IS REAL ESTATE AN IMPORTANT FACTOR IN CORPORATE
VALUATION: EVIDENCE FROM LISTED RETAIL FIRMS?
Executive Summary
Since real estate is an important asset of many retail firms, this study examines whether there is a
systematic real estate factor in retail firms’ common stock returns and whether this risk is priced in the
stock market. In addition, whether the real estate risk sensitivities of retail stocks are linked to each firm’s
real estate intensity is investigated. The theoretical framework used is a three-index model with domestic
stock market and retail market factors as well as a real estate risk factor. The results indicate that property
market risks carry positive risk premia after controlling for sensitivities to general market and retail market
risks, implying that real estate is an important factor priced in the stock market value of the sample retail
firms. However, higher real estate concentration does not necessarily cause higher real estate exposure after
controlling for firm size, leverage and growth, implying that stock market investors are probably unwilling
or unable to understand and capture the full risk real estate ownership risk in corporate valuation.
Introduction
This paper addresses two issues regarding the presence and significance of a real estate factor in
retail firms’ corporate valuation in an international environment. First, we examine whether there is a
systematic real estate factor in retail firms’ common stock returns and whether this risk carries positive risk
premium; and second, we evaluate if relative real estate ownership (real estate intensity) of each firm,
measured by its corporate real estate ratio (CRER) affects the real estate risk sensitivities of retail stocks.
Our hypothesis is that higher CRER levels should result in greater sensitivities to the real estate factor. The
theoretical framework used is a three-index model with domestic stock market and retail market factors as
well as a real estate risk factor as the three explanatory variables. The maximum likelihood (ML) and
Generalized Method of Moment (GMM) methods are used to jointly estimate risk sensitivities and risk
premia of a restricted three-factor system model.
The inclusion of a real estate factor to retailers’ asset portfolio can probably be justified on
theoretical grounds. In the Capital Asset Pricing Model (CAPM) framework, the expected rates of return on
assets are determined by their covariance with the market portfolio of all risky assets. As a risky asset,
significant amount of capital is locked up in real estate in many retail firms worldwide. It is highly likely
that real estate returns play an important role in explaining the cross-sectional variation of expected retail
stock returns. In addition, real estate market risk could possibly be one of the macroeconomic risks
specified by the Arbitrage Pricing Model (APT).
From an international perspective, the ownership of significant amount of real estate by
corporations in the USA is well documented, estimated approximately at about 25% of corporate wealth
(Zeckhauser and Silverman, 1983; Rodriguez and Sirmans, 1996). In the UK, real estate represents on
average 30%-40% of total assets and 100% of capital in the balance sheets of industrial companies (Liow
1999). Moreover, many of the largest non-real estate companies control property portfolios that are
comparable in value terms with those owned by mainstream real estate companies. Our study focuses on
listed retail firms because real estate forms a significant proportion of the total assets in the balance sheet of
many leading retailers. Real estate has always been recognized as a key value driver in the retail industry.
Guy (1999) has further pointed out that many retailers’ corporate real estate (CRE)1 cannot be simply
regarded as sunk or negative costs to the retailers. This is because properties have significant values on
balance sheets and can usually be disposed of on the open market at substantial financial gains over the
original cost of purchase. The large retailers, led by Wal-Mart in the USA and Carrefour in France, have
grown in retail sales with increased owned store outlets over the past decades. In the United Kingdom,
many retailers have a long heritage of property ownership that is up to 80 per cent of net book value in
property (Liow, 2001). The size of CRE portfolio will have an impact on these retailers’ balance sheets as
property values fluctuate over their holding periods.
In understanding the role of CRE in maximizing shareholders’ wealth, our study brings two
contributions to the extant literature. First, it contributes to the empirical APT asset pricing literature
focusing on whether real estate exposure of retail firms is “explicitly” recognized in the stock market.
Although there are increasing numbers of studies that examine the role of CRE in maximizing
shareholders’ wealth, to-date only Liow (1997, 2001) have studied the pricing of CRE in the stock market
on a subset of UK and Singapore industrial firms only. With a sample of 556 retail firms from 15 countries,
our international study will probably allow us generalize the results to the rest of the world by revealing
whether the returns of property intensive retail firms are sensitive to a real estate factor and whether this
factor is priced in the stock market across three continents and eight retail segments. Evidence of a “priced”
real estate systematic risk factor in corporate valuation implies that stock market investors are probably
able to understand and value the real estate position of the retail firms and accordingly provides some
1
Brueggeman, Fisher and Porter (2001) defined corporate real estate (CRE) as the land and buildings
owned by business firms not primarily in the real estate business. A good example is the real estate assets
held by retail firms whose primary business is in retailing.
1
support against the “market undervaluation” hypothesis of CRE.
2
Our second contribution extends the
Fama and French (1992)’s three-factor model (size, book/market ratio and leverage) to explore the link
between real estate risk sensitivities of the retail stocks and each firm’s average real estate intensity. Our
hypothesis is that if greater real estate intensity, as measured by higher CRER levels, results in greater
sensitivities to the real estate factor, then real estate intensity of each firm should probably be a good proxy
for real estate systematic exposure in the stock market. Accordingly, corporate management can use the
firm’s CRER as a gauge of the extent to which stock returns are influenced by changes in the real estate
market. Employing a three-index model (stock market, retail market and real estate market) on an
international dataset, our results suggest that real estate is an important factor priced in the stock market
value of the sample retail firms. However, this real estate beta appears to be weakly and inconsistently
related to how property intensive the firm is after controlling for Fama and French (1992)’ three factors,
implying that stock market investors are probably unwilling or unable to understand and capture the full
risk real estate ownership risks in corporate valuation. .
This research is timely when capital markets are in constrained cycles, and with many
corporations’ shares selling at or below book value per share – and in some cases, below the market values
of their CRE, being vulnerable for takeovers and leveraging on their CRE to raise the required capital for
such takeovers. Even some large retailers with a long heritage of freehold property ownership are not
immune to this trend. The pressure to divest /outsource their CRE is fuelled by falling stock market
valuations, fear of predators and concerns about the long-term value of property in a world where electronic
commerce is becoming increasingly important.
This paper is organized as follows. The next section presents a brief literature review. The third
section describes the retail sample and their real estate holding characteristics. In the fourth section, the
2
The extant literature has revealed that, the belief that CRE is undervalued – at least until a company is
“put into play”, appears almost universally held by the corporate management and investment bankers. For
example, properties that were purchased years ago were carried on the balance sheet for a fraction of their
market value - real estate has been categorized as “latent assets” where value of the assets owned by a
corporation might not be accurately reflected in its share prices (Brennan, 1990). The extant CRE literature
remains inconclusive as to why CRE assets are undervalued. One reason that has been frequently cited is
that stock market investors have no idea what the corporate property assets are worth or whether the firm is
over- or undervalued in property for its current or perspective level of trading. In the context of present
study, if retail stocks’ real estate sensitivities are “priced” in the stock market, then it can be implied that
property risks are paid an ex ante premium in the stock market and accordingly the value of the retail
portfolio should be factored into share prices. However, to what extent that the property portfolio value is
captured in the stock market valuation always remains contentious.
2
multiple-index APT model to include the three factors in this study is justified and the two empirical
procedures are explained. In the fifth section, we discuss the empirical results and implications. The article
ends with a summary of the key findings
Literature Review
This study covers two main branches of literature: the CRE and APT literature. The outcome of
literature review is presented below.
Liow and Nappi-Choulet (2008) discuss CRE ownership and management from three inter-linked
perspectives. The business perspective of CRE calls for the management CRE as a business function as
well as the maximization of CRE contribution to business performance and enterprise strategy. This is even
more important for retailers as real estate has always been recognized as a key value driver in the retail
industry. Although the trend to get capital out of the CRE is happening around the globe, many large retailers
may still favour CRE freehold ownership particularly when properties house a strategic function or are
integral to their retail operations. The financial perspective of CRE highlights the importance of CRE in
influencing the profitability and cost of capital of many business firms with significant CRE ownership. It
is thus necessary for real estate to move into the mainstream of corporate financial management and its
importance analyzed within the context of “whole” firm. Finally the stock market perspective of CRE,
which is the focus of this study, links CRE ownership to shareholders’ wealth maximization objective.3
There is considerable empirical evidence in the literature regarding the testing and application of
the APT in the stock and REIT markets. As far as this research is concerned, several empirical studies have
employed system estimation techniques to estimate simultaneously stock returns’ sensitivities and risk
premia in various situations. Sweeney and Warga (1986) analyze the pricing of interest risk in US utility
stocks using a two-factor APT model that includes a market and interest rate factors. McElroy and
Burmeister (1988) use macroeconomic variables as observable factors and employ iterated non-linear
seemingly unrelated regression (ITNSUR) to estimate the parameters of the APT multivariate time series
model. In another study, Burmeister and McElroy (1988) include in their APT multivariate model with
3
Other key CRE studies include: Zeckhauser and Silverman (1983), Rodriguez and Sirmans (1996), Deng
and Gyourko (1999), Liow (1999), Laposa and Charlton (2001), Seiler et al (2001), Liow (2004), Brounen
and Eicholtz (2005) and Brounen et al. (2005).
3
cross-equation restrictions observed and unobserved (latent) macroeconomic factors. They use iterated nonlinear three-stage least square (ITN3LS), iterated non-linear weighted least square (ITNWLS) and ITNSUR
to estimate respective risk premia for their linear factor model. In a different context, Jorion (1991)
addresses whether currency exposure commands a premium in the US stock market using two-factor and
multi-factor arbitrage pricing models. Finally, Antonios et al. (1998) investigate the validity of the fivefactor APT in pricing UK stocks. The five macroeconomic factors included in their studies are: unexpected
0inflation, expected inflation, money supply, default risk, exchange rate and market portfolio.
