ISSN (Print) 0473-453X

Discussion Paper No. 1158

ISSN (Online) 2435-0982

CORPORATEREAL ESTATE HOLDINGAND STOCK RETURNS:
INTERNATIONAL EVIDENCE
FROM LISTED COMPANIESJoe Cho Yiu NgCharles Ka Yui Leung
Suikang Chen

January 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Corporate Real Estate Holding and Stock Returns: International
Evidence from Listed Companies
Joe Cho Yiu Ng, Charles Ka Yui Leung, Suikang Chen
City University of Hong Kong
This version: January 2022

Abstract
This study examines the relationship between corporate real estate (CRE) holdings and stock
returns before and after the Global Financial Crisis (GFC). We find that (1) the United States andthe United Kingdom show a negative relationship before the GFC and positive after the GFC. (2)Firms that pay positive tax or have positive R&D investments are not systematically different fromthe full sample. This finding cannot support the "scarce capital" theory or the tax incentive
explanation, but it is consistent with the “empire building” theory. After the GFC, financial
constraints tightened, and both CRE holding and stock returns dropped. (3) European (excluding
the United Kingdom) sample shows a positive relationship in the pre-crisis period. This finding iscompatible with the "illiquidity premium" theory. However, the association becomes inconclusive
in the post-crisis period. (3) The Japanese sample shows a negative association between CRE andstock returns in the pre-crisis period, like the United States and the United Kingdom. However, therelationship becomes statistically insignificant in the post-crisis period, consistent with the theoryof financial constraint tightening after the GFC.
JEL Classification: E44, G10, G30
Keywords: Global Financial Crisis, corporate real estate holding, collateral constraint, illiquidity
premium, panel regression



Acknowledgment: We thank K. W. Chau, Been-Lon Chen, Yuen Long Chow, Julan Du, Gangzhi Fang, Chinmoy
Ghosh, Yuichiro Kawaguchi, Fred Kwan, Shian-Yu Liao, Seow Eng Ong, Chihiro Shimizu, Isabel Yan, an
anonymous referee, seminar participants of APRER Symposium (Guangzhou), Taiwan Economic Association(Taipei) for helpful comments and the City University of Hong Kong for financial support. Part of the research is
conducted when Leung visits the Hoover Institution; Ng visits the Virginia Tech through a Junior Fulbright
Scholarship. The hospitality of these institutions is gratefully acknowledged. Leung's travel has been supported by
the Higher Education Sprout Project from the Ministry of Education (Grant No. 110L900201) and the Ministry of
Science and Technology (MOST 110-2634-F-002-045-) in Taiwan, and from ISER in Osaka University through
JSPS KAKENHI Grant Number JP 20H05631. The usual disclaimer applies.

Correspondence: Department of Economics and Finance, City University of Hong Kong, Kowloon Tong, Hong
Kong, cyngjoe@gmail.com , kycleung@cityu.edu.hk .

1. Introduction
This paper investigates the relationship between corporate real estate (CRE)holdings and stock returns. CRE refers to the real estate such as buildings and lands
owned or leased by firms not primarily engaged in real estate business (Dresdow andTryce 1988; Johnson and Keasler 1993). Many non-real estate firms around the world
hold a considerable amount of CRE. Table 1a shows that the percentage of CRE as ashare of total corporate assets ranges from 10% to more than 40%, depending on the
country and sampling period. 1 For such sizable CRE holding, a variety of explanations
have been proposed by different groups of economists. Thus, following the spirit ofEberly et al. (2012), we include both micro and macro-based explanations. 2 As Table
1b provides a summary, and the appendix provides a detailed literature review, we
briefly discuss these theories. Casual observation may suggest that firms hold CRE for
production needs. For instance, manufacturing firms tend to have more CRE thanservice firms. 3 Brounen and Eichholtz (2005) find that industrial differences rather
than regional differences drive the differences in CRE ownership. Since CRE is a valueenhancing tool, the share of CRE in the total corporate asset would be positively
correlated with the stock return.
[Table 1 about here]
The asset pricing literature suggests another reason for a positive nexus. For1

For instance, in the United States, Zeckhauser and Silverman (1983) report that at least 25% of the total assets ofcorporations in the U.S. were corporate properties in the 1980s. From 1984 to 2011, Zhao and Sing (2016) reportthat the average CRE controlled by listed firms in the U.S. was about 10% of the total assets. In Europe, a reportconducted by DTZ (2003) shows that the full value of the CRE in Germany, France, and the U.K. was 1 trillion, 0.7
trillion, and 0.71 trillion euros, respectively, in 2002. In Asia, Liow (1999) reports that over 1987-1996, CRE heldby a sample of Singapore non-real estate firms was about S$ 35.9 billion and comprised about 29% of the firms' total
tangible assets. Brounen and Eichholtz (2005) study an international sample of nine countries whose CRE as apercentage of total assets ranges from 17% in Germany to 41% in Canada in 2000.
2 Several papers compare competing theories on housing and commercial real estate, such as Kwan et al. (2015),
Leung et al. (2002), Leung and Feng (2005), among others.
3 For example, in 2000, the average CRE ratio for the sample countries was 0.13 for the business service industry
and 0.63 for mining companies.

2

instance, Tuzel (2010) proposes that firms with a relatively high real estate level areriskier due to the slow adjustment to adverse productivity shocks. Hence, they are
expected to have a higher return. Therefore, a "risk premium" or "illiquidity premium"could be associated with CRE, and there could be a positive relationship between the
CRE holding and the stock returns. Moreover, the macroeconomics literature proposes
an additional reason for a positive relationship between CRE holding and stock return.
Firms may hold CRE as collateral for loans (Bernanke and Gertler 1989, 1990; Kiyotaki
and Moore 1997; Chaney et al. 2012; Gan 2007a, b; Jin et al. 2012). Due to an aggregate
negative shock, the value of CRE suddenly drops, some firms may sell CRE to repay
the debts. Thus, firms decrease their CRE holdings, causing their productivity and even
investment drop, which bring them lower returns. Therefore, a positive nexus also exists
after a negative shock hits the firms.
However, if firms hold too much CRE or CRE outside their core business, thismay reduce their capital to support other investments, like R&D (Linneman 1998).
Many studies find that firms gain higher returns after more R&D expenses (Chan et al.
1990; Sundaram et al. 1996; Eberhart et al. 2004; Brown et al. 2009; Li 2011; Gu 2016).
Since capital for investment is limited ("scarce capital" argument), more capital
allocated to CRE means less for R&D. 4 Hence, a positive relationship between R&D
and returns would negatively affect CRE holdings and returns. The corporate finance
perspective provides an additional justification why a large amount of CRE holding
may not be return-enhancing (Sirmans 1999; Sing and Sirmans 2008; Coles et al. 2006;
Du et al. 2014). For instance, Du et al. (2014) show that less financially constrained,
weakly governed U.S. listed firms are more likely to over-expand (the so-called "empire
building" problem). Therefore, the "empire building" and "scarce capital" arguments

The appendix presents a simple model of corporate investment, where the trade-off between investing in CRE andR&D depends on the probability of success in R&D, which may vary across firms.

