2013-3
Corporate Real Estate Holdings: Fool’s Gold or Crown Jewel?
Takanori Fukushima Nobuyuki Isagawa
Tomohiro Mae Satoru Yamaguchi Takashi Yamasaki

Corporate Real Estate Holdings: Fool’s Gold or Crown Jewel?

Takanori Fukushimaa, Nobuyuki Isagawab, Tomohiko Maec, Satoru Yamaguchid, and Takashi
Yamasaki*,eaMizuho Securities, Strategic Solutions Department, JA Bldg. 1-3-1, Otemachi, Chiyoda-ku, Tokyo 100-6825,

Japan. E-mail: takanori.fukushima@mizuho-sc.com.
b

Kobe University, Graduate School of Business Administration, 2-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo

657-8501, Japan. E-mail: Isagawa@kobe-u.ac.jp.
cSumitomo Mitsui Trust Bank, Alternative Assets Investment Department, 1-4-1, Marunouchi, Chiyoda-ku,

Tokyo 100-8233, Japan. E-mail: mae_tomohiko@smtb.jp.
d

Hiroshima University of Economics, Department of Business Administration, 5-37-1, Gion, Asaminami,

Hiroshima-shi, Hiroshima 731-0138, Japan. E-mail: st-yamaguchi@hue.ac.jp.
eGraduate School of Business Administration, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo

657-8501, Japan. E-mail: yamasaki@kobe-u.ac.jp.
* Corresponding author. All correspondence should be directed to yamasaki@kobe-u.ac.jp.

Corporate Real Estate Holdings: Fool’s Gold or Crown Jewel?

Abstract
This paper aims to introduce new evidence of consistently negative impacts on enterprisevalue when corporations increase real estate holdings. Adopting a different approach fromprevious studies, our data analysis shows that corporate real estate portfolios and enterprisevalue share an inverse relationship. Surprisingly, this trend was seen during the Japanesereal estate bubble in the late 1980’s. Previous reports from the United States and Singapore
show that only in the retail sector do increases in corporate real estate holdings have apositive influence on stock performance. However, our research indicates that the stock
performance of Japanese firms in the retail sector was negative across the board for firms inother sectors.

Keywords: Corporate real estate, Stock performance, Systematic risk, Stock market

11. Introduction
Corporations raise capital and deploy these funds to invest in assets as part of theirbusiness activities. In turn, the performance of such activities is evaluated by investors using
various profitability indexes, such as return on invested capital and return on assets. If
investment performance exceeds the cost of capital, the firm’s enterprise value increases andvice versa. This mechanism is the fundamental essence of corporate finance.
Corporate real estate (CRE) is defined as the total of land and buildings owned by a firm
and makes up the largest portion of its asset holdings. Yu and Liow (2009) report that as of2006, the ratio of CRE to total assets (CRER) has been about 36%, 33%, and 32% for North
America, Europe, and Asia, respectively. From this finding leads us to conclude that it isessential for firms to devise financial strategies that utilize CRE effectively to maximize
enterprise value.
On the other hand, other studies on this topic point out that the stock market evaluates
real estate-heavy firms negatively. Liow (2004) finds such a negative impact of CRE holdingson Singaporean firms. Using a dataset of nine countries, Brounen and Eichholtz (2005) alsofind a relationship between CRE holdings and stock performance that is negative but notstatistically significant. The authors hypothesize that this is due to market participants
viewing investments in CRE as a necessary evil, since they require the commitment of vast
capital for relatively long periods.
2

In addition, some studies point out that the relationship between CRE holdings and stock
performance varies, depending on the firm’s industry. Brounen and Eichholtz (2005) reportthat the impact of CRE holdings on stock performance hinges on the firm’s industry sector
rather than geography. Because retailers are highly dependent on their store locations in termsof sales and CRE is therefore closely integrated with the core business, Brounen et al. (2005)
focus on listed retail companies. The authors find that their CRE holdings are generally
associated with a positive return performance. Contrary to the literature that claims a negative
relationship, Yu and Liow (2009) report positive impacts of CRE holdings on retail sectors
using a different analytical methodology.
However, most previous studies focus primarily on the cross-sectional impact of CRE
holdings on stock performance and do not factor in the time-series variation of real estate
prices.1 As observed in the 2008 U.S. housing bubble collapse, real estate prices fluctuateconsiderably with economic conditions (i.e., the boom and bust cycle). This poses aninteresting question: Does the stock market deem CRE holdings a negative factor on
shareholder value when real estate prices go up rapidly?
Furthermore, we notice that the numbers of observations and representative subsets usedin previous studies are relatively small, raising the question of the overall validity of those1

Liow (2004) uses data from 1997–2001 and states that CRE holdings had a negative impact on the stock

performance of non-real estate firms immediately following the Asian financial crisis. The authors add that thisconclusion should not be applicable to other periods.