In contrast, research studies extend the APT investigation across the real estate and stock markets
are relatively few. Liow (1997) and Liow (2001) examine the real estate sensitivities of real estate intensive
non-real estate UK and Singapore stocks and investigate whether this sensitivity is priced. In both studies, a
three-index (stock market, industry and real estate) APT model suggests that real estate is a factor priced in
the stock market value of these UK and Singapore business firms. Hsieh and Peterson (2000) examines
whether there is a real estate factor in common stock returns that is not completely captured by existing
asset pricing models. His model includes the three factors of Fama and French (1993) and a real estate
factor. Their results indicate that a significant 19 percent of the 53 US industry portfolios are systematically
related to the real estate factor. Moreover, the loading of the real estate factor in common stock returns is
related to the loading of the book-to market equity factor in the portfolio returns. The results of their
analysis indicate that portfolio managers should manage their exposure to real estate. In Chen, Hsieh and
Jordon (1997), an APT model for equity REIT returns is estimated using macroeconomic variables and
factor loading approaches. However, real estate was not a specific variable in their study. Finally, He
(2002) finds that the real estate factor plays an important role in explaining excess returns on the industrial
stocks in the USA, along with other five risk factors (the overall stock market, size, book-market equity
ratio, the term structure and default risk). His sub-period results indicate that the effects of the real estate
factor are quite stable and second only to the overall stock market factor
Sample and Data Characteristics
4
An international sample of 556 listed retail firms is derived from the Osiris database4 based on SIC
primary code classification 52 to 59 5 as of December 2006. The stocks of these firms must be continuously
traded over the study period from January 2001 through December 2006. These firms are distributed across
three regions (Asia, Europe and North America) and are based in Australia (14)6, Canada (12), China (27),
France (14), Germany (13), Hong Kong (22), Japan (137), Korea (8), Malaysia (15), Singapore (15),
Sweden (6), Switzerland (6), Thailand (8), the UK (43) and the USA (216). Our sampling procedure has
survivorship bias as well as liquidity restrictions; but has the advantage of maintaining the identity of the
firms throughout the period.
Following literature, we derive a corporate real estate ratio (CRER) to measure the trend in
relative CRE ownership over a period of six years from 2001-2006. This CRER divides Osiris’s net
property, plant and equipment (NPPE)7 by the book value of a firm’s total tangible assets (TA); i.e. CRER
= NPPE / TA. We conjecture that the book value of PPE to proxy for the value of real estate assets owned
by the firm. The CRER ratio will enable a comparison of relative CRE ownership (i.e. real estate intensity)
between the eight retail segments, years and also countries in the sample. The estimated CRER is free of
estimate bias as both NPPE and TA are both based on book values. Ideally, the percentage of real estate
ownership would be a better measure. However, similar to Ambrose (1990), Deng and Gyourko (1999),
Seiler et al. (2001), Brounen and Eichholtz (2005) and Brounen et al. (2005) which derived CRER from
Compustat, we have to use the NPPE variable which offers the best available proxy from Osiris for an
international comparison in the CRE ownership. One addition point to note is that although this measure of
real estate concentration, NPPE/TA, does not measure the share of real estate in the firm’s physical capital,
4
Osiris is a comprehensive database of listed companies, banks and insurance companies around the world.
In addition to the income statement, balance sheet, cash flow statement and ratios it contains a wide range
of complementary information such as news, ownerships, subsidiaries, M & A activities and ratings. Osiris
contains information on 38,000 companies from over 130 countries including 30,000 listed companies and
8,000 unlisted or de-listed companies.
.
5
The eight primary SIC retail segments are: SIC52 (Building materials. Hardware, garden supply and
mobile home dealers), SIC53 (General merchandise stores), SIC54 (Food stores), SIC55 (Automotive
dealers and gasoline service stations), SIC56 (Apparel and accessory stores), SIC57 (Home furniture,
furnishings and equipment stores), SIC58 (Eating and drinking places) and SIC59 (Miscellaneous retail)
6
Numbers in bracket indicate the respective countries’ firm sample sizes.
7
NPPE is included in tangible fixed assets of the firm, having deducted from the historical cost and
r e v a lu a tion o f p rop e r ties , th e a c cumu la te d d ep r e c ia tio n, a mo r tiz a tio n a nd d ep le tio n .
5
but rather the “tangibility” of firm, it is quite unlikely that a larger part of the high CRER ratio for retailers
can be attributed to plant and equipment, as most retailers have little need to own significant plant and
equipment. If the retail firms own more land and buildings, this should be reflected in higher levels of
NPPE. Our goal here is to document trends in CRE ownership for retails companies at an international
level.
Exhibit 1 reports the sample distribution across countries and retail segments by number of firms,
years and average CRER. It is for illustrative purpose since the sample is not evenly distributed across
countries and retail segments. Consequently, the number of firms in some countries and retail segments
within a country are small compared to those of the USA and Japan.8 From Exhibit 1, one main observation
is that international differences are displayed among some countries’ CRER levels due probably to regional
and country differences in the CRE ownership. Moreover, CRE ownership appears to decrease over time
for 9 out of 15 countries suggesting possibly a trend towards leasing. Exhibit 2 reports an overall slightly
decreasing trend in CRE ownership from the CRER levels of around 37.10 percent in 2001 to
approximately 34.35 percent in 2006. However, absolute dollar value of real (estate) holdings reports an
average increase of about US$48.5 million per annum over the same period. Exhibit 2 derives another two
property related indicators; i.e. property as a percentage of shareholders’ equity (CRE/book value) and
property as a percentage of market capitalization (CRE/MV). As the numbers indicate, the six-year
averages show that aggregate real estate holdings (represented by NPPE) worth approximately USD 715.22
million and real estate comprises about 35.7% of a retail firm’s total tangible assets. Approximately 104%
of shareholders’ funds are in the form of real estate assets (CRE/BV) and property further represents about
95.4% of a retail firm’s market capitalization (CRE/MV). These three statistics together provide some
indications as to the importance of real estate in corporate position of the retail firms. Consequently, the
risk-return profile of the firms’ stocks could probably be (heavily) dependent on their real estate holdings.
Another important observation from Exhibit 2 is that the regional and segment dimensions of real estate
related characteristics are different, as revealed by the respective significant ANOVA F-statistics and nonparametric chi-square values. An overall conclusion is that within the retail industry, there exists significant
8
Numbers of firms classified by the three regions are: Asia (246), Europe (82) and North America (228).
Numbers of firms classified by SIC eight retail codes are: SIC52 (13), SIC53 (99), SIC54 (49), SIC55 (44),
SIC56 (82), SIC57 (49), SIC58 (102) and SIC59 (118).
6
segment and, to some extent, regional effect on the firms’ investment in CRE and that the importance of
real estate to retail firms could vary across the eight retail segments and three regions. However, these
findings must be viewed with some caution with two caveats: (a) the absolute and relative levels did not
only include property, but also plant and equipment, and (d) differences in the national accounting
standards, especially with regard to the treatment of “leases” and “depreciation” are likely to contribute to
the ways that the NPPE value is reported in corporate balance sheets in different national context..
(Exhibits 1 and 2 here)
Three-index APT Model and Empirical Procedures
In this study, the theoretical framework for investigating the role of retail real estate in corporate
valuation of .retail firms is the APT of Ross (1976). Based on the law of one price, the APT asserts that the
expected return of assets is linearly dependent on their sensitivities to a set of common factors or indices
and the resulting portfolio should command similar pricing (Roll and Ross, 1980).
The basic formulation of APT in this study is a three-index model:
R jt   0   jm Rmt   j  rt Rt  rt   j re Rt  re   jt ………………………….(1)
The rationale behind this approach is that there is more than one source of co-variation among
stock returns (King, 1966; Roll and Ross, 1980). Our research hypothesis is that the common stock returngenerating process of the retail firms has at least three risk factors; i.e. “stock market condition (R m)”,
“retail market condition (R rt)” and “real estate market condition (R re).9
Our empirical procedures have two parts. First, we focus on the real estate sensitivity or exposure
(  j re ) of the sample retail stocks and, further, to investigate whether this real estate beta, after controlling
for movements in the broader stock market and retail market that affect the return on retail stocks, is priced
in the “APT” spirit of the retail firms being paid an ex-ante premium for bearing risk in CRE ownership.
Following Burmeister and McElroy (1988) and Liow (2001), our empirical APT model is specified as:
R jt  R f t   j m ( Rmt  R ft )   j  rt ( Rt  rt   rt )   j  re ( Rt  re   re )   jt -------(2)
The main feature of equation (2) is that the APT formulation is recast as a multivariate non-linear
9
R rt (retail market condition) and R re (real estate market condition) can be regarded as two “industry”
factors. King (1966) found evidence on the influence of industry factors on stock returns. He also detected
strong interdependence of industry factors
7
regression model by constraining the estimates to have common market risk premia for the three factors.10
The resulting coefficients provide estimates of the ex ante risk premia (  rt and
 re ) for
the two “non-
market” factors and the coefficients (  m ,  rt ,  re ) on the three indexes. Factor one (R m) is specified to be
the “general market” factor that attracts an ex ante premium in general. Factor two (R rt) is specified to be
the “retail market” factor which is also expected to command an ex ante premium. Factor three (R re) is
hypothesized to be the “real estate market” factor. To the extent that the model is able to estimate
significant a risk premium, as measured by  re , then the real estate market factor represents a systematic
risk and plays an important role in explaining excess returns on retail stocks.