43

suggest a negative relationship between CRE holdings and returns.
To summarize, while some theories predict a positive relationship between the
CRE holding and stock returns, some conjectures predict a negative one. Hence,clarifying the correlation between the CRE holding and stock returns would help us
focus on the fact-consistent views and progress in economics (Cooley 1995; Friedman,
1953).
Here are our key contributions to the literature. First, most of the existing literature
focuses on U.S. firms. We study the U.S. sample, the European sample, and theJapanese sample. Since institutions and market conditions differ across countries,
comparing geographical subsamples would help us establish robust results. 5

Second,we use the Global Financial Crisis (GFC) as a natural experiment to test thesecompeting theories on the relationship between CRE holdings and stock returns.
This investigation is motivated by several considerations. As we explained earlier, themacro-based theory would suggest that the relationship between CRE holding and stock
returns be positive after a tremendous negative shock such as GFC, which is exogenous
to firms and brings a tightening of financial conditions. On the other hand, a positive
relationship between CRE holdings and stock returns can hold both before and after acrisis if the illiquid premium is the dominant reason for firms to own CRE. Thus, theGFC may shed light on the driving force of the CRE holdings. Moreover, recent
research suggests a "structural change" in the housing market after the GFC. 6
Therefore, it is interesting to see if a similar change occurs in the commercial real estate
sector.
More specifically, this study addresses the following questions: (1) Does CRE

Some authors also argue that the U.S. dollar has an "exorbitant privilege" (Eichengreen, 2011). Some investors
are willing to accept a lower return for U.S. assets (Forbes, 2010). Therefore, it is beneficial to verify whether the"stylized facts" in the U.S. market also hold in other markets.
6 Among others, see Chang and Leung (2021), Green (2021), Leung and Ng (2019), Ng (2021).
5

4

holdings affect stock returns? If so, how? (2) Did the GFC bring any changes to therelationship between CRE holdings and stock returns? If so, is the change in that
relationship consistent with the theories we discussed? (3) Is the relationship between
CRE holdings and stock returns in the U.S. also observed in other major stock markets?
To address these questions, we employ panel regressions with the system GMM
estimator to study the relationship between CRE holdings and stock returns after
controlling for firm characteristics that may also affect stock returns. Relative to theearlier literature, this paper examines whether the GFC affects the relationship between
CRE holdings and stock returns. Therefore, we divide our sample into pre-crisis andpost-crisis. We then compare whether there is a change in the nexus. In addition to the
U.S., we study samples of European economies and Japan. 7
The remainder of this paper is organized as follows. Section 2 describes the data,and section 3 presents the results for the U.S. sample. Data and results for the European
and the Japanese sample are shown in section 4. The last section concludes.

2. Data for the U.S. sample
Following the standard practice, we employ annual data from all listed nonfinancial and non-real estate firms (excluding firms with four-digit SIC codes between
6000 and 6999) from 2001 to 2015 for the U.S. sample. 8 All the accounting variables
are collected from the Compustat. In our study, CRE is measured by the ratio of net
property, placement, and equipment (PPE) and a firm's total assets in each fiscal year. 9

We also conduct the same analysis for an Asia pacific sample (excluding Japan). Unfortunately, the results do notpass the specification tests (the Arellano-Bond and the Hansen test).
8 Notice that some financial firms can take deposits or premiums from customers, and hence their cost of capitalwill be very different from non-financial firms. Some financial firms are also subject to various regulations than nonfinancial firms. In addition, real estate firms may need real estate as input, and thus, their motives for CRE holding
may differ from non-real estate firms.
We compare the pre-crisis and post-crisis periods, and we employ data starting from 2001 to balance the pre-crisis(2001-2006) and post-crisis (2010-2015) samples.
9 Different measures of CRE employed in the previous literature are provided in the appendix.
7

5

CRE𝑖𝑖,𝑡𝑡 =𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡
𝐹𝐹𝐹𝐹𝐹𝐹𝐵𝐵𝑖𝑖,𝑡𝑡 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖,𝑡𝑡 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖,𝑡𝑡 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖,𝑡𝑡=
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖,𝑡𝑡
𝐴𝐴𝐴𝐴𝑖𝑖,𝑡𝑡

where 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹, 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹, 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 and 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 stands for buildings (cost), construction inprogress (cost), land and improvements (cost) and leases (cost), respectively.

The Compustat Industry Annual provides a breakdown of PPE into buildings,
capitalized leases, machinery and equipment, natural resources, land and improvements,
and construction in progress, both in gross and net value for each fiscal year-end.
Following Tuzel (2010), machinery, equipment, and natural resources are excluded
from net PPE as these items do not satisfy the definition of corporate real estate.
Following the corporate finance and real estate finance literature, our dataset includes
other accounting variables. Table 2 defines each variable. To make firms of a different
size comparable, we use the R&D ratio (R&D expenses / total sales) rather than the
R&D expenses. These accounting variables will be used as control variables in the panel
regression analysis, except for "Taxrate," which is used for dividing a sub-sample withfirms who pay positive tax on average. We will discuss this in the next section.
[Table 2 about here]
We conduct the usual "winsorizing," which eliminates firm-year observations forwhich no CRE holding is reported and those with financial variables in the top andbottom 1% percentiles. After this data screening process, firms in the agriculture (SIC
= 0) and public administration (SIC = 9) industry are all excluded from our samples. As
a result, our sample has more than 18,000 firm-year observations. To control for theindustry effect and to construct a measure that is comparable across different industries,
we employ the RCRE (or relative CRE) ratio, which is defined as
𝑁𝑁𝑗𝑗,𝑡𝑡

1
RCRE𝑖𝑖,𝑗𝑗,𝑡𝑡 = CRE𝑖𝑖,𝑗𝑗,𝑡𝑡 −� CRE𝑖𝑖,𝑗𝑗,𝑡𝑡 ,𝑁𝑁𝑗𝑗,𝑡𝑡𝑖𝑖=1

where 𝑁𝑁𝑗𝑗,𝑡𝑡 is the number of firms in industry j in fiscal year t. Thus, the RCRE of a6

firm i in industry j in fiscal year t is the difference between the CRE ratio of that firm
and the industry equal-weighted average.
The stock return data in monthly frequency are obtained from the CRSP. We
eliminate firms with less than 36 months of consecutive returns. Following Fama andFrench (1992, 1993) and Tuzel (2010), we match the annual accounting information in
the fiscal year ending in year t-1 with the stock return data from July of year t to Juneof year t+1, allowing for a minimum of a six-month gap.
To calculate the “excess return” (Alpha), we employ firm-to-industry-excess return(FIER) rather than the conventional firm-level excess return (FLER). While FLER only
compares the stock performance over the risk-free rate to the market return, FIER
compares the firm excess return relative to its corresponding industry. This distinction
may be potentially valuable. For example, due to the difference in production mode,
some industries have higher CRE holding than others. Since CRE holding could affectthe potential risk, some industries may offer higher returns than others. Thus, it may be
instructive to use FIER, considering the possible differences in risk and return acrosssectors. The monthly FLER for each firm 𝑖𝑖 would be the return over the month 𝑚𝑚over the risk-free monthly rate of return:

𝑅𝑅𝑖𝑖,𝑚𝑚 = 𝑟𝑟𝑖𝑖,𝑚𝑚 − 𝑟𝑟𝑟𝑟𝑚𝑚

Then we can compute the value-weighted average return of the industry over the same
period for each industry j:
𝑅𝑅𝑗𝑗,𝑚𝑚 = �𝑛𝑛

𝑤𝑤𝑖𝑖 𝑅𝑅𝑖𝑖𝑖𝑖

𝑖𝑖=1

𝑖𝑖∈𝑗𝑗

, 𝑤𝑤𝑖𝑖 =𝑀𝑀𝑀𝑀𝑖𝑖,𝑚𝑚∑𝑛𝑛1 𝑀𝑀𝑀𝑀𝑖𝑖,𝑚𝑚

Once we have the industry weighted-average return, we can compute the firm-toindustry-excess return (FIER), which is simply:
𝑅𝑅𝑅𝑅𝑖𝑖,𝑚𝑚 = 𝑅𝑅𝑖𝑖,𝑚𝑚 − 𝑅𝑅𝑗𝑗,𝑚𝑚 .