3

findings. Against such a backdrop, we perform tests using extensive data with respect to bothcross-sectional and timer-series analyses.
This paper analyzes the impact of changes in CRE holdings on Japanese non-real estate
firms by adopting a different methodology from that of previous studies. The advantages oftargeting Japanese firms are as follows. First, we can acquire vast amounts of data in terms ofboth cross section and time series since the Japanese stock market is one of the largest in theworld since a long time. Furthermore, since Japan’s real estate market is also the second
largest in the world, 2 it is suitable for testing our hypothesis. We collected 27,753
observations for our analysis.
Second, the Japanese economy is prime testing ground for our hypothesis, because Japan
experienced a real estate bubble and its collapse from the late 1980’s to the early 1990’s.
Using data from the Land Market Value Publication published by the Ministry of Land,
Infrastructure, Transport and Tourism, Figure 1 shows the fluctuation of land prices in Japan(1970 prices benchmarked at 100 points). Based on the overall average from this figure, we
notice that land prices rose sharply, by about 600 points, during the 1980’s and fell by 300
points as of 2009. Using panel data during the 30-year period since 1978, we seek to examine
any significant changes in the relationship between CRE holdings and stock performance
during the real estate bubble of the 1980’s until its collapse after the early 1990’s.
2

According to Fiorilla et al. (2012), the size of the global institutional-grade commercial real estate market in

Japan was US$2.7 trillion, the second largest after the United States as of 2011.

4

(Figure 1)
As a result, we find that the stock performance of firms that have increased their CRE
holdings is, on average, significantly negative. In addition, we show that these firms do notexperience a significant increase in systematic risk (i.e., beta close to zero). Surprisingly, sucha trend was observed even when the real estate bubble economy was fairly buoyant. Contrary
to several overseas studies that report that only in the retail sector does property ownership
positively influence stock performance, our research results show the exact opposite, where
the stock performance of Japanese firms in the retail sector is consistently negative, in parallelwith the trend of firms in other sectors.
The remainder of this paper is organized as follows. The next section reviews theliterature. Section 3 develops the methodology and discusses the sample data used in our
research. Section 4 presents the empirical results, followed by Section 5, which draws ourconclusions.

2. Literature Review
Seiler et al. (2001) investigate the relationship between CRE ownership and firm
performance. In their analysis, they use a set of two-stage least squares (2SLS) equations to
examine whether real assets provide a diversification benefit to US corporations. The authors
assume that if real estate assets provide such a benefit, then firms with real estate assets
5should achieve a higher rate of return for a given level of risk or a lower level of risk for agiven rate of return. Their results from scrutinizing a dataset of 80 US firms from 1985
through 1994 show no significant relationship between CRE and systematic beta or between
CRE and risk-adjusted returns. From these findings, the authors conclude that further researchis needed to draw any generalizations.
Liow (2004) examines the impact of CRE on the stock market for 75 Singaporean
non-real estate corporations with portfolios comprised of at least 20% real estate assets from1997 to 2001. The author uses the monthly property proxy index and the stock market index
to isolate hypothetical business returns from stock returns. The results of calculating thecomparative median returns, total risk, time-varying systematic risk, and time-varying
risk-adjusted return values for the four pairs of stock return and business return series, suggestthat CRE holdings are associated with lower returns, lower risk-adjusted returns, higher risks,and higher systematic risk. This negative impact of CRE on stock market performance isconsistent across the board for all non-real estate firms in different industries and with varying
levels of exposure to real estate assets.
Brounen and Eichholtz (2005) use 2SLS to investigate the relationship between CRE
holdings and stock market performance in nine countries for the years 1992, 1995, 1998, and2000. Their findings indicate that aggregate CRE holdings have significantly decreased overtime, although the levels of CRE exposure vary across industries. Using the same6