The major challenge for us is to find an appropriate national real estate market index to proxy for
CRE performance. Since this is an international study, all 15 national real estate market indices are
published information and should be consistent in their index construction to facilitate international
comparison. As a proxy for real estate returns and for practical reason, we consider public real estate index
returns based on prior evidence that the performance of public real estate should reflect the performance of
the underlying real estate market (Lizieri and Satchell, 1997). Of the published public real estate indices
(GPR, FTSE/EPRA, Dow Jones, Datastream and S&P/City Group), the only comprehensive source is the
Dow Jones (DJ) listed real estate indices that are available for all 15 countries except China. For
consistency purpose the DJ real estate indices, DJ retail market indices and DJ stock market indices are
used as the three factor proxies. Two types of data treatment are necessary before implementing Equation
(2). First, for each country, we perform orthogonalization to remove the impact of stock market index from
the retail sector index as well as the influence of the general and retail markets from the real estate market.
Second, for index returns, we extract the shocks to form time series on unexpected change in the index
returns using Kalman Filter technique in state space formulation11.
10
McElroy and Burmeister (1988) point out that the word “factor” in the sense of the APT, is built on the
assumption that returns are generated by a linear factor model. Alternatively, the “factors” may be
interpreted in the linear factor model as unanticipated changes in economic variables that generate
unexpected movement in asset prices. From a statistical viewpoint, the measured macroeconomic “factors”
are realizations of random variables and the statistical results are conditioned on these sample realizations.
11
This data treatment is necessary because only the innovations or unexpected changes in the factor returns
are of interest in APT. Mathematically, the unexpected returns or innovations are defined as the difference
between the actual return in period t, and the expected return of the same period with expectation formed at
the end of time t-1. The Kalman Filter is a recursive algorithm for sequentially updating the state vector
8
Instead of using the usual two-stage pass-through estimation method of Fama and MacBeth (1973)
to estimate factor risk premia, we appeal to the maximum likelihood methodology of Gibbons (1982)
which estimates factor sensitivity coefficients and factor risk premia simultaneously using an iterative
seemingly unrelated regression (ITSUR) technique.12 In addition, we use the Generalized Method of
Moments (GMM) procedure as an alternative estimation method. Whilst the ITSUR technique does not
require the specification of instrument and accounts for heteroskedasticity and contemporaneous correlation
in the errors across equations, the GMM method performs instrumental system estimation and seeks to
minimize the correlations between the exogenous variables and error term.
Empirically, the APT estimation is performed on an overall, three-regional, seven-segment and
five-CRER systems. The three factors are the market-capitalization weighted average of the individual
index returns. This system estimation approach on various samples hopes to ensure that the model is robust
across different samples of retail stocks. All retail firms in each system are then grouped into several
portfolios according to their average beta values over the relevant study periods. Wherever possible, each
portfolio will have an equal number of firms.
(a)
An overall APT system: This full-period APT system (2001-2006) includes 13-country portfolios,
4 Japanese portfolios and 7 USA portfolios. In addition, two identical systems are tested for 20012003 and 2004-2006.
(b)
Three regional APT systems: i.e., Asia (16 portfolios), Europe (5 portfolios) and North America
(15 portfolios).
(c)
Seven segment APT systems: i.e.; SIC 53 (9 portfolios), 54 (4 portfolios), 55 (4 portfolios), 56 (7
portfolios), 57 (4 portfolios), 58 (9 portfolios) and 59 (10 portfolios).13
given past information. Interested readers are encouraged to refer to Harvey (1993) for the mathematical
details. All the shocked series are statistically insignificant different from zero. They do not display any
significant autocorrelation up to the 20th lag. In addition, the Pearson correlations between any two shocked
series are very low and significant. For each country, the final series are the “unexpected stock market
factor”, “unexpected retail market factor” and “unexpected real estate market factor”. The results are not
reported in order to conserve space.
12
The use of the ITSUR technique has three advantages according to McElroy and Burmeister (1988).
First, the technique does not require the choice of instruments. Second, if errors are assumed normal,
iteration on the contemporaneous matrix yields maximum likelihood estimates. Third, even if the normality
assumption is not satisfied, the ITSUR technique possesses the statistical properties of least square
estimators which are consistent and asymptotically normal.
9
(d)
Five randomized SIZE-CRER systems: the 556 firms are first ranked in descending order of their
average market capitalization and grouped into five SIZE portfolios. Within each SIZE group, five
portfolios of approximate equal number are formed in descending order of CRER values. Then,
the same “CRER” portfolios in the five SIZE groups (i.e. CRER1 in SIZE1, CRER1 in SIZE2,
CRER1 in SIZE3, CRER1 in SIZE4 and CRER1 in SIZE5………) are combined to form five
randomized SIZE-CRER systems. Finally, firms in each system are grouped into 7 portfolios
based on their average beta values.
In our second part, we investigate whether the individual retail firms’ real estate betas estimated
from equation (1) are affected by the firms’ CRER levels and other financial characteristics. Bearing in
mind that financial variables are related in ways that makes it difficult, if not impossible, to determine
causality, and that they are often simultaneously determined by each other, we appeal to instrumental
variables estimation technique, using three-stage least squares (3SLS), to first derive the predicted CRER
values from a regression model using sales turnover, firm leverage, the set of two regional dummy
variables and the set of seven segment dummy variables as instrumental variables. The natural logarithm of
sales turnover variable is included as proxy for firms’ operating size, and is expected to influence positively
the CRER level. Debt /market value (a proxy for leverage) is included to see if there is a positive leverage
influence on the CRER level. The expectation is that higher leverage can buy more real estate particularly
if money can be borrowed at lower rate. The regional and segment variables allow for a determination of
possible differences in the CRE ownership by region and retail segments. The first-stage regression model
looks as follow:
2
7
r 1
s 1
CRER j  e0  e1 LnSales j  e 2 ( D / MV ) j   f i DREG r   f s DSEG s   j
Then a second-stage regression equation is estimated based on the following model:
2
7
r 1
s 1
 j  re  a0  a1 CRER j  a 2 LnMV j  a3 ( D / MV ) j  a 4 Ln( B / M ) j   ci DREGr   c s DSEGs   j
where
13
 j re is the average estimate of real estate beta of each firm for the full period, CRER is
SIC52 was excluded from the system estimation because it has only 13 firms to just form one portfolio.
10
the predicted value of real estate intensity for individual firms from the first stage regression model. The
expectation is that real estate intensive retail firms would display greater sensitivity to a real estate risk
factor. As such, we conjecture that the link between the CRER level (as a proxy for real estate intensity)
and the real estate risk sensitivity should be significantly positive. The Fama and French (1992)’s three
factors are size, leverage and growth opportunities. LnMV represents the natural logarithm of market
capitalization (proxy for firm size). Debt /market value (proxy for leverage) serves to capture the effect of
leverage on real estate market betas. LnB/M (the natural logarithm of book-to-market value ratio) is
designed to capture the extent to which the market perceives the growth opportunities of individual firms.
We will expect higher real estate betas for high B/M retail firms, DREG r (r = 1,2) are (0,1) dummy
variables representing Asia and Europe (relative to North America), DSEG
s
(s=1,2,3,4,5,6,7) are (0,1)
dummy variables representing SIC codes 52, 53, 54, 55, 56, 57 and 58 (relative to SIC code 59) and
 j is
the regression error term. Two identical models are tested for 2001-2003 and 2004-2006.
The simultaneous equation approach thus allows for the possibility that real estate beta and CRER
are two key endogenous variables. Before estimation, we have manually checked the financial data for
outliers. They include zero values for variables such as market value and sales turnover, negative values for
book-to-market value ratio and leverage ratios, and extraordinarily large observations for any of the
variables (defined as more than three standard deviations away from the mean). Consequently, a number of
firms for which these observations occurred have been removed from the samples, so the remaining
samples consists of 480 firms (86.3%) each for the entire study period (2001-2006) and two 3-year subperiods (2001-2003 and 2004-2006).
Empirical Results
Exhibit 3 provides the country averages of return, risk and coefficient of variation for the sample
retail firms’ weekly total return data. The sample period runs from January 2001 through December 2006
and contains 312 weeks of return data.
(Exhibit 3 here)
Are retail stocks real estate sensitive?
11
We estimate the beta coefficients in Equation (1) for the 556 retail stocks over 2001-2006, 20012003 and 2004-2006. The first observation is that the estimated beta coefficients on the stock market
returns are high significant, with the percentages of significant
 m (at least at the 10 percent level) range
between 84.7% and 91.6% of the retail stocks. Second, between 49.8 and 62 percent of the retail stocks
have a significant retail betas (  rt ) each across the three estimation periods, with the magnitude of the
estimated retail betas range between 0.4289 and 0.4901. Focusing on the estimated real estate coefficients
(  re ) as well as the number of significant
 re stocks for the sample firms, the percentages
of positive and
significant real estate betas range between 23.2% and 37% across the 15 markets. Only less than four
percent of the estimated real estate betas are negative and significant at the 10% level. One other
observation is that many retail firms were less exposed to real estate market risk in the 2004-2006 subperiod with an average real estate beta value of only 0.0262; compared to a high of 0.1740 during the 20012003 sub-period. Hence, a real estate risk factor has probably been much weaker in influencing the
variability of retail stock returns in recent years.
Overall, our preliminary results indicate that the number of firm level real estate betas that are
positive and significant at the 10% level is substantially less than the corresponding stock and retail market
betas implying that for the majority of retailers, their corporate values are largely tied to the performance of
the retail sales market and not the real estate market. Nevertheless, about one-third of the retail firms are
sensitive to a real estate factor after controlling for sensitivity to general and retail market risks, indicating
that stock market investors are able to understand and value the real estate position of these firms in
addition to their primary business performance.
Are real estate risk premia priced?