Then, we adopt the Fama-French three factors and the momentum factor
7introduced by Carhart (1997) to calculate Alpha. All these series come from Kenneth
R. French’s Data Library. Alpha is extracted from the standard four-factor model:
𝑟𝑟𝑖𝑖,𝑚𝑚,𝑡𝑡 = 𝛼𝛼𝑖𝑖,𝑡𝑡 + 𝛽𝛽1 𝑖𝑖,𝑡𝑡 𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚,𝑡𝑡 + 𝛽𝛽2 𝑖𝑖,𝑡𝑡 𝑆𝑆𝑆𝑆𝑆𝑆𝑚𝑚,𝑡𝑡 + 𝛽𝛽3 𝑖𝑖,𝑡𝑡 𝐻𝐻𝐻𝐻𝐻𝐻𝑚𝑚,𝑡𝑡 + 𝛽𝛽4 𝑖𝑖,𝑡𝑡 𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑚𝑚,𝑡𝑡

where 𝑟𝑟𝑖𝑖,𝑚𝑚,𝑡𝑡 represents the FIER of firm i at month m over the period t. 10

3. Result for the U.S. sample
3.1 Panel regression with system GMM estimator
This section employs the panel regression model to study the relationship between
alpha and CRE holdings. We control for firm characteristics and unobservable factors.
We include individual firm-fixed effects to control for unobservable variations acrossfirms. We also have time-fixed effects for unobservable variations across different
periods. Our simple regression model takes the following form:
𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑎𝑎𝑖𝑖,𝑡𝑡 = 𝜃𝜃0 + 𝜃𝜃1 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1 +𝜃𝜃2 𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1 + 𝜃𝜃3 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖,𝑡𝑡−1 + 𝜃𝜃4 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖,𝑡𝑡−1+ 𝜃𝜃5 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡−1 + 𝜃𝜃6 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖,𝑡𝑡−1 + 𝜃𝜃7 𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡−1 + 𝛾𝛾𝑖𝑖 + 𝛿𝛿𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡

𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑎𝑎𝑖𝑖,𝑡𝑡 is the annual alpha of firm i. 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 is the RCRE ratio described in theprevious section. Control variables include 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙, 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙, 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶, 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 and 𝑇𝑇𝑇𝑇.
Their definitions are presented at Table 2. 𝛾𝛾𝑖𝑖 and 𝛿𝛿𝑡𝑡 account for the individual andtime-fixed effects, respectively.

Our regression model offers protection against bias arising from reverse causality
by employing lagged regressors. However, the strict exogeneity assumption might still
be violated since the fixed effect model is used. For example, Under the within-groups
transformation, the unbiased estimates require E�𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1 − 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,−1 , 𝜀𝜀𝑖𝑖,𝑡𝑡 − 𝜀𝜀𝑖𝑖 � =Note that period t covers the 12-month from July in year t to June in year t+1. The Fama-French three factors arecalculated at a monthly frequency. MKT represents the market excess return, SMB represents the return of theportfolio that is long in small firms and short in big firms, and HML stands for the return of the portfolio that is longin high B/M firms and short in low B/M firms. Finally, Carhart (1997) momentum factor (MOM) is constructed at amonthly frequency. It captures the return of the trading strategy that is long in short-term winners and short in shortterm losers. 𝛼𝛼𝑖𝑖,𝑡𝑡 absorbs all the abnormal returns that are not captured by the four factors.

108

0 where 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,−1 is the average of 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡 over the periods 0,...,T-1 and 𝜀𝜀𝑖𝑖 is theaverage of 𝜀𝜀𝑖𝑖,𝑡𝑡 . However, it still violates the strict exogeneity assumption since
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,−1 and 𝜀𝜀𝑖𝑖 contain 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 and 𝜀𝜀 from every period. Therefore, we employ thesystem GMM estimator (Arellano and Bover 1995; Blundell and Bond 1998). The
system GMM estimator augments the difference GMM by assuming that the firstdifferences of instruments are uncorrelated with the fixed effects. It simultaneously
estimates a differenced equation and a level equation, where lagged variables in levels
instrument the differenced equation, lagged differences instrument levels. It is a generalestimator designed for situations with independent variables that are not strictly
exogenous; they correlate with past and possibly current error realizations (Roodman,2009b). 11
We employ a two-step system GMM estimator and Windmeijer's (2005) finitesample adjustment to correct the downward bias in the computed standard errors in twostep results. We also employ the “forward orthogonal deviations” transformation(Arellano and Bover, 1995). To avoid over-fitting the endogenous variables, we
collapse the instruments and use lag 2 to 4 for instruments. We report the p-values of
the Arellano-Bond test for AR(2) and the Hansen test for each regression. An AR(1)process is expected in first differences, because 𝜀𝜀𝑖𝑖,𝑡𝑡 − 𝜀𝜀𝑖𝑖,𝑡𝑡−1 should correlate with

𝜀𝜀𝑖𝑖,𝑡𝑡−1 − 𝜀𝜀𝑖𝑖,𝑡𝑡−2 since both share the 𝜀𝜀𝑖𝑖,𝑡𝑡−1 term. But the absence of an AR(2) process
in the first differences should not be rejected. The null hypothesis of the Hansen test isthat the instruments as a group are exogenous. Since omitting important explanatory
variables could make the error term correlated with the instruments, the Hensen test canalso be viewed as a test of structural specification (Roodman, 2009a). Failing to reject
Furthermore, the endogeneity problem caused by selection bias is a common concern (e.g., see Dang et al. (2015)
and the reference therein). In the current context, the entry and exit of firms could potentially create a selection bias
(Guo and Leung, 2021; Hopenhayn, 1992; Jovanovic, 1982). Fortunately, through analyzing the dynamic panel data
models with sample selection, Al-Sadoon et al. (2019) recently found that the inconsistency of the System GMM
estimator is tiny and hardly induces bias in the estimator, even and especially in small samples.

11

9

the null implies there is no specification problem.
To study the impact of the GFC, we divide the sample into pre-crisis and postcrisis sub-samples and compare the relationship of CRE holding and stock return ineach sub-sample. In addition, we study sub-samples of firms with positive R&D
expenses and positive tax payments. These subsample analyses are motivated by thetheories we discussed earlier. If R&D matters for firms’ return, then we expect that theeffect of CRE holdings on returns will be different in the sub-samples of firms withpositive R&D expenses and the entire sample with all firms. The reason for studying
firms with actual tax payments is as follows. The current U.S. corporate tax code allows
for the loss-offset provision, which means that firms can write off operation losses
against both past and future profit and reduce their tax obligations (Kaymak and Schott2019). Therefore, firms may purchase an "excessive amount" of CRE to immediately
reduces the pretax profit, and hence the tax obligation, at the year of purchase. Also,
should there be a capital loss when the CRE is sold, the loss-offset provision would
allow the firms to pay lower taxes or no tax. Thus, those tax-paying firms are less likely
to be "overloaded" with CRE. Hence, the relationship between CRE holdings andreturns among firms might be "weaker'' than the whole sample.

