
P-Value
6.925 1E-06
0.003242165
0.333986938
0.004268926
0.751146622
0.005126742
0.202151169
0.016376834
0.04512256
0.000926019
3.4071E-05
0.062391725
Lower 95%
0.019449283
0.014741274
-0.006400454
-0.009595062
-0.000681831
-0.403627318
-0.642956942
0.006456706
0.000478494
0.012000029
0.018442328
-0.000627717
df
Regression
Residual
Total
Intercept
debt ratio
size
%change in sf
A 01
outperformance rate
index growth
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
11
45
56
UPPER 95%
0.044960607
0.068925614
0.00222037
-0.00190228
0.000938529
-0.0756226
0.13987762
0.060665268
0.041688873
0.043549338
0.047139052
0.023889043
Lower 95%
0.019449283
0.014741274
-0.006400454
-0.009595062
-0.000681831
-0.403627318
-0.642956942
0.006456706
0.000478494
0.012000029
0.018442328
-0.000627717
UPPER 95%
0.044960607
0.068925614
0.00222037
-0.00190228
0.000938529
-0.0756226
0.13987762
0.060665268
0.041688873
0.043549338
0.047139052
0.023889043
Appendix K: Set 3
Data for Set 3
data #
3
8
9
14
15
16
17
20
21
22
23
24
25
27
28
29
34
35
36
39
41
42
43
44
50
51
59
60
61
62
63
64
65
66
71
72
73
74
75
76
77
78
82
cap rate debt ratio
27.26%
3.62%
27.14%
3.70%
21.40%
1.56%
37.73%
4.98%
44.93%
4.29%
10.25% 75.45%
62.92%
9.69%
8.75%
3.98%
12.94%
6.36%
13.14%
8.26%
14.64%
8.02%
12.34%
5.09%
13.61%
4.76%
44.56%
3.68%
7.19% 69.08%
66.19%
5.62%
46.04%
5.32%
38.17%
5.31%
43.24%
5.79%
19.64%
2.61%
52.29%
8.33%
79.54%
8.63%
10.55% 82.28%
67.97%
11.04%
36.39%
5.20%
41.93%
5.64%
29.17%
5.95%
35.80%
5.54%
45.65%
9.42%
53.17%
5.05%
54.23%
2.31%
28.61%
3.78%
34.76%
2.70%
27.21%
4.70%
31.61%
1.85%
37.15%
4.83%
44.65%
5.46%
45.35%
4.50%
48.45%
6.27%
54.41%
8.36%
26.93%
4.18%
44.54%
5.28%
34.22%
4.07%
size
1.2945
0.4919
0.2919
1.6662
2.0784
1.0344
1.1073
0.0143
0.0274
0.0510
0.0671
0.0797
0.1029
0.8957
0.7900
1.1930
2.6863
2.7286
2.7283
5.6944
0.0608
0.1213
0.1751
0.2224
2.0793
2.0650
0.7396
1.2810
2.3861
3.2357
3.8430
2.5471
1.6158
1.0655
0.0139
0.0266
0.0494
0.0651
0.0773
0.0998
0.4350
0.3632
0.7981
% change in sf
172.68%
223.02%
585.91%
53.69%
273.73%
22.92%
35.83%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
117.35%
23.18%
13.76%
88.74%
86.02%
67.81%
124.16%
-3.85%
28.85%
3.61%
24.30%
93.04%
150.96%
10.69%
-0.14%
-2.96%
0.00%
94.82%
0.00%
190.70%
88.10%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
129.63%
-28.00%
95.86%
A 01
0.15
3.40
2.76
0.47
(5.51)
(2.85)
3.73
(0.39)
(1.27)
0.65
1.16
0.53
(2.40)
0.13
1.00
0.33
1.46
(0.15)
0.35
(6.58)
6.71
(4.67)
4.29
(13.26)
(0.93)
(1.15)
0.51
(2.71)
2.97
1.51
(3.81)
6.14
(3.22)
(0.30)
(2.21)
0.17
0.67
(0.83)
(5.55)
9.50
1.93
1.06
4.88
market index
6.82%
11.02%
4.47%
10.95%
4.31%
7.04%
4.09%
4.65%
2.04%
7.08%
6.74%
1.69%
4.27%
10.22%
7.95%
11.00%
11.22%
14.28%
3.55%
7.19%
6.43%
4.76%
2.68%
-3.84%
3.06%
7.74%
6.27%
5.75%
7.29%
6.03%
0.85%
3.39%
8.57%
69
-0. %
9.13%
9.84%
13.99%
5.50%
2.17%
-0.75%
6.84%
0.63%
11.57%
outperformance rate
-3.64%
0.56%
-1.68%
0.49%
-1.84%
1.30%
0.72%
-5.81%
-4.11%
1.34%
3.38%
3.69%
10.96%
-0.24%
1.79%
5.25%
0.76%
8.13%
-2.20%
-3.27%
-4.03%
-1.39%
-3.06%
-7.21%
-7.40%
1.58%
-4.19%
-0.41%
1.54%
2.66%
2.86%
10.08%
-1.89%
-6.84%
-1.33%
3.68%
8.24%
2.14%
4.18%
5.94%
-3.62%
-5.53%
1.11%
index growth dummy 92
0
0.33%
0
0.18%
0
-1.44%
0
1.22%
0
4.12%
0
0.00%
0
0.00%
0
0.00%
0
0.00%
0
0.00%
0
0.00%
0
0.00%
1
0.00%
0
0.88%
0
-0.61%
0
-0.79%
0
0.98%
0
-3.52%
0
2.00%
0
4.12%
0
0.20%
0
0.00%
0
0.00%
0
000%