The ITSUR and GMM estimates are first computed for the three overall APT systems (2001-2006,
2001-2003 and 2004-2006). The three explanatory factors that measure the general market return, retail
market return and real estate market return proved important in explaining the returns of individual
portfolios within the system. Exhibit 4 reports the results of the estimated sensitivities and risk premia
associated with the real estate market factor for the respective sample periods, with both ITSUR and GMM
techniques display positive risk premia for all six estimates (i.e. 3 ITSUR and 3 GMM estimates) that are at
12
least significant at the 10 percent level. These results indicate that real estate market return has a positive
relationship with retail portfolio returns; an increase in real estate market return will increase expected
retail returns. In other words, investors can expect higher stock returns when the real estate market is
performing well.
Included in Exhibit 4 are also the real estate sensitivity estimates of the portfolios. For the full
period, both ITSUR and GMM estimates have found 15 portfolios (62.5%) that were “real estate sensitive”.
Similarly, the numbers (percentages) of retail portfolios that have significant real estate betas (p<=0.10) are
15 (62.5%-ITSUR) and 16 (66.7% - GMM) for the first sub-period, with only 3 (12.5%-ITSUR) and 2
(8.3%-GMM) significant real estate betas derived for the 2nd sub-period.
The implied annual risk estate risk premia for all portfolios are computed by multiplying the
respective portfolio sensitivity coefficient times the weekly risk premia coefficient over a 52-week period.
The average implied annual real estate risk premia are between 5.54% and 5.59% for the full period, with
higher values (between 6.70% and 6.78%) happened over 2001-2003, and decreased to between 0.20% and
0.30% in the later 2004-2006 period in line with a weaker real estate market condition in many countries
during this period
(Exhibit 4 here)
For the three regional APT systems, the numbers in Exhibit 5 indicate, all estimated real estate
risk premia are positive and statistically significant at the one percent level. These weekly estimates
(between 1.39% and 1.44%) are the highest for Asian retail firms, followed by North America firms
(between 1.10% and 1.37%) and finally by European firms (between 0.67% and 0.86%). Compared to their
European and North American counterparts, many Asian real estate markets during this six-year period
were probably characterized by higher risk-high return market profile. This real estate market systematic
risk is further “priced” (recognized) at equilibrium in the stock market, in that investors generally expect an
excess return from property in the region of between 3.31% and 3.65%.
(Exhibit 5 here)
Exhibit 6 reports the estimates for seven retail segments. The results are less conclusive with the
estimated average real estate risk premia and property sensitivities being different for retail firms in
different SIC retail segments. The numbers indicate five of the seven segments derived positive risk premia
13
that are at least statistically significant at the 10 percent level. The implied annual real estate risk premia are
between 0.44% and 6.54% implying that investors value differently the real estate position of the firms in
different retail segments. With the absolute highest real estate holdings (in dollar term) and a 43.7% CRER
level, food stores appear to be highly sensitive to real estate market movements and their exposures are
“priced” by investors with relatively high annual risk premia of approximately between 6.42% and 6.54%.
Compared to other segments, these estimates indicate that food stores have probably been fairly
compensated for taking additional risk in CRE ownership.
(Exhibit 6 here)
Finally, Exhibit 7 reports the estimates classified by five CRER groups randomized by market
capitalization (a popular proxy for size). The risk premia estimates are positive and at least statistically
significant at the 10 percent level for three of the five CRER portfolios. It is observed that the third CRER
portfolio, with an average intensity of about 32.14%, derived annual risk premia of between 6.1% and 7.0%
which are the highest of the five CRE groups. This is followed by the 4th CRER group firms, which with an
average real estate intensity of about 22.12%, derived an average annual real estate risk premium of up to
5.7%. On the contrary, the highest CRER group firm (with real estate intensity of 67.18%) was only
compensated for its real estate market risk with an excess return of up to 4.8%, suggesting that the riskreturn profile of CRE ownership has probably not been fully recognized by the investors. One wider
implication from this evidence is that the market valuation of a retail firm’s real estate becomes
unfavorable when its real estate intensity reaches a limit which is considered two large by the market. This
situation happens either because the stock market is of the opinion that the retail firm’s earnings growth is
not strong enough to sustain a larger real estate asset base or the market is doubtful that the firm is able to
manage its enlarged retail portfolio in its highest and best use. Of course, it is not within the scope of this
paper to speculate on this “CRE undervaluation” issue further.
(Exhibit 7 here)
Are real estate sensitivities affected by real estate intensiveness?
The preceding analysis establishes that real estate exposure (i.e. sensitivity) is probably an
important factor in affecting retail stock returns and that real estate risk premia are priced. Accordingly,
real estate is an important factor in corporate valuation. This section seeks to investigate whether there is a
14
consistent and significant link between how sensitive retail stocks to the real estate risk factor and how real
estate intensive the firm is, measured by its CRER level. If so, then real estate intensity should probably be
a good proxy for real estate exposure.
Exhibit 8 first reports the estimates for the stage-one regression of the simultaneous system for
three sample periods. Both sales turnover and leverage are consistently and significantly positive in
predicting the CRER levels. The second part of Exhibit 8 reports the results from estimating our 2nd-stage
cross-sectional regressions using the estimated real estate betas from the three index model as the
dependent variable and the predicted value of a constant, lnMV, lnB/M and debt/MV, two regional
dummies and seven segment dummies as explanatory variables. As the numbers indicate, while we do
detect a positive relationship each between the CRER level and real estate beta in the full period and first
sub-period, the link is nevertheless statistically insignificant. On the contrary, the coefficient for the CRER
level is significantly negative (at the 10 percent level) in the 2nd sub-period.
The coefficients on firm size are significantly negative in the three samples, which is in line with
prior stock market literature. However, there is some weak evidence that low growth firms (with high
LnB/M ratios) are more exposed to the real estate market risk over the full period and 1st sub-period. This
coefficient becomes statistically negative in the latter years (2004-2006) which is in line with prior
expectation. Finally, higher debt-to-market value ratios (D/MV) are associated with lower real estate betas
in all three samples, which is again probably inconsistent with the standard relation between leverage and
risk
In addition, the coefficients on the two regional dummies and seven segment dummy variables
indicate that there are some significant variations in the real estate betas across the three regions as well as
across the eight retail segments. The adjusted R2s from the three cross-sectional real estate beta regressions
range between 0.084 and 0.136. The low adjusted R2s suggest that only a small portion of the variations in
the cross-sectional real estate betas is accounted for with the chosen set of explanatory variables.
(Exhibit 8 here)
Exhibit 9 contains the 3SLS summaries by three regions. Focusing on the CRER coefficients in
the 2nd stage regression where real estate betas is the dependent variable, the coefficients are insignificantly
positive and negative, respectively, in the sub-samples of 2001-2003 and 2004-2006 and insignificantly
15
positive for the full sample (2001-2006) of Asian retail firms. The results for European retail firms reveal
one insignificantly positive, one significantly positive and one significantly negative CRER coefficients are
detected, respectively, for 2001-2006, 2001-2003 and 2003-2006. Finally, all three CRER coefficients are
not significant for the North American real firms. Thus we uncover no consistent evidence that real estate
betas are positively related to real estate intensiveness for retail firms grouped by three regions. In contrast,
with minor exceptions, the coefficients for firm size, leverage (debt/MV) and growth opportunities (ln
B/M) are largely statistically negative in all samples, which are all consistent to prior expectation.
(Table 9 here)
The 3SLS summaries, by retail segments, are reported in Exhibit 10 (2001-2006), Exhibit 11
(2001-2003) and Exhibit 12 (2004-2006). The estimated CRER coefficients are significantly positive for
three SIC segments (52, 55 and 57) for the full period and 1st sub-period, indicating that for these segments,
higher CRER levels of retail firms will render their stock returns more sensitive to changes in real estate
conditions. In contrast, retail firms from SIC56 derive two significantly negative coefficients over the two
same sample periods. In other cases, we find no significant relationship between the real estate betas and
CRER levels. Thus our results imply that the impact of real estate concentrations on real estate betas among
firms from different retail segments could probably differ widely both in terms of the direction of the
relationship as well as their magnitudes. Finally, while the coefficients for firm size and leverage variables
are generally significantly negative, we find a significantly positive impact on retail firms’ real estate betas
by their CRER levels in several retail segments over the three sample periods.
(Exhibits 10 to 12 here)
In summary, the regression results are far from conclusive to suggest that real estate intensive
retail firms would be more sensitive to a real estate risk factor although there is some evidence of a
significantly positive link between the two in some retail segments after controlling for changes in firm
size, leverage and growth opportunities. There are two possible implications arising from these results. One
obvious one is that our proxy for real estate intensity (CRER), which includes other tangible fixed assets, is
not ideal to measure the relative CRE ownership of the retail firms; however this proxy has been the best
that was available. Another less obvious implication is, broadly in line with the results of Exhibit 7, that
excessive CRE ownership of retailers is probably not favorable to their stock market valuation as stock
16
market investors may be unwilling or/and unable to recognize the full risk premia of CRE ownership and
thereby result in possible market undervaluation of CRE. A wider implication is that even the importance
of retail CRE ownership has probably been recognized by stock market investors and consequently more
retailers might be motivated to invest more resources in real estate or probably slow down their existing
divesting activities, these firms still need to trade off their higher CRE investment against any likely
increase in the overall corporate risk profile due to the adverse effect of huge capital investment in real
estate on the firms’ liquidity and cash flows. The optimal proportion of CRE ownership is thus an
important strategic investment decision that these retail firms have to carefully commit (Liow and NappiChoulet, 2008). This is definitely a challenging area for future research
Conclusion
The present paper explores whether real estate is an important factor in corporate valuation.