[Table 3 about here]
Table 3 shows the panel regression results for the U.S. Sample. First, ArellanoBond tests for AR(2) are not rejected, meaning that the error term in levels is serially
uncorrelated. Also, the Hansen test of over-identification indicates that the instruments
as a group appear exogenous. Second, there is a negative relationship between the
RCRE ratio and the Alpha in the pre-crisis sample. The point estimate of the coefficienton RCRE among positive R&D firms seems to be more negative than that of the wholesample. It indicates that if the positive R&D firms allocate funds more to purchasing
10

CRE, their average returns will drop more than the counterpart of the entire sample.
However, the F test shows that the difference between the two coefficients is
insignificant. Thus, we do not have direct statistical evidence to support the "scarecapital" theory. Third, the F test also indicates that the positive tax firms' sample has no(statistical) difference compared to the whole sample, suggesting that CRE holding tax
incentives may not be substantial.
Forth, the negative relationship in the pre-crisis sample indicates that while the"empire building" theory may hold before the crisis, it is then challenged after the GFC,
as the relationship between RCRE and the stock return becomes positive. The F test
also confirms that the difference between pre-crisis and post-crisis samples issignificant. The finding is consistent with the macroeconomic theory, which proposesthat in the post-crisis period, with declining productivities, tightening financial
constraints force the firms to sell CRE, perhaps to repay the debts.
We also adopt a more direct approach to test the "empire building" theory byincluding firm-level corporate governance-related variables into the regression.
Unfortunately, corporate governance variables that are commonly agreed upon for all
countries are unavailable. Therefore, we restrict our attention to the U.S. sample. We
employ the firm-level corporate governance index constructed by Gompers et al. (2003).
This index is only available for a sub-sample of U.S. firms in 1990, 1993, 1995, 1998,
2000, 2002, 2004, and 2006. Thus, we are unable to compare the regressions before andafter the GFC. The results are shown in the appendix. The coefficient on RCRE is
insignificant in this sub-sample of U.S. firms even before adding the corporate
governance index. And the coefficient continues to be negligible after introducing thecorporate governance variable. A small and discontinuous sample could cause theestimation result, and hence it may be premature to reject the empire-building theory11

on this basis.12 We would instead conclude that we have not found any direct support
for that class of theory.

4. The European and Japanese sample
Thus far, we have focused on U.S. firms. How about the firms in other countries?
Economic intuitions suggest that explanations on the relationship between CRE
holdings and stock returns should also hold across countries. Also, GFC affects not only
U.S. firms but all firms globally. On the other hand, institutional factors might also
affect the CRE holdings. Hence, examining the relationship between CRE holdings andstock returns would ensure that the economic explanations provided in this paper indeed
hold in general. 13

4.1 Data
Therefore, we would repeat the analysis with our European and Japanese samples.
Based on Compustat, the European sample covers seven economies, in alphabet order,
Denmark, France, Germany, Italy, Netherlands, Russia, and the United Kingdom. We
employ the same econometric model and the same set of variables and "winsorizing"as the U.S. sample. The Fama-French three factors and the momentum factor are
obtained from Kenneth R. French's Data Library and Gregory et al. (2013).

Moreover, firm-level variables such as leverage may be influenced by corporate governance variables. For morediscussion, see Morellec et al. (2012) and the reference therein, among others,
13 We receive an additional suggestion during the GFC, capital flow to the USA for flight-to-liquidity (FTL) or
flight-to-safety (FTS) considerations. Hence, the results that hold in the U.S. do not necessarily hold internationally.
Considering the impact of international capital flows on CRE holding would be beyond the scope of this paper. The
literature on FTL and FTS is also abundant. See Baele et al. (2019), Beber et al. (2009), Longstaff (2004), and thereferences therein, among others.
1212

4.2 Panel regression results
Table 4a shows the results for the European sample. 14 The coefficients on RCRE
are negative but insignificant in both pre-crisis and post-crisis samples. The F test showsthat the pre- and the post-crisis difference is statistically insignificant. However, when
we conduct the leave-one-out-cross-validation as a robustness check (Table 4b), we findthat after dropping the United Kingdom, the coefficients on RCRE become positive andsignificant in the pre-crisis sample.
[Table 4 about here]
Therefore, we exclude the United Kingdom and re-run the panel regressions. Table
4c shows the results for Europe, excluding the U.K. sample. The relationship between
the RCRE ratio and the Alpha is positive in the pre-crisis period, consistent with the
production-based explanation and the "illiquidity premium" theory. However, therelationship becomes insignificant in the post-crisis period. One possibility is theilliquidity of CRE does not concern investors anymore. Alternatively, it might be thatthe illiquidity concern (which would drive the CRE-return correlation to positive) is
offset by other forces, such as the financial constraints (which would cause the CREreturn correlation to negative). We leave this to future research for further clarification.
Table 4d shows that, like the U.S. case, the RCRE ratio and the stock return
relationship in the United Kingdom is negative before the GFC and positive after. The
F test also confirms that the pre-crisis and post-crisis difference is significant. Again,
factors such as the "empire building" may be driving the relationship before the crisis.
In the post-crisis period, these factors are overwhelmed by tightening financial
constraints or CRE illiquidity, making the CRE-return relationship positive.
Table 4e displays the results for Japan. In the pre-crisis sample, similar to the U.S.
14 Although corporate tax policies vary among different economies, to be consistent, we compare the subsample oftax-paying firms with the entire sample in each region.

13

and the U.K. sample, the coefficients on RCRE are negative and significant. In the postcrisis period, the relationship between CRE holding and stock return is mainly
weakened and insignificant. The F test shows that the pre-crisis and post-crisis
difference is significant. The finding may also suggest that tighter financial constraint
matters after the financial crisis since it potentially turns the negative relationship into
a positive one or weaken the negative correlation. To facilitate a comparison of results,
Table 5 provides a summary.
[Table 5 about here]

5. Concluding Remarks
By definition, CRE holdings refer to real estate ownership by firms that do notprimarily engage in real estate business. Why would firms commit resources on thatwhen capital is scarce? Researchers from different backgrounds provide different
answers. Some authors argue that a relatively high level of CRE holdings reflects a
relatively low level of corporate governance. As a result, over-expansion, or the socalled "empire building" problem, is more likely to occur. Therefore, a higher level of
CRE holding will be associated with a lower level of stock returns. Some other authors
propose that firms with a relatively high CRE holding are riskier due to the illiquidity
and slow adjustment nature of CRE. Hence, such firms are expected to provide higher
returns to compensate for the risk. Besides, some authors consider that CRE serves as
collateral and enhances borrowing capacity. If the value of CRE suddenly drops due toa negative shock, financially constrained firms may face forfeiture of collateral, andsome of them may sell CRE to repay the debts. Since firms' returns are likely to belower in that scenario, a positive relationship between CRE holding and stock returns
has resulted.

14

This study has no ambition to settle this debate in one research paper. It merely
provides some robust stylized facts that hopefully inspire future theoretical modeling(Abad and Khalifa 2015; Cooley 1995). More specifically, it uses the Global Financial
Crisis (GFC) as a natural experiment to test these competing theories on the relationshipbetween CRE holdings and stock returns. We find that (1) the United States and theUnited Kingdom show a similar pattern on the relationship between CRE holding andstock return in both pre-crisis (negative correlation) and the post-crisis period (positive
correlation). This finding suggests the "empire building" theory might be valid before
the GFC. A tightening of financial constraints after the crisis dominates the relationshipbetween CRE holding and stock return. (2) We also compare the sample of all firms
with the sub-sample that pay positive tax or have positive R&D investment and find nosystematic difference. Hence, we cannot provide direct evidence to support the "scarce
capital" theory. (3) European, excluding the United Kingdom sample, shows a positive
relationship in the pre-crisis period. This finding suggests that the "illiquidity premium"argument holds before the crisis. However, the link between CRE holding and stock
return becomes negligible in the post-crisis period. (4) The Japanese sample shows anegative relationship in the pre-crisis period, similar to the United States and the United
Kingdom. However, the association is primarily weakened and becomes insignificant
in the post-crisis period. This finding may also suggest that tighter financial constraint
matters after the GFC.
Putting all these together, we conclude that tightening financial constraints after
the GFC matter for firms in the United States, the United Kingdom, and Japan. It turns
a negative relationship into a positive or insignificant one. The results of the European
sample (excluding the United Kingdom) are admittedly counter-intuitive. One
possibility is that after the GFC and the later EURO crisis in 2011, there was a wave ofgovernment interventions, including the Outright Monetary Transactions (OMT)
15

program conducted by the European Central Bank (ECB). Those interventions leadbanks to make “zombie loans” to firms that would otherwise declare bankruptcy(Acharya et al. 2019a, b; Andrews and Petroulakis 2019; McGowan et al. 2018;
Schmidt et al. 2020). 15 With the support of such loans, firms may not need to unload
their CRE. Hence, the CRE-stock return relationship may be changed artificially. We
leave it to future research for further explorations.
We believe that the critical question is whether the CRE holding boost or diminish
the firm value. For listed firms, stock returns are arguably a less controversial measure.
On the other hand, non-listed firms also have a substantial amount of commercial realestate. Thus, future research should also study how CRE holding would impact thosefirms.