0
1.44%
0
-0.11%
0
-0.06%
0
0.01%
0
-0.16%
0
0.00%
0
1.20%
1
0.00%
0
0.56%
0
0.36%
0
0.00%
0
0.00%
0
0.00%
0
0.00%
0
0.00%
1
0.00%
0
-0.40%
0
6.15%
0
-1.55%
dummy 93
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
dummy 94 dummy 95
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
dummy 96
0
0
1
0
1
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
Set 3- model A
SUMMARY OUTPUT
Regression Statistics
0.789055673
Multiple R
0.622608855
R Square
0.519683997
Adjusted R Square
0.016368441
Standard Error
43
Observations
ANOVA
SS
0.014586536
0.008841554
0.02342809
MS
0.001620726
0.000267926
F
6.049159246
Significance F
5.47457E-05
Coefficients
Standard Error
0.038778063
0.007412775
0.046556936
0.01492565
-0.004010332
0.00209002
-0.006167761
0.002634303
5.01945E-05
0.000735703
0.006015404
0.011070698
-0.003615082
0.010826636
0.019693099
0.009178053
0.020894811
0.008330446
0.006763928
0.006903052
t Stat
5.231248174
3.119256748
-1.918800579
-2.341325972
0.068226514
0.543362708
-0.33390634
2.145672932
2.508246523
0.979846026
P-Value
9.32579E-06
0.003749351
0.063693637
0.025404355
0.946017049
0.590532023
0.740562902
0.039349158
0.017227532
0.334291387
Lower 95%
0.023696648
0.016190448
-0.008262514
-0.011527295
-0.001446606
-0.016508118
-0.025642056
0.001020196
0.003946379
-0.007280448
df
Regression
Residual
Total
Intercept
debt ratio
size
% change in sf
A OI
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
9
33
42
UPPER 95%
0.053859479
0.076923424
0.000241849
-0.000808228
0.001546995
0.028538926
0.018411891
0.038366002
0.037843244
0.020808304
Lower 95%
0.023696648
0.016190448
-0.008262514
-0.011527295
-0.001446606
-0.016508118
-0.025642056
0.001020196
0.003946379
-0.007280448
UPPER 95%
0.053859479
0.076923424
0.000241849
-0.000808228
0.001546995
0.028538926
0.018411891
0.038366002
0.037843244
0.020808304
Set 3- model B
SUMMARY OUTPUT
Regression Statistics
0.830248482
Multiple R
0.689312541
R Square
0.59222271
Adjusted R Square
0.015081874
Standard Error
43
Observations
ANOVA
SS
0.016149276
0.007278814
0.02342809
MS
0.001614928
0.000227463
F
7.099739852
Standard Error
Coefficients
0.009736226
0.057071013
0.013804626
0.043382914
0.002070285
-0.002332239
0.002455157
-0.006905722
0.080947633
-0.210265562
0.187151703
-0.293243974
0.01119752
-0.007730491
0.011208358
-0.018368941
0.009250151
0.009168958
0.007734161
0.018860659
0.006535668
0.001887008
tStat
5.86171827
3.142635854
-1.126530448
-2.812741255
-2.597550465
-1.566878472
-0.690375266
-1.63886112
0.991222455
2.438617385
0.288724673
P-Value
1.62036E-06
0.003596703
0.268318456
0.008325106
0.014072707
0.126979874
0.494935503
0.111038666
0.329013374
0.020473261
0.774654221
df
Regression
Residual
Total
Intercept
debt ratio
size
%change insf
market index
index growth
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
10
32
42
Significance F
9.18716E-06
Lower 95%
0.037238987
0.015263834
-0.006549267
-0.011906709
-0.375150355
-0.674459195
-0.030539075
-0.041199599
-0.009672968
0.003106702
-0.011425699
UPPER 95%
0.07690304
0.071501993
0.00188479
-0.001904735
-0.045380769
0.087971247
0.015078092
0.004461716
0.028010883
0.034614615
0.015199716
Lower 95%
0.037238987
0.015263834
-0.006549267
-0.011906709
-0.375150355
-0.674459195
-0.030539075
-0.041199599
-0.009672968
0.003106702
-0.011425699
UPPER 95%
0.07690304
0.071501993
0.00188479
-0.001904735
-0.045380769
0.087971247
0.015078092