Specifically, we investigate the extent to which investors recognize the importance of retail CRE as
reflected through their pricing in the stock market. Given the significant commercial real estate component
in some retailers’ corporate asset base, there is an a priori reason to argue that real estate can influence
significantly their corporate valuation. The main thrust of this paper is an empirical investigation of a threeindex model in the APT framework of international retail firms in the period 2001-2006. The model is
estimated with a stock market factor, retail market factor as well as a real estate market factor on the basis
of existing theoretical work.
It appears from our empirical results that the returns of property intensive retail firms are probably
sensitive to a real estate factor and that this real estate market risk factor is reflected in an ex ante premium
in the stock market. These international results are in agreement with prior evidence on specific countries.
The finding that real estate market risk is “priced” implies that such risk is systematic and is therefore not
diversifiable. Consequently, investors and portfolio managers should include retail real estate exposure as
one of the systematic risk factors when constructing a multiple-index model to predict retail stock returns.
Hence, there is probably a strong case for the inclusion of a premium for real estate market risk in the
discount rate for capital budgeting decisions and corporate valuation of retail firms. From the corporate
management viewpoint, those retail firms with a significant real estate portfolio should always consider the
17
“real estate exposure” factor in their overall corporate strategy. Their high real estate exposure renders
them vulnerable to shocks in the real estate market.
A further analysis reveals that relative CRE ownership of the retail firms, measured by their
CRER levels, is not consistently reflected in the real estate risk sensitivities of retail stock in a positive
manner. Although there is some weak evidence of a significantly positive link detected between the CRER
levels and real estate betas in some retail segments after controlling for changes in firm size, leverage and
growth opportunities, these insignificant results suggest that, on one hand, stock market investors have
probably not incorporated the full real estate ownership risk in corporate valuation; on the other hand, it
might be due to the inability of our CRER variable (which measures the “tangibility” of the retail firms) to
pick up a significantly positive relationship between the real estate concentrations and real estate betas of
the retail firms. Moreover, the real estate betas might not have been estimated efficiently using the threeindex model as well as the DJ real estate proxy is probably a good measure of some countries’ real estate
market condition.
18
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20
Exhibit 1 Country (no. of firms) AUS (14) 1 Corporate Real Estate Ratio (CRER) of Listed Retail Companies CRER SIC
No of Companies 52 53 54 55 56 57 59 1 2 2 4 2 2 1 Average CHI (27) 53 54 56 58 59 HK (22) 2001 2002 2003 2004 2005 2006 Average 22 1 1 2 1 Average 0.28 0.51 0.41 0.20 0.22 0.42 0.11 0.31 0.43 0.38 0.75 0.12 0.28 0.39 0.26 0.21 0.38 0.17 0.21 0.41 0.07 0.24 0.41 0.41 0.76 0.12 0.27 0.39 0.27 0.20 0.39 0.23 0.22 0.40 0.12 0.26 0.40 0.55 0.78 0.12 0.32 0.44 0.27 0.16 0.38 0.21 0.22 0.30 0.17 0.25 0.39 0.60 0.81 0.11 0.28 0.44 0.28 0.11 0.39 0.21 0.21 0.30 0.17 0.24 0.42 0.59 0.80 0.08 0.36 0.45 0.29 0.11 0.32 0.21 0.23 0.26 0.17 0.23 0.42 0.55 0.78 0.08 0.16 0.40 0.27 0.22 0.38 0.21 0.22 0.35 0.13 0.25 0.41 0.51 0.78 0.11 0.28 0.42 53 54 55 56 57 58 59 5 2 1 5 1 4 4 Average 0.22 0.31 0.04 0.18 0.29 0.51 0.17 0.25 0.23 0.30 0.01 0.16 0.29 0.49 0.17 0.24 0.11 0.28 0.01 0.14 0.22 0.32 0.14 0.17 0.16 0.27 0.00 0.11 0.21 0.11 0.14 0.14 0.16 0.24 0.13 0.10 0.20 0.05 0.12 0.14 0.20 0.33 0.03 0.11 0.01 0.05 0.09 0.12 0.18 0.29 0.04 0.14 0.20 0.25 0.14 0.18 JP (137) 52 53 54 55 56 57 58 59 1 26 16 12 13 15 26 28 Average 0.28 0.45 0.40 0.41 0.28 0.32 0.38 0.28 0.35 0.23 0.46 0.39 0.41 0.27 0.33 0.39 0.27 0.34 0.23 0.47 0.40 0.42 0.28 0.34 0.40 0.27 0.35 0.38 0.47 0.40 0.41 0.25 0.34 0.40 0.26 0.36 0.43 0.47 0.41 0.41 0.25 0.31 0.39 0.26 0.36 0.39 0.46 0.39 0.39 0.23 0.28 0.38 0.25 0.35 0.32 0.46 0.40 0.41 0.26 0.32 0.39 0.26 0.35 KOR (8) 53 55 57 6 1 1 Average 0.72 0.76 2.67E‐3 0.50 0.66 0.85 0.13 0.55 0.70 0.84 0.17 0.57 0.71 0.85 0.01 0.52 0.66 0.85 0.03 0.51 0.65 0.85 0.19 0.57 0.68 0.83 0.09 0.54 MAL (15) 52 53 54 55 56 58 59 1 2 1 5 1 2 3 Average 0.06 0.15 0.73 0.34 0.17 0.65 0.34 0.35 0.08 0.23 0.71 0.33 0.16 0.55 0.36 0.34 0.10 0.23 0.73 0.35 0.14 0.55 0.36 0.35 0.10 0.20 0.75 0.40 0.14 0.56 0.25 0.34 0.09 0.28 0.80 0.37 0.14 0.49 0.30 0.35 0.09 0.29 0.66 0.39 0.13 0.49 0.32 0.34 0.09 0.23 0.73 0.36 0.15 0.55 0.32 0.35 SG (15) 53 55 56 57 58 59 4 2 1 1 3 4 Average 0.39 0.13 0.50 0.22 0.25 0.24 0.29 0.40 0.11 0.50 0.23 0.22 0.19 0.28 0.37 0.09 0.51 0.19 0.27 0.12 0.26 0.39 0.08 0.50 0.19 0.21 0.11 0.25 0.38 0.17 0.45 0.15 0.20 0.08 0.24 0.35 0.13 0.36 0.13 0.18 0.08 0.21 0.38 0.12 0.47 0.19 0.22 0.14 0.25 TH (8) 53 55 56 57 1 2 1 1 0.23 0.40 0.26 0.18 0.25 0.38 0.26 0.16 0.26 0.30 0.27 0.12 0.24 0.29 0.28 0.09 0.26 0.29 0.28 0.07 0.23 0.31 0.29 0.10 0.25 0.33 0.27 0.12 21
58 59 1 2 Average 0.20 0.42 0.28 0.16 0.41 0.27 0.17 0.41 0.25 0.21 0.40 0.25 0.13 0.38 0.23 0.12 0.40 0.24 0.16 0.40 0.26 FR (14) 53 54 56 57 58 59 2 4 1 1 3 3 Average 0.16 0.35 0.14 0.22 0.37 0.25 0.25 0.16 0.33 0.15 0.15 0.36 0.26 0.23 0.17 0.35 0.16 0.20 0.31 0.23 0.24 0.17 0.34 0.14 0.13 0.31 0.22 0.22 0.18 0.36 0.16 0.15 0.31 0.21 0.23 0.16 0.35 0.16 0.12 0.32 0.19 0.22 0.16 0.34 0.15 0.16 0.33 0.23 0.23 GER (13) 52 53 54 56 57 59 1 2 1 1 2 6 Average 0.54 0.45 0.40 0.81 0.07 0.15 0.40 0.55 0.46 0.51 0.84 0.06 0.13 0.42 0.58 0.53 0.48 0.82 0.14 0.14 0.45 0.52 0.49 0.55 0.82 0.17 0.15 0.45 0.47 0.48 0.55 0.82 0.06 0.15 0.42 0.44 0.43 0.57 0.82 0.05 0.15 0.41 0.52 0.47 0.51 0.82 0.09 0.15 0.43 SWE (6) 54 56 57 1 4 1 Average 0.29 0.17 0.33 0.26 0.30 0.16 0.27 0.24 0.26 0.17 0.27 0.23 0.25 0.18 0.31 0.25 0.20 0.19 0.35 0.24 0.24 0.17 0.36 0.26 0.26 0.17 0.31 0.25 SWI (6) 53 54 56 59 2 1 1 2 0.49 0.28 0.36 0.20 0.45 0.27 0.37 0.27 0.44 0.30 0.39 0.24 0.42 0.24 0.44 0.23 0.42 0.24 0.44 0.17 0.42 0.24 0.48 0.15 0.44 0.26 0.41 0.21 UK (43) 52 53 54 55 56 57 58 59 Average 2 3 4 5 5 5 9 10 Average 0.33 0.61 0.48 0.67 0.29 0.36 0.26 0.76 0.23 0.46 0.34 0.59 0.49 0.62 0.27 0.32 0.29 0.70 0.25 0.44 0.34 0.58 0.45 0.63 0.25 0.30 0.27 0.70 0.23 0.43 0.33 0.58 0.47 0.63 0.24 0.30 0.28 0.69 0.21 0.42 0.32 0.57 0.56 0.58 0.23 0.29 0.29 0.68 0.19 0.42 0.32 0.55 0.56 0.58 0.22 0.31 0.30 0.68 0.19 0.42 0.33 0.58 0.50 0.62 0.25 0.31 0.28 0.70 0.22 0.43 CAN (12) 53 54 56 57 59 2 3 3 2 2 Average 0.22 0.35 0.37 0.41 0.31 0.33 0.18 0.38 0.38 0.38 0.26 0.32 0.17 0.40 0.36 0.40 0.34 0.33 0.17 0.41 0.37 0.43 0.35 0.34 0.20 0.39 0.32 0.42 0.28 0.32 0.26 0.37 0.31 0.41 0.27 0.33 0.20 0.38 0.35 0.41 0.30 0.33 USA (216) 52 53 54 55 56 57 58 59 7 20 13 12 43 16 52 53 Average 0.37 0.39 0.52 0.25 0.33 0.27 0.66 0.23 0.38 0.39 0.39 0.51 0.24 0.34 0.28 0.66 0.21 0.38 0.37 0.39 0.48 0.24 0.32 0.27 0.65 0.20 0.37 0.36 0.38 0.49 0.24 0.33 0.26 0.64 0.19 0.36 0.36 0.39 0.49 0.25 0.31 0.26 0.63 0.19 0.36 0.35 0.40 0.48 0.26 0.30 0.27 0.63 0.19 0.36 0.37 0.39 0.50 0.25 0.32 0.27 0.65 0.20 0.37 Notes 1