15 There are different definitions of “zombie firms” used in the literature. However, a prevalent practice is toinclude firms which “were not able to cover their interest expenses out of their pretax earnings.” See Acharya et al.
(2019a, b), among others, for more details.

16

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21

Tables
Table 1a. CRE held by firms around the world reported in previous literature
RegionLiterature

North
America Zeckhauser and Silverman (1983)
and Australia
Roulac (2003)
Brounen and Eichholtz (2005)
Zhao and Tien (2016)
EuropeDTZ (2003)
Brounen and Eichholtz (2005)
Krumm and Linneman (2001)

Asia

CRE holdings
At least 25% of the total assets of corporations in the US are corporate properties
in the 1980s.
The inflation-adjusted book value of the corporate-owned real estate wasapproximately $8.6 trillion in 2000.
CRE as a percentage of total assets in 2000: 26% in the U.S; 35% in Australia.
The average CRE controlled by listed firms in the U.S. was about 10% of thetotal asset from 1984 to 2011.
The 2002 total value of the CRE in Germany, France, and the United Kingdom
was 1 trillion, 0.7 trillion, and 0.71 trillion euros, respectively.
CRE as a percentage of total assets in 2000: 18% in France; 17% in Germany;
22% in the Netherlands; 29% in the United Kingdom.
The book value of CRE held by Dutch corporations was approximately 0.22
trillion euros in 1995.

Liow (1999)

Over 1987-1996, CRE held by a sample of Singapore non-real estate firms was
about S$35.9 billion and comprises about 29% of the firms’ total tangible assets.

Brounen and Eichholtz (2005)

CRE as a percentage of total assets in 2000: 30% in Hong Kong; 31% in Japan.

22

Table 1b. Theoretical predictions on the relationship between CRE holdings and stock returns
Justification

ExamplesTheories suggest a positive relationship Production-based perspective: Technological Brounen and Eichholtz (2005); Gort et al.
between CRE holding and stock returns.
constraint(1999); Kan et al. (2004)
Asset Pricing perspective: Illiquidity Premium Tuzel (2010)
Macroeconomic
Constraint

perspective:

Theories suggest a negative relationship Basic trade-off: Scarcity of Capital
between CRE holding and stock returns.

Collateral Bernanke and Gertler (1989, 1990); Chaney etal. (2012); Chen and Leung (2008); Gan(2007a, b); Jin et al. (2012); Kiyotaki andMoore (1997)
Brown et al. (2009); Chan et al. (1990);
Eberhart et al. (2004); Gu (2016); Li (2011);
Sundaram et al. (1996)

Corporate Finance perspective: Weak Coles et al. (2006); Dong et al. (2012); Du etgovernance leads to “empire-building” or al. (2014); Sing and Sirmans (2008); Sirmans
over-expansion(1999)

23

Table 2. Definition of accounting variables
Variable

Definition

MeasurementAT
CAPX
Leverage
MV
RD
TaxrateTQ

Total asset
Capital expenditureLong term debt/Total asset
Market value
R&D expenses/ Total sales
Income tax rate
Tobin’s Q

Total asset (AT)Capital expenditure (CAPX)Long term debt (DLTT) / Total asset (AT)Common shares outstanding (CSHO) * Price close annual – fiscal (PRCC_F)
R&D expenses (XRD) / Total sales (SALE)
Income tax total (XTX) / Earnings before interest and tax (EBIT)
Market value (MV) / (Total asset (AT) – Common equity (CEQ) – Deferred tax (TXDB))

24

Table 3. Panel regressions: United States sample
Dependent variable: Alpha

RCREi,t−1

Controls
Year fixed effectArellano-Bond test for AR(2)Hansen test of overidentification
Observations
Number of instruments
F statistic: compared to the coefficient in column (1)F statistic: difference between pre- and post-crisis

(1)
(2)
(3)
All firms Positive RD Positive tax(4)
All firms

Pre-crisis sample: 2001-2006

Post-crisis sample: 2010-2015-0.129*(0.077)
Yes
Yes
0.326
0.432
10831
33

-0.153*(0.093)
Yes
Yes
0.631
0.361
7297
33
0.040
All firms: 6.67***(5)
Positive RD(6)
Positive tax-0.1210.222** 0.502**
0.252**(0.083)(0.112)(0.255)(0.123)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.683
0.583
0.331
0.601
0.362
0.774
0.765
0.521
8650
7437
4770
6100
33
33
33
33
0.005
1.01
0.033
Positive RD: 5.82**
Positive tax: 6.32**

Note: Robust standard errors are given in parentheses. *, ** and *** indicate 10%, 5% and 1% level of significance. P-values are reported for the Arellano-Bond and Hansen
test.

25

Table 4a. Panel regressions: European sample
Dependent variable: Alpha

RCREi,t−1

Controls
Year fixed effectArellano-Bond test for AR(2)Hansen test of overidentification
Observations
Number of instruments
F statistic: compared to the coefficient in column (1)F statistic: difference between pre- and post-crisis

(1)
(2)
(3)
All firms Positive RD Positive tax(4)
All firms

Pre-crisis sample: 2001-2006

Post-crisis sample: 2010-2015-0.098(1.418)
Yes
Yes
0.366
0.473
5500
33

(5)
Positive RD-1.433-0.102-0.338-0.162(3.105)(1.782)(0.449)(0.793)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.231
0.528
0.832
0.934
0.562
0.489
0.69
0.744
3148
4819
4549
2785
33
33
33
33
0.153
0
0.037
All firms: 0.026
Positive RD: 0.157
Positive tax: 0.023(6)
Positive tax-0.384(0.457)
Yes
Yes
0.562
0.703
3988
33
0.005

Note: Robust standard errors are given in parentheses. *, ** and *** indicate 10%, 5% and 1% level of significance. P-values are reported for the Arellano-Bond and Hansen
test.

26

Table 4b. Leave-one-out test: coefficient on RCREi,t−1, European sample
Excluding:Denmark
FranceGermany
Italy
Netherlands
RussiaUnited Kingdom

(1)
(2)
(3)
All firms Positive RD Positive tax(4)
All firms

Pre-crisis sample: 2001-2006

Post-crisis sample: 2010-2015

0.005(2.395)-1.005(2.866)-0.224(2.749)-0.054(2.430)-0.045(2.787)-0.470(2.169)0.950**(0.467)-0.356(0.503)0.451(0.686)0.075(0.701)-0.490(0.548)-0.324(0.536)0.021(0.549)0.098(0.473)-1.528(4.129)-2.456(4.806)-0.906(5.323)-1.437(3.778)-1.994(3.989)-2.359(4.136)1.447*(0.759)0.114(3.173)-1.10(4.246)-0.206(3.751)-0.227(3.353)-0.843(3.512)-0.846(2.879)1.390**(0.563)(5)
Positive RD-0.047(1.034)-0.429(1.435)0.442(1.452)-0.558(1.102)-0.233(1.044)-0.105(1.032)0.128(0.524)27(6)
Positive tax-0.412(0.645)-0.499(0.778)0.102(0.871)-0.475(0.707)-0.355(0.662)-0.107(.604)
0.190(0.558)