0.004461716
0.028010883
0.034614615
0.015199716
Set 3- model C
SUMMARY OUTPUT
Regression Statistics
0.830242824
Multiple R
0.689303147
R Square
0.59221038
Adjusted R Square
0.015082102
Standard Error
43
Observations
ANOVA
SS
0.016149056
0.007279034
0.02342809
MS
0.001614906
0.00022747
F
7.099428412
Standard Error
Coefficients
0.006970846
0.03507662
0.013804819
0.043383608
0.002070285
-0.002332641
0.002455191
-0.006905593
0.080948562
-0.210249717
0.187153048
-0.293219169
0.013144994
0.028336134
0.010740001
0.007838482
0.008596796
0.024089708
0.008313865
0.028778255
0.00647384
0.01093803
t Stat
5.031902663
3.14264226
-1.126724684
-2.812649718
-2.597324929
-1.56673467
2.155659793
0.729839926
2.802172634
3.461477488
1.689573643
P-Value
1.81117E-05
0.003596643
0.268237521
0.008326998
0.014080308
0.127013498
0.038734946
0.470797322
0.008546295
0.001545347
0.100831201
df
Regression
Residual
Total
Intercept
debt ratio
size
% change insf
outperformance rate
index growth
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
10
32
42
Significance F
9.19115E-06
UPPER 95%
Lower 95%
0.049275757
0.020877483
0.071503081
0.015264136
0.001884387
-0.006549669
-0.001904536
-0.011906649
-0.045363032
-0.375136402
0.087998792
-0.674437131
0.055111588
0.001560681
0.029715129
-0.014038166
0.041600794
0.006578621
0.045713029
0.011843482
0.024124799
-0.00224874
Lower 95%
0.020877483
0.015264136
-0.006549669
-0.011906649
-0.375136402
-0.674437131
0.001560681
-0.014038166
0.006578621
0.011843482
-0.00224874
UPPER 95%
0.049275757
0.071503081
0.001884387
-0.001904536
-0.045363032
0.087998792
0.055111588
0.029715129
0.041600794
0.045713029
0.024124799
Set 3- model D
SUMMARY OUTPUT
Regression Statistics
0.831060713
Multiple R
0.690661909
R Square
0.58089678
Adjusted R Square
0.015289888
Standard Error
43
Observations
ANOVA
SS
0.016180889
0.007247201
0.02342809
MS
0.00147099
0.000233781
F
6.292179631
Significance F
2.42684E-05
Coefficients
Standard Error
0.007146945
0.034682975
0.043384185
0.013995008
-0.002214389
0.002123131
-0.006937277
0.002490497
0.000262007
0.000710031
0.082914693
-0.214621822
-0.284851188
0.191081832
0.027858558
0.013388791
0.008978931
0.011318105
0.024793644
0.00892157
0.008432269
0.028872453
0.011442399
0.006703847
t Stat
4.852839049
3.099975782
-1.042982713
-2.785499354
0.36900813
-2.588465491
-1.490728789
2.080737472
0.793324656
2.779067333
3.424043064
1.706840588
P-Value
3.2734E-05
0.004098043
0.305022054
0.009034683
0.714630124
0.014546805
0.146138767
0.045811328
0.433619136
0.009178505
0.001755361
0.097851284
Lower 95%
0.020106676
0.014841163
-0.006544545
-0.012016682
-0.001186112
-0.383727547
-0.674565372
0.000551925
-0.014104508
0.006597972
0.011674717
-0.002230196
df
Regression
Residual
Total
Intercept
debt ratio
size
%change insf
A OI
outperformance rate
index growth
dummy 92
dummy 93
dummy 94
dummy 95
dummy 96
11
31
42
UPPER 95%
0.049259274
0.071927207
0.002115767
-0.001857873
0.001710127
-0.045516096
0.104862995
0.055165192
0.032062371
0.042989316
0.046070189
0.025114994
Lower 95%
0.020106676
0.014841163
-0.006544545
-0.012016682
-0.001186112
-0.383727547
-0.674565372
0.000551925
-0.014104508
0.006597972
0.011674717
-0.002230196
UPPER 95%
0.049259274
0.071927207
0.002115767
-0.001857873
0.001710127
-0.045516096
0.104862995
0.055165192
0.032062371
0.042989316
0.046070189
0.025114994
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100