Primary SIC (Standard Industry Classification) code: 52 (building materials, hardware, garden supply and mobile home dealers), 53 (general merchandise stores), 54 (food stores), 55 (automotive dealers and gasoline service stations), 56 (apparel and accessory stores), 57 (home furniture, furnishings and equipment stores), 58 (eating and drinking places) and 59 (miscellaneous retail) Source: derived from Osiris 22
Exhibit 2 Average CRE characteristics by Year, Region and Retail Segments: 2001‐2006 CRE holdings(US$ million) ALL 715.22 2001 2002 2003 2004 2005 2006 F‐statistic a Chi‐square b 570.56 601.05 681.11 764.54 812.46 861.62 0.73 12.00** Asia Europe North Americas F‐statistic a Chi‐square b 282.67 1269.91 982.46 25.66*** 69.65*** 52 53 54 55 56 57 58 59 F‐statistic a Chi‐square b 2513.42 1541.54 1908.03 275.88 238.11 222.80 450.64 256.81 21.550*** 351.69*** CRER (%) Panel A: Overall 35.71 Panel B: By Year 37.10 36.54 36.07 35.41 34.81 34.35 1.34 7.36 Panel C: By Region 34.10 34.91 37.43 0.048 3.48 Panel D: By SIC 37.92 41.07 43.74 30.68 30.02 27.63 53.07 21.88 168.71*** 602.18*** CRE/Book value (%) CRE/ Market value (%) 104.12 95.37 114.43 104.16 104.21 103.35 98.96 99.68 0.70 2.58 102.49 129.08 117.04 91.45 73.28 59.18 7.81*** 47.35*** 103.63 102.90 105.08 11.45*** 129.33*** 115.76 88.21 76.22 10.78*** 16.46*** 95.00 139.84 127.00 105.73 63.50 72.22 145.90 70.24 24.25*** 368.28*** 73.74 141.34 113.52 104.94 55.11 92.05 118.16 58.67 10.13*** 844.08*** Notes: CRE (US$ million) is represented by the absolute dollar value of net property, plant and equipment (NPPE); CRER (%) is NPPE/total tangible assets; CRE/book value indicates the percentage the book value (shareholders’ equity) that is invested in real estate; CRE/market value indicates the percentage of firm’s market value of equity that is related to the real estate holding. a Obtained from SPSS ANOVA analysis (parametric). b obtained from SPSS Kruskal‐
Wallis test (non‐parametric). ***, ** ‐ denotes two tailed significance at the 1% and 5% levels respectively. Source: derived from Osiris and SPSS tests 23
Exhibit 3 Average weekly return, risk and coefficient of variation of retail firms: 2001‐2006 Region Asia Europe North America Australia China Hong Kong Japan Korea Malaysia Singapore Thailand France Germany Sweden Switzerland United Kingdom Canada 14 27 22 137 8 15 15 8 Average 14 13 6 6 43 0.30% ‐0.15% 0.24% 0.16% 0.59% 0.08% 0.34% 0.32% 0.23% ‐0.04% 0.05% 0.35% 0.12% 0.29% 5.15% 5.51% 8.89% 5.52% 11.28% 6.84% 6.32% 5.82% 6.92% 5.94% 5.79% 5.56% 5.78% 5.56% |Coefficient of Variation| 17.2 37.0 36.4 34.8 19.1 88.2 18.8 18.2 33.74 143.6 124.5 16.1 46.5 19.1 Average 12 0.15% 0.56% 5.72% 6.37% 69.94 11.4 USA 216 Average 0.28% 0.42% 7.45% 6.91% 26.9 19.13 Country No Average Return Average Risk Source: derived from Datastream 24
Exhibit 4 Estimates of average real estate sensitivity (  re ) and risk premium (  re ) in the three‐index model 1 R jt  R f t   j m ( Rmt  R ft )   j  rt ( Rt  rt   rt )   j  re ( Rt  re   re )   jt Iterated non‐linear seemingly related regression (ITNLSUR) No (%) of Implied annual re  re average risk premia 2 significant  Period re re
2001‐2006 (full) 2001‐2003 2004‐2006 0.0064*** 0.0052*** 0.0041** 15 (62.5%) 15 (62.5%) 3 (12.5%) 0.1679 0.2509 0.0139 5.588% 6.784% 0.296% Generalized Method of Moment (GMM) No (%) of Implied annual  re average risk premia 2 significant  re
0.0070*** 0.0046*** 0.0036* 15 (62.5%) 16 (66.7%) 2 (8.3%) 0.1522 0.2782 0.0106 5.540% 6.700% 0.198% Notes: 1 This APT system includes 13‐country portfolios, 4 Japanese portfolios (sorted according to average stock beta value) and 7 USA portfolios (sorted according to average stock beta value). 2 Implied annual risk premium =  re x  re average x 52. ****, **, * ‐ indicates two‐tailed significance at the 1%, 5% AND 10% levels respectively Exhibit 5 Estimates of average real estate sensitivity (  re ) and risk premium (  re ) in the three‐index model – regional APT system 1 R jt  R f t   j m ( Rmt  R ft )   j  rt ( Rt  rt   rt )   j  re ( Rt  re   re )   jt APT system Iterated non‐linear seemingly related regression (ITNLSUR) No (%) of Implied annual re  re average risk premia 2 significant  re re
Asia 0.0139*** 4 (25%) 0.0459 3.318% Europe 0.0086*** 3 (60%) 0.0408 1.400% North‐America 0.0137*** 6 (100%) 0.0289 2.059% Notes 1
No of portfolios in the APT regional systems: Asia (16), Europe (5) and North America (6) 2
Generalized Method of Moment (GMM) No (%) of Implied annual  re average risk premia 2 significant  re
0.0144** 0.0067*** 0.0110*** 2 (12.5%) 3 (60%) 6 (100%) 0.0487 0.0420 0.0296 3.647% 1.463% 1.6931 Implied annual risk premium =  re x  re average x 52. ****, ** ‐ indicates two‐tailed significance at the 1% and 5% levels respectively 25
Exhibit 6 Estimates of average real estate sensitivity (  re ) and risk premium (  re ) in the three‐index model – segment APT system 1 R jt  R f t   j m ( Rmt  R ft )   j  rt ( Rt  rt   rt )   j  re ( Rt  re   re )   jt
APT system (SIC retail segment) Iterated non‐linear seemingly related regression (ITNLSUR) No (%) of Implied annual re  re average risk premia 2 significant  re re
53 54 55 56 57 58 59 0.0032 0.0087* 0.0224 0.0092** 0.0090* 0.0079*** 0.0036*** 5 (55.6%) 3 (75%) 0 5 (71.4%) 3 (75%) 5 (55.6%) 7 (70%) 0.1325 0.1446 0.0594 0.0115 0.0733 0.1527 0.1887 2.205% 6.542% 6.920% 0.550% 3.430% 6.273% 3.533% Generalized Method of Moment (GMM) No (%) of Implied annual  re average risk premia 2 significant  re
0.0040* 0.0086* 0.0221 0.0122*** 0.0094* 0.0081*** 0.0052*** 5 (55.6%) 3 (75%) 1 (25%) 5 (71.4%) 3 (75%) 6 (66.7%) 7 (70%) 0.1514 0.1436 O.0480 0.0070 0.0555 0.1584 0.1432 3.149% 6.422% 5.526% 0.444% 2.713% 6.672% 3.872% Notes 1
No of portfolios in the APT retail segment systems: SIC 52 (1) (excluded from the estimation), SIC 53 (9), SIC 54 (4), SIC 55 (4), SIC 56 (7), SIC 57 (4), SIC 58 (9); SIC 59 (10) 2
Implied annual risk premium =  re x  re average x 52 ****, **, * ‐ indicates two‐tailed significance at the 1%, 5% and 10% levels respectively 26
Exhibit 7 Estimates of average real estate sensitivity (  re ) and risk premium (  re ) in the three‐index model – APT randomized SIZE‐CRER system 1 R jt  R f t   j m ( Rmt  R ft )   j  rt ( Rt  rt   rt )   j  re ( Rt  re   re )   jt
APT system (randomized SIZE‐CRER system) 1 2 3 4 5 Iterated non‐linear seemingly related regression (ITNLSUR) No (%) of Implied annual  re average risk premia 2 significant  re re re
0.0052* 0.0048 0.0085** 0.0054** 0.0001 3 (42.9%) 6 (85.7%) 4 (57.1%) 5 (71.4%) 1 (14.3%) 0.1644 0.0804 0.1594 0.1966 0.0234 4.445% 2.007% 7.045% 5.521% 0.012% 0.0056* 0.0049 0.0064** 0.0054** 0.0024 Generalized Method of Moment (GMM) No (%) of  average significant  re 3 (42.9%) 5 (71.4%) 4 (57.1%) 5 (71.4%) 2 (28.6%) re