Table 4c. Panel regressions: European excluding United Kingdom sample
Dependent variable: Alpha

RCREi,t−1

Controls
Year fixed effectArellano-Bond test for AR(2)Hansen test of overidentification
Observations
Number of instruments
F statistic: compared to the coefficient in column (1)F statistic: difference between pre- and post-crisis

(1)
(2)
(3)
All firms Positive RD Positive tax(4)
All firms

Pre-crisis sample: 2001-2006

Post-crisis sample: 2010-2015

0.950**(0.467)
Yes
Yes
0.889
0.735
3138
33

0.098
(0.473)
Yes
Yes
0.599
0.756
3041
33

1.447*
1.390**(0.759)(0.563)
Yes
Yes
Yes
Yes
0.981
0.730
0.895
0.738
1789
2792
33
33
0.311
0.362
All firms: 1.64
Positive RD: 2.05

(5)
Positive RD

0.128(0.524)
Yes
Yes
0.776
0.913
1831
33
0.002
Positive tax: 2.29

(6)
Positive tax
0.190(0.558)
Yes
Yes
0.706
0.875
2715
33
0.016

Note: Robust standard errors are given in parentheses. *, ** and *** indicate 10%, 5% and 1% level of significance. P-values are reported for the Arellano-Bond and Hansen
test.

28

Table 4d. Panel regressions: United Kingdom sample
Dependent variable: Alpha

RCREi,t−1

Controls
Year fixed effectArellano-Bond test for AR(2)Hansen test of overidentification
Observations
Number of instruments
F statistic: compared to the coefficient in column (1)F statistic: difference between pre- and post-crisis

(1)
(2)
(3)
All firms Positive RD Positive tax(4)
All firms

Pre-crisis sample: 2001-2006

Post-crisis sample: 2010-2015-1.751**(0.861)
Yes
Yes
0.751
0.511
2362
33

-2.1*
(1.085)
Yes
Yes
0.254
0.894
1359
33
0.063
All firms: 8.88***(5)
Positive RD(6)
Positive tax-1.25*
1.251** 0.579*
0.756*(0.701)(0.523)(0.308)(0.436)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.412
0.699
0.591
0.948
0.882
0.833
0.86
0.823
2027
1508
954
1273
33
33
33
33
0.204
1.226
0.528
Positive RD: 5.64**
Positive tax: 5.1**

Note: Robust standard errors are given in parentheses. *, ** and *** indicate 10%, 5% and 1% level of significance. P-values are reported for the Arellano-Bond and Hansen
test.

29

Table 4e. Panel regressions: Japanese sample
Dependent variable: Alpha(1)
All firms(2)(3)
Positive RD Positive tax

Pre-crisis sample: 2001-2006
RCREi,t−1

Controls
Year fixed effectArellano-Bond test for AR(2)Hansen test of overidentification
Observations
Number of instruments
F statistic: compared to the coefficient in column (1)F statistic: difference between pre- and post-crisis-0.170***(0.057)
Yes
Yes
0.555
0.110
5604
33

(4)
All firms(5)(6)
Positive RD Positive tax

Post-crisis sample: 2010-2015-0.159**-0.157***
0.010
0.044
(0.075)(0.052)(0.071)(0.065)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.544
0.489
0.130
0.320
0.139
0.185
0.515
0.528
4131
5448
4789
3383
33
33
33
33
0.014
0.028
0.125
All firms: 3.91**
Positive RD: 4.18**
Positive tax: 3.54*-0.045(0.029)
Yes
Yes
0.276
0.581
4653
33
0.514

Note: Robust standard errors are given in parentheses. *, ** and *** indicate 10%, 5% and 1% level of significance. P-values are reported for the Arellano-Bond and Hansen
test.

30

Table 5. A summary of results: Relationship between CRE holding and stock return
Sample

Pre-Crisis Period (2001-2006)

Post-Crisis Period (2010-2015)

The United States
Europe (excluding the United Kingdom)
The United Kingdom
Japan

Negative
Positive
Negative
Negative

Positive
Insignificant
Positive
Insignificant

31

Appendix
Appendix A provides a comprehensive literature review.
Appendix B presents a model of a simple firm, which can engage in R&D investment
and corporate real estate (CRE) investment.
Appendix C presents additional tables related to different measures of CRE and thepanel regression with the corporate governance index.

32

Appendix A
This appendix mainly discusses two strands of the literature: the motives to own CRE
and the relationship between CRE holdings and firm performance.
Motives to Own CRE
In the main text, we indicate that there are different motives to own CRE beyond
production needs. Each purpose could result in another nexus between CRE holdings
and returns. The first motivation for CRE holding is "empire building." Due to weakcorporate governance, firms may over-invest in CRE and make less investment, and R& D. It leads to a negative correlation in CRE holding and stock return. Based on Real
Estate Investment Trusts (REITs) data in the U.S., Sirmans (1999) hypothesizes thatspecific sets of corporate governance mechanisms are needed for firms with substantial
real estate holding. Sing and Sirmans (2008) employ a sample of 228 stocks listed in
Singapore and formally reject the hypothesis that corporate governance mechanisms
are independent of a firm's real estate ownership. Thus, the result is consistent with
Sirmans (1999). Coles et al. (2006) show a strong causal relationship between
management incentives and firms' behavior on investment policy, debt policy, and risktaking. Employing a sample of U.S. listed corporations, Du et al. (2014) find noevidence for a return-enhancing role for CRE holdings, suggesting that CRE holdings
are a form of managerial "empire building." In firms with weak governance, overinvestment in the CRE is more likely to occur, and higher CRE holdings are associatedwith lower returns to shareholders. Dong et al. (2012) employ the Listed Chinese firms
and find that corporate governance, state ownership, and preferential tax policy
explain the CRE holding.
The second motive is related to CRE's collateral channel effect, which will lead toa positive nexus between CRE holdings and returns. Firms use CRE as inputs of33

production and collaterals to raise debt for investment, and firms could benefit from theappreciation of CRE holdings (Bernanke and Gertler 1989, 1990; Kiyotaki and Moore
1997; Chaney et al. 2012; Gan 2007a, b). For instance, Ogawa et al. (1996) and Ogawa
and Suzuki (1998) find that the land price fluctuations in Japan would affect corporate
investment behaviors. Gan (2007a) finds that, during the early 1990s, the investment
rate of an average firm in Japan drops by 0.8 percentage points resulting from a 10%
drop in land value. Chaney et al. (2012) also find that firms' investments in the U.S. aresubstantially affected by the shocks to the value of real estate holdings. For example,
during 1993-1997, a $1 increase in collateral value leads the representative U.S.
corporation to raise its investment by $0.06.

Relationship between CRE holdings and firm performance
After discussing the motives to own CRE, we review the literature on therelationship between CRE holdings and firm performance. The first strand of research
employs the idiosyncratic return (Alpha) and systematic risk component (Beta) tomeasure firm performance. Table A1 provides a summary of their main findings. For
example, in the case of the United States, Deng and Gyourko (1999) employ firm-level
data for 717 companies from 57 different non-real estate industries in the U.S. in 19841993 and find that firms with high degrees of real estate concentration and high Beta
experience lower returns. However, employing a similar sample period (1985-1994),
Seiler et al. (2001) find no relationship between CRE holdings and systematic risk andexcess return.
On the other hand, Tuzel (2010) finds that CRE holdings positively affectabnormal returns in non-real estate firms in the U.S. from 1963 to 2003. In the case ofother economies, Brounen and Eichholtz (2005) explores international CRE effects
using samples from 18 industries and nine countries in the year 1992, 1995, 1998, and34

2000, and find a significantly negative relationship between CRE holdings andsystematic risk, while no association between CRE holdings and idiosyncratic risk.
Finally, Cheong and Kim (1997) find that a listed manufacturing firm's CRE holdings
had no significant effect upon the return-on-investment in its stocks from 1987 to 1991
in Korea.
On the other hand, Liow and Ooi (2004) use entirely different measures of firm
performance. They evaluate stock return by two value-based metrics: economic value
added (EVA), and market value added (MVA). Based on the data of listed non-realestate firms in Singapore from 1997 to 2001, the authors find that CRE hurts non-realestate firms' EVA and MVA. Based on the data of listed non-real estate firms in
Singapore from 1997 to 2001, the authors find that CRE hurts non-real estate firms'
EVA and MVA.
Another strand of literature explores the impact of CRE holding on other aspectsof a firm's operation. For instance, Zhao and Sing (2016) empirically test therelationship between CRE holdings and the production risk of firms, which is measured
by the volatility of the output per unit of capital. The publicly listed U.S. firms' datafrom 1984 to 2011 prove that CRE holding is significantly and negatively correlatedwith a firm's productivity risks. As a result, firms with high productivity risk (morevolatile firms) hold a relatively lower level of the CRE.