aforementioned method to measure the impact of CRE holdings on stock market performance,
the authors find an insignificant adverse relationship between CRE holdings and bothrisk-adjusted return and firm systematic risk, although such impact differs across industries.
Furthermore, they find that the retail sector is generally associated with higher CRE exposure,largely due to the inevitable nature of real estate constituting a vitally strategic asset forbusinesses engaged in this sector.
Hwang et al. (2005) examine the diversification effects of CRE holdings for US-based
multinational corporations (MNCs). They use a series of rolling 2SLS regression models on asample set of data from 91 firms selected from the first quarter of 1992 through the fourth
quarter of 2001 to determine the relationships shared between CRE holdings, systematic risk
(beta), and risk-adjusted returns. They find that the CRE holdings of US-based MNCs have asignificant negative impact on firm systematic risk during limited periods but no significant
impact on risk-adjusted returns. These findings ultimately imply that the CRE holdings ofUS-based MNCs do not have a diversification effect on firm systematic risk.
Brounen et al. (2005) focus on firms in the retail sector since CRE holdings in this sector
appear to be strategically important for these firms. By applying an ordinary least squares
regression methodology to data accumulated from 454 international retailers between 1999
and 2002, the authors measure the impact of CRE holdings on their respective stock
performance. The results show that CRE holdings have a significant positive relationship with7

risk-adjusted returns and a significantly adverse effect on systematic risk. Although Brounen
and Eichholtz (2005) reports an insignificant negative relationship between CRE holdings,
risk, and returns in general, the results suggest that this relationship may be dependent on how
closely CRE holdings are associated with the firm’s core business.
Employing the same method as Liow (2004), Yu and Liow (2009) study the impact of
CRE holdings on risk returns for international retail firms from a sample set of 556 firms from15 countries between 2001 and 2006. Similar to Brounen et al. (2005), the authors find thathigher levels of CRE holdings are associated with better stock market performance. In
particular, their results indicate that positive market performance is incremental on adiminishing return to scale (i.e., performance growth becomes proportionately lower as CRE
exposure increases), which suggests that there may be an optimal level of CRE holdings to
induce relatively positive stock performance benefits. The authors conclude that further
research is needed to verify this hypothesis.
Looking back on previous studies, we identify no consistent relationship between CRE
holdings and risk return in general, thus implying the fact that the impact of CRE holdings on
stock performance may depend on the subject company’s industry sector or general businesscharacteristics. Although previous studies are limited to analyzing cross-sectional data
between the 1980’s and the early 2000’s, our study encompasses a broader time span (the1970’s to the late 2000’s) that includes both economic boom and bust cycles of the real estate8

markets (i.e., asset bubbles). The study also adopts the panel data methodology to consider the
potential impact of economic status on CRE holdings. In contrast to previous studies based onrather narrow sample sets, our study employs an extensive sample set of over 20,000 samples,leading to more precise empirical results.

3.

Research Design

3.1. Methodology
As with previous studies, we define CRE holdings as buildings and structures plus landand other non-depreciable property. To estimate the impact of the change of CRE holdings onchanges in excess return and systematic risk, we measure the former based on the Fama andFrench three-factor model (Fama and French (1993)). We use a window of 48 months (t - 35
to t + 12, where t = 0 is the end of a given fiscal year) to estimate the parameters of theregression model:rii  rmt   i   i d t  bi ( rmt  rft )  bi d t ( rmt  rft ) si SMBt  si d t SMBt  hi HMLt  hi d t HMLt   t(1)where dt is a dummy variable equal to one during a given fiscal year from t + 1 to t + 12 andzero otherwise; Δαidt measures the changes in excess return during a given fiscal year whenfirms increase or decrease CRE holdings; and, similarly, Δbidt, Δsidt, and Δhidt measure thechanges in factor loading during a given fiscal year when firms increase or decrease CRE

9holdings. We obtain the changes in systematic risk (ΔRISK) by summing up the factor
loadings.
Previous studies show that excess return and systematic risk are correlated with firm size,
leverage, and the book-to-market ratio. To allow for differences in a firm’s financial profile,
we regress changes in excess return and systematic risk on firm size, leverage, and thebook-to-market ratio:
 i  1   2 CRERt   3CRERt   4 LnSIZEt   5 LEVt   6 BM t   t(2)

RISK i  1   2 CRERt   3CRERt   4 LnSIZEt   5 LEVt   6 BM t   t(3)

where CRERt is CRE holdings divided by the book value of total assets, LnSIZEt is the naturallog of the book value of total assets, LEVt is debt divided by the book value of total assets,
BMt is the book value of equity divided by the market value of equity, and ΔCRERt measureschanges in CRE holdings from t to t + 1. We define ΔCRERt as CRE holdings at fiscal yeart + 1 minus CRE holdings at fiscal year t, which are divided by the book value of assets at
fiscal year t. All reported regressions use White (1980) heteroskedastic-consistent errors
corrected for correlations across a given firm’s observations.