0.1640 0.0678 0.1819 0.2034 0.0256 Implied annual risk premia 2 4.776% 1.728% 6.054% 5.711% 0.320% Notes 1
Five randomized SIZE‐CRER systems: the 556 firms were first ranked in descending order of their average market capitalization and grouped into five SIZE portfolios. Within each SIZE group, five portfolios of approximate equal number are formed in descending order of CRER. Then, the same “CRER” portfolios in the five SIZE groups (i.e. CRER1 in SIZE1, CRER1 in SIZE2, CRER1 in SIZE3, CRER1 in SIZE4 and CRER1 in SIZE5………) are combined to form five randomized SIZE‐CRER systems. Finally, firms in each system are grouped into 7 portfolios based on their average beta values. 2
Implied annual risk premium =  re x  re average x 52 **, * ‐ indicates two‐tailed significance at the 5% and 10% levels respectively 27
Exhibit 8 Results of Simultaneous Equation Estimation – Full retail sample 2001‐2006 (N=480) Explanatory variables / Adjusted R2 2001‐2003 (N=480) 2
7
r 1
s 1
2004‐2006 (N=480) CRER j  e0  e1 LnSales j  e 2 ( D / MV ) j   f i DREG r   f s DSEG s   j
LnSales Leverage (debt / MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC52 DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 Adjusted R2 0.0101*** 0.0115*** ‐0.0499*** ‐0.0157*** 0.1666*** 0.1827*** 0.2118*** 0.0715*** 0.0792*** 0.0548*** 0.3242*** 32.0% (stage 1)
0.0103** 0.0084* ‐0.0568*** ‐0.0152 0.1465*** 0.1779*** 0.2055*** 0.0742** 0.0816** 0.0548* 0.3263*** 30.7% 0.0119*** 0.0180*** ‐0.0461*** ‐0.0201*** 0.1550*** 0.1911*** 0.2163*** 0.0760*** 0.0849*** 0.0538*** 0.3306*** 31.7% 2
7
r 1
s 1
 j  re  a 0  a1 CRER j  a 2 LnMV j  a3 ( D / MV ) j  a 4 Ln( B / M ) j   ci DREGr   c s DSEG s   j
CRER 0.0205 0.0298 (stage 2)
‐0.0398* LnMV ‐0.0337*** ‐0.0424*** ‐0.0267*** Leverage (debt /MV) ‐0.0132*** ‐0.0201** ‐0.0041 Ln (B/M) 0.0026 0.0142 ‐0.0347*** DREG (dummy) – Asia ‐0.0651*** ‐0.2237*** 0.0035 DREG (dummy) –Europe 0.0729*** ‐0.0504 0.1265*** DSGE (dummy) – SIC52 0.1551*** 0.0981 0.1829*** DSGE (dummy) – SIC53 0.0681*** 0.0777* 0.0972*** DSGE (dummy) – SIC54 ‐0.0142 ‐0.0370 ‐0.0446*** DSGE (dummy) – SIC55 0.0195** 0.0765 ‐0.0296* DSGE (dummy) – SIC56 0.0114 0.0505 ‐0.0176 DSGE (dummy) – SIC57 ‐0.0196** ‐0.0422 0.0256* DSGE (dummy) – SIC58 0.0168* 0.0669 ‐0.0258** Adjusted R2 11.5% 13.6% 8.4% Notes: Real estate beta (  re ) and CRER are the endogenous variables. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation. The natural log of sales (lnSales) is used as a proxy for operating size. In the second equation, firm size is represented by the natural log of market capitalization (lnMV) of each firm. Leverage is represented as the percentage of book value of debt to market value of equity (D/MV). The natural log of book value to market value (ln B/M) captures the perceived growth opportunities of the firm. The region dummies (DREG) and segment dummies (DSEG) control for regional and cross‐industrial retail variations respectively (North Americas and SIC59 are the respectively references)..The two equations are estimated via a simultaneous equation framework using 3SLS technique (based on robust covariance formula) available from Time Series Modeling (TSP) version 4.26. ***, **, * ‐ denotes two‐tailed significance at the 1%, 5% and 10% levels respectively. 28
Exhibit 9 Simultaneous Equation Estimation: Regional results Explanatory variables / Adj R2 2001‐2006 Asia Europe N. America (N=206) (N=70) (N=204) Asia (N=207) 2001‐2003 Europe N. America (N=71) (N=202) 2
7
r 1
s 1
CRER j  e0  e1 LnSales j  e 2 ( D / MV ) j   f i DREG r   f s DSEG s   j
LnSales Leverage (debt / MV) DSGE (dummy) – SIC52 DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 Adjusted R2 0.0219*** 0.0064*** 0.0353*** 0.1537*** 0.1302*** 0.0630*** 0.0180*** 0.0198*** 0.1177*** 14.2% 0.0093* 0.0208&*** 0.3348*** 0.2186*** 0.2452*** 0.0777*** 0.0956*** 0.0253*** 0.3962*** 30.6% 0.0191*** 0.0056*** 0.1502*** 0.1497*** 0.2844*** 0.0292*** 0.1275*** 0.1093*** 0.4659*** 58.9% 0.0231*** 0.0027*** ‐0.0438*** 0.1418*** 0.1114*** 0.0691*** 0.0166*** 0.0329*** 0.1073*** 13.2% 0.0147** 0.0026*** 0.3671*** 0.2531*** 0.2967*** 0.1369*** 0.1172*** 0.0318*** 0.4203*** 32.2% 0.0208*** 0.0086*** 0.1512*** 0.1392*** 0.2776*** 0.0174*** 0.1325** 0.0993*** 0.4766*** 57.5% 2
7
r 1
s 1
Asia (N=201) 2004‐2006 Europe N. America (N=71) (N=208) (stage 1)
0.0272*** 0.0118*** 0.0234*** 0.1501*** 0.1253*** 0.0621*** 0.0262*** ‐0.0056*** 0.1109*** 13.9% 0.0038* 0.0492*** 0.3126*** 0.1931*** 0.2248*** 0.0312*** 0.0882*** 0.0415*** 0.3847*** 31.6%  j  re  a 0  a1 CRER j  a 2 LnMV j  a3 ( D / MV ) j  a 4 Ln( B / M ) j   ci DREGr   c s DSEG s   j
0.0156*** 0.0003 0.1550*** 0.1693*** 0.3075*** 0.0291*** 0.1245*** 0.1142*** 0.4701*** 59.4% (stage 2)
CRER ‐0.0180 0.0288 ‐0.0458 0.0187 0.1804*** ‐0.1026 ‐0.0445 ‐0.2753*** 0.0005 LnMV Leverage (debt /MV) Ln (B/M) DSGE (dummy) – SIC52 DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 Adjusted R2 ‐0.0369*** ‐0.0044* ‐0.0116* 0.0522*** 0.1350*** ‐0.0121 0.0190 ‐0.0124 ‐0.0692*** ‐0.0027 10.5% ‐0.0486*** ‐0.0445*** 0.0037 0.0330* 0.0827*** 0.0188 0.2246*** ‐0.0075 0.0355* 0.1048*** 30.6% ‐0.0162*** ‐0.0073* 0.0441*** 0.2452*** ‐0.0505*** 0.0408* ‐0.0311** 0.0521*** 0.0199 0.0713*** 8.4% ‐0.0196*** ‐0.0025 ‐0.0128* 0.1481*** 0.1014*** ‐0.0259 0.0352* ‐0.0008 ‐0.0827*** 0.0124 6.9% ‐0.0363*** ‐0.0254*** ‐0.0424*** ‐0.0657** 0.1249*** ‐0.1182*** 0.3721*** 0.0029 ‐0.0278 0.1042*** 25.4% ‐0.0480*** ‐0.0269*** 0.0584** 0.1842** ‐0.0380 ‐0.0217 0.0632 0.1097** ‐0.0016 0.1594** 10.1% ‐0.0525*** ‐0.0162*** ‐0.0468*** 0.2205*** 0.1658*** ‐0.0481** 0.0401* ‐0.0150 0.0262 ‐0.0271 11.0% ‐0.0392*** ‐0.0215*** 0.0362*** 0.1172*** 0.0977*** 0.0684*** ‐0.0228 ‐0.0504*** 0.0573** 0.0452* 14.7% ‐0.0066* ‐0.0276*** ‐0.0485*** 0.1806*** ‐0.0006 ‐0.0557** ‐0.1438*** ‐0.0064 0.0292 ‐0.0310 5.6% Notes: For the three sample periods, three regional models each are estimated. Real estate beta (  re ) and CRER are the endogenous variables. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation. The natural log of sales (lnSales) is used as a proxy for operating size. In the second equation, firm size is represented by the natural log of market capitalization (lnMV) of each firm. Leverage is represented as the percentage of book value of debt to market value of equity (D/MV). The natural log of book value to market value (ln B/M) captures the perceived growth opportunities of the firm. The segment dummies (DSEG) controls for cross‐industrial retail variations within each region. The two equations are estimated via a simultaneous equation framework using 3SLS technique (based on robust covariance formula) available from Time Series Modeling (TSP) version 4.26. ***, **, * ‐ denotes two‐tailed significance at the 1%, 5% and 10% levels respectively. 29
Exhibit 10 Results of Simultaneous Equation Estimation – By retail segments: 2001‐2006 (full period) SIC52 SIC53 SIC54 SIC55 SIC56 SIC57 Explanatory variables /Adj R2 (N=13) (N=80) (N=43) (N=37) (N=75) (N=41) 2
7
r 1
s 1
CRER j  e0  e1 LnSales j  e 2 ( D / MV ) j   f i DREG r   f s DSEG s   j