35

Table A1. Previous literature on the relationship between CRE holdings and returns
Literature

SampleDeng and Gyourko 717 companies from(1999)
57 different non-realestate industries in
the U.S.
Seiler et al. (2001) Firms from fourindustries (SIC =20/35/36/37) in the
U.S.
Tuzel (2010)
Non-real estate firms
in the U.S.
Cheong and Kim Listed manufacturing(1997)
firms in Korea
Brounenand Samples from 18
Eichholtz (2005)
industries and 9
countries

Sample Period
1984-1993

Relationship
Systematic risk (Beta)

Abnormal return (Alpha)
Negative (for the high beta firm)

1985-1994

Insignificant

Insignificant

1963-2003

Mixed

Positive

1987-1991

Stock return

Insignificant

1992, 1995, 1998 Negativeand 2000

36

Insignificant

Appendix B
This section presents a simple model of a firm, which can engage in R&D investment
and corporate real estate (CRE) investment.
There are two periods, 𝑡𝑡 = 0,1. At time 0, a risk-neutral firm endowed with an amountof initial capital K and a linear technology to produce can choose to invest in R&D
investment, which would boost productivity and invest in CRE, whose valuation in time
1 can be different. For simplicity, we assume that all these investment decisions are
discrete. More specifically, the firm which invests 𝐷𝐷 units of capital, 0 < 𝐷𝐷 < 𝐾𝐾 has
a probability 𝑝𝑝 to be successful, 𝑝𝑝 ∈ [0,1], and its productivity would increase from𝐴𝐴 to 𝐴𝐴𝐴𝐴, 𝐴𝐴 > 0, 𝑔𝑔 > 1. If the firm fails, the productivity remains to be 𝐴𝐴. On theother hand, the firm can also acquire 1 unit of CRE, which costs 𝑃𝑃ℎ units of capital inperiod 0, 𝑃𝑃ℎ > 0. In period 1, the valuation of the CRE would become 𝑃𝑃ℎ 𝜀𝜀, where 𝜀𝜀
represents an idiosyncratic valuation shock. The shock has finite and positive support,
𝜀𝜀 ∈ [𝜀𝜀𝐿𝐿 , 𝜀𝜀𝐻𝐻 ], 0 < 𝜀𝜀𝐿𝐿 < 𝜀𝜀𝐻𝐻 < ∞. We assume that the first moment is also finite, 0 <𝐸𝐸(𝜀𝜀) < ∞. We assume that the valuation shock is independent of the risk involved in
the R&D if R&D efforts are ever be made. Alternatively, the firm may rent CRE fromthe market at a rate 𝑅𝑅ℎ , 0 < 𝑅𝑅ℎ < 𝑃𝑃ℎ . 16 And to produce in period 1, the firm needs
to pre-install capital in period 0. To simplify the analysis, we assume that 𝐾𝐾 − 𝐷𝐷 −𝑃𝑃ℎ > 0. We introduce two indicator functions to represent the firm's R&D and CRE
investment decisions. Formally,
𝐼𝐼 𝑅𝑅 = �1 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑅𝑅&𝐷𝐷
�,0
𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

1 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝐶𝐶𝐶𝐶𝐶𝐶
𝐼𝐼 𝐻𝐻 = ��.
0𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

Thus, the firm which maximizes the expected value of the profit is
𝑚𝑚𝑚𝑚𝑚𝑚. 𝐸𝐸(𝜋𝜋)Where
𝐸𝐸[𝜋𝜋(𝐼𝐼 𝑅𝑅 , 𝐼𝐼 𝐻𝐻 )] = {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]𝐼𝐼 𝑅𝑅 + (1 − 𝐼𝐼 𝑅𝑅 )𝐴𝐴} ∗ [𝐾𝐾 − 𝐼𝐼 𝑅𝑅 𝐷𝐷 − 𝐼𝐼 𝐻𝐻 𝑃𝑃ℎ −(1 − 𝐼𝐼 𝐻𝐻 )𝑅𝑅ℎ ] + 𝐼𝐼 𝐻𝐻 𝑃𝑃ℎ 𝜀𝜀. This formula looks more complicated than it is. For instance, theprofit for a firm engaging in both R&D and CRE investment is simply
𝐸𝐸[𝜋𝜋(1,1)] = {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐾𝐾 − 𝐷𝐷 − 𝑃𝑃ℎ ] + 𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀).

Similarly, the profit for a firm engaging in R&D but not CRE investment is simply
𝐸𝐸[𝜋𝜋(1,0)] = {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ].

The profit for a firm engaging in CRE but not R&D investment is simply
𝐸𝐸[𝜋𝜋(0,1)] = 𝐴𝐴 ∗ [𝐾𝐾 − 𝑃𝑃ℎ ] + 𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀).

The profit for a firm engaging in neither R&D nor CRE investment is simply
To further simplify the analysis, we can assume that the rental rate for the CRE, i.e. 𝑅𝑅ℎperiod 0.
16

37

is pre-determined in𝐸𝐸[𝜋𝜋(0,0)] = 𝐴𝐴 ∗ [𝐾𝐾 − 𝑅𝑅ℎ ].

Since the investment decisions are discrete, we simply compare different options
pairwise.

1

Lemma 1. If 𝑔𝑔−1 ∗ �
sufficiently large.

𝐸𝐸(𝜀𝜀)𝑅𝑅
𝑃𝑃ℎ𝐴𝐴�1− ℎ �

− 1� > 0, 𝐸𝐸[𝜋𝜋(1,0)] > 𝐸𝐸[𝜋𝜋(1,1)] if and only if 𝑝𝑝 is

Proof. The proof is straightforward. Observe that𝐸𝐸[𝜋𝜋(1,0)] − 𝐸𝐸[𝜋𝜋(1,1)]{[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ] − {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐾𝐾 − 𝐷𝐷 − 𝑃𝑃ℎ ] −=𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀)= {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ (𝑃𝑃ℎ − 𝑅𝑅ℎ ) − 𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀)1>0 if and only if 𝑝𝑝 > 𝑝𝑝1∗ , where 𝑝𝑝1∗ = 𝑔𝑔−1 ∗ �
1� > 0, 𝑝𝑝1∗ > 0.

𝑅𝑅

Notice further that in practice, 𝑃𝑃ℎ
that

1

𝑔𝑔−1

∗�

𝐸𝐸(𝜀𝜀)𝑅𝑅
𝑃𝑃ℎ𝐴𝐴�1− ℎ �

− 1� > 0.