LnSales Leverage (debt / MV) DREG (dummy) – Asia DREG (dummy) –Europe Adjusted R2 0.0546*** 0.0833*** 0.0375*** 0.2898*** 14.4% 0.0063*** 0.0239*** 0.0464*** 0.0345*** 6.5% 0.0120*** 0.0127*** ‐0.0865*** ‐0.0846*** 9.8% 0.0172*** 0.0106** 0.1156*** 0.0833*** 6.8% 0.0148*** 0.0262*** ‐0.0642*** ‐0.0265*** 6.9% 7
r 1
s 1
SIC59 (N=100) 0.0231*** 0.0170*** ‐0.2950*** ‐0.0272*** 20.7% 0.0220*** 0.0155*** 0.0663*** 0.0318*** 4.3% (stage 1)
0.0098*** 0.0082** ‐0.0190*** ‐0.0924*** 10.6% 2
SIC58 (N=91)  j  re  a 0  a1 CRER j  a 2 LnMV j  a3 ( D / MV ) j  a 4 Ln( B / M ) j   ci DREGr   c s DSEGs   j
(stage 2)
CRER 0.2463*** ‐0.0109 0.0102 0.0663** ‐0.1816** 0.4245*** ‐0.0263 0.0629 LnMV Leverage (debt /MV) Ln (B/M) DREG (dummy) – Asia DREG (dummy) –Europe Adjusted R2 ‐0.0092 0.0940*** 0.1710*** ‐0.1302** ‐0.2692 35.6% ‐0.0393*** ‐0.0501*** 0.0028 0.1063*** 0.1602*** 28.3% ‐0.0248*** ‐0.0233*** ‐0.0382** ‐0.1185*** ‐0.0393** 8.48% ‐0.0322*** ‐0.0189** 0.0130* ‐0.0262 0.2826*** 24.6% ‐0.0296*** ‐0.0079 0.0633*** ‐0.1898*** ‐0.0151 13.7% ‐0.0495*** ‐0.0243*** 0.0338** ‐0.1651*** 0.1015*** 29.8% ‐0.0006 0.0279*** 0.0087 ‐0.0889*** 0.1838*** 9.8% ‐0.0308*** ‐0.0248*** 0.0365*** ‐0.0793*** 0.0668*** 8.8% Notes: This table reports the estimation results for eight SIC retail segments for the full period. Real estate beta (  re ) and CRER are the endogenous variables. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation. The natural log of sales (lnSales) is used as a proxy for operating size. In the second equation, firm size is represented by the natural log of market capitalization (lnMV) of each firm. Leverage is represented as the percentage of book value of debt to market value of equity (D/MV). The natural log of book value to market value (ln B/M) captures the perceived growth opportunities of the firm. The regional dummies (DREG) controls for regional variations within each retail segments. The two equations are estimated via a simultaneous equation framework using 3SLS technique (based on robust covariance formula) available from Time Series Modeling (TSP) version 4.26. ***, **, * ‐ denotes two‐tailed significance at the 1%, 5% and 10% levels respectively. 30
Exhibit 11 Results of Simultaneous Equation Estimation – By retail segments: 2001‐2003 (1st sample period) SIC52 SIC53 SIC54 SIC55 SIC56 SIC57 Explanatory variables /Adj R2 (N=.13) (N= 80) (N=43) (N=37) (N=75) (N= 41) 2
7
r 1
s 1
CRER j  e0  e1 LnSales j  e 2 ( D / MV ) j   f i DREG r   f s DSEG s   j
LnSales Leverage (debt / MV) DREG (dummy) – Asia DREG (dummy) –Europe Adjusted R2 0.0508*** 0.0701*** ‐0.0057 0.3132*** 27.9% 0.0056*** 0.0140*** 0.0433*** 0.0363*** 4.6% 0.0155*** 0.0061*** ‐0.1033*** ‐0.0872*** 11.9% 0.0215*** ‐0.0056* 0.1315*** 0.0993*** 7.69% 0.0106*** 0.0025*** ‐0.0577*** ‐0.0129*** 4.5% 7
r 1
s 1
SIC59 (N=101) 0.0246*** 0.0165*** ‐0.2973*** ‐0.0141*** 41.3% 0.0218*** 0.0085*** 0.0674*** 0.0256*** 8.4% (stage 1)
0.0165*** 0.0046*** ‐0.0135*** ‐0.0998*** 12.0% 2
SIC58 (N=90)  j  re  a 0  a1 CRER j  a 2 LnMV j  a3 ( D / MV ) j  a 4 Ln( B / M ) j   ci DREGr   c s DSEGs   j
(stage 2)
CRER 0.5028** 0.0335 0.0717 0.1025* ‐0.8236* 0.6805*** 0.0802 0.1282 LnMV Leverage (debt /MV) Ln (B/M) DREG (dummy) – Asia DREG (dummy) –Europe Adjusted R2 0.0216 0.0944** 0.3702*** ‐0.1182 ‐0.4718*** 36.1% ‐0.0436*** ‐0.0322*** 0.0124 ‐0.0332* 0.1282*** 8.2% 0.0278*** ‐0.0041 0.0362 ‐0.0970*** ‐0.1727*** 11.1% ‐0.0449*** 0.0240*** ‐0.0745*** ‐0.2321*** 0.2003*** 22.5% ‐0.0551*** ‐0.0210 0.0415 ‐0.4162*** ‐0.1915*** 18.6% ‐0.0578*** ‐0.0414*** 0.0915*** ‐0.3142*** ‐0.0156*** 29.2% ‐0.0263** ‐0.0167 0.0072 ‐0.2693*** 0.0202 11.9% ‐0.0469*** ‐0.0408*** 0.0076 ‐0.2272*** ‐0.0867*** 8.4% Notes: This table reports the estimation results for eight SIC retail segments for the period 2001‐2003. Real estate beta (  re ) and CRER are the endogenous variables. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation. The natural log of sales (lnSales) is used as a proxy for operating size. In the second equation, firm size is represented by the natural log of market capitalization (lnMV) of each firm. Leverage is represented as the percentage of book value of debt to market value of equity (D/MV). The natural log of book value to market value (ln B/M) captures the perceived growth opportunities of the firm. The regional dummies (DREG) controls for regional variations within each retail segments. The two equations are estimated via a simultaneous equation framework using 3SLS technique (based on robust covariance formula) available from Time Series Modeling (TSP) version 4.26. ***, **, * ‐ denotes two‐tailed significance at the 1%, 5% and 10% levels respectively. 31
Exhibit 12 Results of Simultaneous Equation Estimation – By retail segments: 2004‐2006 (2nd sample period) SIC52 SIC53 SIC54 SIC55 SIC56 SIC57 Explanatory variables /Adj R2 (N=13) (N=81) (N=43) (N=37) (N=74) (N=42) 2
7
r 1
s 1
CRER j  e0  e1 LnSales j  e 2 ( D / MV ) j   f i DREG r   f s DSEG s   j
LnSales 0.0566*** Leverage (debt / MV) 0.1036*** DREG (dummy) – Asia 0.0782*** 0.0073*** 0.0324*** 0.0386*** DREG (dummy) –Europe 0.2544*** 0.0240*** Adjusted R2 4.12% 5.7% 0.0152*** 0.0188*** 0.0204*** 0.0114*** ‐0.0176*** 0.1423*** 0.087*** 11.1% 0.0592*** ‐0.0836*** ‐0.0952*** 10.8% ‐0.0435*** ‐0.0415*** 6.7% 7
r 1
s 1
SIC59 (N=100) 0.0199*** 0.0079*** 0.0212*** ‐0.2890*** ‐0.0441*** 35.6% 0.0759*** (stage 1)
0.0045** ‐0.0154*** ‐0.0129*** ‐0.0776*** 8.2% 2
SIC58 (N=90)  j  re  a 0  a1 CRER j  a 2 LnMV j  a3 ( D / MV ) j  a 4 Ln( B / M ) j   ci DREGr   c s DSEGs   j
CRER 0.0032 0.0225 LnMV ‐0.0098 ‐0.0390*** Leverage (debt /MV) 0.0118 ‐0.0598*** Ln (B/M) 0.1036*** ‐0.0966*** DREG (dummy) – Asia ‐0.0923* 0.1201*** DREG (dummy) –Europe ‐0.0503 0.2541*** Adjusted R2 11.6% 12.5% Notes: ‐0.0796 ‐0.0131*** ‐0.0215*** ‐0.0232 ‐0.0940*** 0.1031*** 5.6% 0.0168*** 0.0239*** 5.4% (stage 2)
‐0.0398 ‐0.0141 0.0691 ‐0.0737 ‐0.0805 ‐0.0154* ‐0.0387*** ‐0.0564*** 0.0155 ‐0.0384*** ‐0.0150*** 0.0525 0.1087*** ‐0.0386*** 0.0073 ‐0.0571** 0.0674*** 7.6% ‐0.0225* ‐0.0615** ‐0.0394 0.1203*** 0.0713*** 0.0512*** ‐0.0191 0.1390*** 7.4% 0.0527*** 0.2378*** 1.4% 7.5% ‐0.0710*** ‐0.0484* 0.1257*** 12.7% This table reports the estimation results for eight SIC retail segments for the period 2004‐2006. Real estate beta (  re ) and CRER are the endogenous variables. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation. The natural log of sales (lnSales) is used as a proxy for operating size. In the second equation, firm size is represented by the natural log of market capitalization (lnMV) of each firm. Leverage is represented as the percentage of book value of debt to market value of equity (D/MV). The natural log of book value to market value (ln B/M) captures the perceived growth opportunities of the firm. The regional dummies (DREG) controls for regional variations within each retail segments. The two equations are estimated via a simultaneous equation framework using 3SLS technique (based on robust covariance formula) available from Time Series Modeling (TSP) version 4.26. ***, **, * ‐ denotes two‐tailed significance at the 1%, 5% and 10% levels respectively. 32