ℎ

𝐸𝐸(𝜀𝜀)𝑅𝑅
𝐴𝐴�1− ℎ �𝑃𝑃ℎ1

− 1�. Since 𝑔𝑔−1 ∗ �

𝐸𝐸(𝜀𝜀)𝑅𝑅
𝑃𝑃ℎ𝐴𝐴�1− ℎ �−

is very small. Hence, if 𝐸𝐸(𝜀𝜀) > 𝐴𝐴 , then it is likely
𝑅𝑅

Notice further that if our condition is violated, for instance, 𝐸𝐸(𝜀𝜀) < 𝐴𝐴 �1 − 𝑃𝑃ℎ �. In thatℎcase, it means that every firm which satisfies the stated assumption would find it betterto invest in R&D only, rather than both R&D and CRE investment.

Lemma 2. If �

𝐴𝐴[𝐷𝐷+𝑅𝑅ℎ ]−𝑃𝑃ℎ �𝐴𝐴−𝐸𝐸(𝜀𝜀)�

sufficiently large.

𝐴𝐴(𝐾𝐾−𝐷𝐷−𝑅𝑅ℎ )� > 0,

𝐸𝐸[𝜋𝜋(1,0)] > 𝐸𝐸[𝜋𝜋(0,1)] if and only if 𝑝𝑝 is

Proof. The proof is again straightforward. Observe that𝐸𝐸[𝜋𝜋(1,0)] − 𝐸𝐸[𝜋𝜋(0,1)] =𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀)}{[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ] − {𝐴𝐴 ∗ [𝐾𝐾 − 𝑃𝑃ℎ ] += 𝑝𝑝(𝑔𝑔 − 1)𝐴𝐴𝐴𝐴 − {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐷𝐷 + 𝑅𝑅ℎ ] + 𝑃𝑃ℎ �𝐴𝐴 − 𝐸𝐸(𝜀𝜀)�
= 𝑝𝑝(𝑔𝑔 − 1)𝐴𝐴[𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ] − 𝐴𝐴[𝐷𝐷 + 𝑅𝑅ℎ ] + 𝑃𝑃ℎ �𝐴𝐴 − 𝐸𝐸(𝜀𝜀)�
38

1> 0 if and only if 𝑝𝑝 > 𝑝𝑝2∗ , where 𝑝𝑝2∗ = (𝑔𝑔−1) ∗ ��𝐴𝐴[𝐷𝐷+𝑅𝑅ℎ ]−𝑃𝑃ℎ �𝐴𝐴−𝐸𝐸(𝜀𝜀)�
𝐴𝐴(𝐾𝐾−𝐷𝐷−𝑅𝑅ℎ )� > 0, 𝑝𝑝2∗ > 0.

𝐴𝐴[𝐷𝐷+𝑅𝑅ℎ ]−𝑃𝑃ℎ �𝐴𝐴−𝐸𝐸(𝜀𝜀)�
𝐴𝐴(𝐾𝐾−𝐷𝐷−𝑅𝑅ℎ )� . Since

First, notice that 𝐴𝐴(𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ) > 0, and (𝑔𝑔 − 1) > 0 by assumption. Hence, itsuffices to study the term 𝐴𝐴[𝐷𝐷 + 𝑅𝑅ℎ ] − 𝑃𝑃ℎ �𝐴𝐴 − 𝐸𝐸(𝜀𝜀)�. And 𝐴𝐴[𝐷𝐷 + 𝑅𝑅ℎ ] − 𝑃𝑃ℎ �𝐴𝐴 −𝐸𝐸(𝜀𝜀)� > 0 iff 𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀) < 𝐴𝐴𝑃𝑃ℎ − 𝐴𝐴𝑅𝑅ℎ − 𝐴𝐴𝐴𝐴. Notice also that𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀): return from investing in CRE.

𝐴𝐴𝑃𝑃ℎ : addition return from investing in R&D only.
𝐴𝐴𝑅𝑅ℎ : loss from investing in R&D only.
𝐴𝐴𝐴𝐴: return from investing in R&D.

Thus, the RHS 𝐴𝐴𝑃𝑃ℎ − 𝐴𝐴𝑅𝑅ℎ − 𝐴𝐴𝐴𝐴 is the net return from R&D only, while the LHS
𝑃𝑃ℎ 𝐸𝐸(𝜀𝜀) is net return from investing in CRE. If LHS < RHS, then the firm will invest
in R&D only when the probability of success in R&D is sufficiently high.
Lemma 3. 𝐸𝐸[𝜋𝜋(1,0)] > 𝐸𝐸[𝜋𝜋(0,0)] if and only if 𝑝𝑝 is sufficiently large.
Proof. The proof is again straightforward. Observe that𝐸𝐸[𝜋𝜋(1,0)] − 𝐸𝐸[𝜋𝜋(0,0)] = {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]} ∗ [𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ] − 𝐴𝐴 ∗ [𝐾𝐾 − 𝑅𝑅ℎ ]= 𝑝𝑝(𝑔𝑔 − 1)𝐴𝐴[𝐾𝐾 − 𝑅𝑅ℎ ] − {[𝑝𝑝𝑝𝑝𝑝𝑝 + (1 − 𝑝𝑝)𝐴𝐴]}𝐷𝐷= 𝑝𝑝(𝑔𝑔 − 1)𝐴𝐴[𝐾𝐾 − 𝐷𝐷 − 𝑅𝑅ℎ ] − 𝐴𝐴𝐴𝐴

1𝐷𝐷

> 0 if and only if 𝑝𝑝 > 𝑝𝑝3∗ , where 𝑝𝑝3∗ = (𝑔𝑔−1) ∗ �(𝐾𝐾−𝐷𝐷−𝑅𝑅 )�. Notice that 𝑝𝑝3∗ > 0. Itℎmeans that some firms would find it optimal not to make any investment, should they
inherit a probability of success low enough.
Based on the three lemmas, we can define a new quantity 𝑝𝑝1∗∗ = 𝑚𝑚𝑚𝑚𝑚𝑚{𝑝𝑝1∗ , 𝑝𝑝2∗ , 𝑝𝑝3∗ }. And
for 𝑝𝑝 > 𝑝𝑝1∗∗ , it is necessary that 𝐸𝐸[𝜋𝜋(1,0)] = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎{𝐸𝐸[𝜋𝜋(𝐼𝐼 𝑅𝑅 , 𝐼𝐼 𝐻𝐻 )]}. In other words,
it means that investing in R&D but not in CRE is the best strategy if the probability ofsuccess in R&D is sufficiently high.

39Appendix C
Table C1. Different measures of CRE in the previous literature
Literature
Data
CRE measurement
SourceDeng and Gyourko Compustat Real Estate Concentration (RC)(1999)RC = (building at cost + land and improvements)/
Total Asset
Seiler et al. (2001) Compustat Real Asset (RA)RA = PPE/ Total Asset
Liow and Ooi Compustat Real Estate Asset Intensity (PPTY)(2004)PPTY = Tangible Asset/ Total AssetBrounenand Compustat Corporate Real Estate Ratio (CRER)
Eicholtz (2005)
CRER = PPE/ Total Asset
Tuzel (2010)
Compustat Real Estate Raio (RER)
RER = (buildings + capitalized leases)/ Total
Asset
Zhao and Tien Compustat Ratio of CRE ownership to total asset (CRE_A)(2016)
CRE_A = (building cost + land andimprovements + construction in progress)/ Total
Asset

40

Table C2. Panel regression with the corporate governance index
Dependent variable: Alpha(1)(2)-0.017 -0.042(0.082) (0.094)
0.009(0.007)
Yes
Yes
Yes
Yes
0.640
0.751
0.627
0.596
1686
1686
37
37

RCREi,t−1

cooperate governancei,t−1

Controls
Year fixed effectArellano-Bond test for AR(2)Hansen test of overidentification
Observations
Number of instruments

Key: All firms are from the U.S. sample. We do not distinguish between the pre-crisis and the postcrisis periods. The corporate governance index here is from Gompers et al. (2003).

41