3.2. Sample
Stock data are compiled from Portfolio Master, provided by Financial Data Solutions,and financial data are compiled from NEEDS Financial Quest, provided by Nikkei Media
10

Marketing. Our sample set consists of all firms listed on first and second sections of theTokyo Stock Exchange (TSE) between September 1977 and December 2009. In accordancewith previous research methodology, financial firms, utility firms, and real estate companies
are excluded from the sample set. To further narrow down the sample, any firm without
continuous monthly returns from t - 35 to t + 12 are excluded. Also excluded are firms whose
data on CRE holdings, market value, total liabilities, and market-to-book ratio of equity iseither unavailable or unusable for two consecutive fiscal periods. The top and bottom 1% of
all variables are omitted. The final sample consists of 27,753 firm–years.
Table 1 is a summary of CRER statistics calculated by CRE divided by total assets,
sorted by industry based on the TSE classification.3 This table shows that average CRE
holdings are 18.3% against total assets. The land transportation sector has the highest CRE
holdings ratio (40% of total assets) and the marine transportation ranks the lowest (6.6% of
total assets).
(Table 1)

4. Results
This section highlights our empirical results, beginning with statistics from the univariate
analysis in Section 4.1 and followed by results of the regression analysis in Section 4.2, to3Five industry sectors excluded from the TSE’s 33 sectors are banks, insurance, securities and commodities

futures, electric power and gas, and real estate.

11

accurately measure the relationship between changes in excess return or systematic risk with
CRE holdings.

4.1. Univariate Analysis
To investigate the relationship between CRE holdings and changes in excess return orsystematic risk, we divide the sample into two groups, based on whether the firms in question
increased or decreased CRE during a given fiscal year. Table 2 shows the results of themedian difference between the set categorized by increased CRE and that categorized bydecreased CRE. For this analysis, we exclude firms that did not change CRE holdings (ΔCRE= 0). Panel A of Table 2 reports the results for the total sample. The figures from Panel A
imply that CRE holdings are perceived negatively by the market: Δα is noticeably negativefor firms that increased CRE, but noticeably positive for firms that decreased CRE. The
Inc-Dec column shows that the difference in Δα for firms that altered their CRE exposure issignificantly negative at the 1% level.
This relationship is prevalent in other subsample periods. Panel B of Table 2 shows theresults for the bubble economy period. As is apparent from the ΔCRE data, firms
continuously increased CRE holdings during this period. Based on these results, it seems thatstock market participants do not consistently appreciate firms that increase their CRE
portfolios.
12

From the data in Panel C of Table 2, we observe that the excess returns of
CRE-increasing firms are far below the levels seen in other subsamples. The real estate myth
was debunked as land prices collapsed during this period. Such results imply that the market
does not perceive CRE as a profitable investment. Panel D shows the results during therebound period, when land prices gradually returned to pre-bubble levels. This panel indicatesthat the difference between the two groups (increase/decrease) is significantly negative.
Hence, regardless of economic conditions, firms that increase CRE holdings are consistently
perceived as experiencing negative performance in comparison to firms that decrease CRE
exposure.
However, when focusing on changes in systematic risk, no such relationship is found.
The median difference of ΔRISK between the two subgroups (increase/decrease) is marginal.
The term ΔRISK does not change substantially whether a firm increases or decreases its CRE
holdings. The above results indicate that CRE exposure has barely any impact on systematicrisk.
(Table 2)

4.2. Regression Analysis
To accurately estimate the impact of changes in CRE holding on changes in excess
return or systematic risk, we regress Δα or ΔRISK on ΔCRER, CRER, LnSIZE, LEV, and BM.
13

Table 3 shows the results of regression analysis. Each panel shows the results for Δα andΔRISK, along with regressions reports. Focusing on the results of the simple regression
analysis on Δα, we find the coefficient of ΔCRER to be significantly negative at the 1% levelin all our sample periods. On the other hand, there is no identifiable relationship with ΔRISK.
These results are consistent with those of the univariate analysis in Section 4.1.
Previous studies show that excess return and systematic risk are correlated with firmsize, leverage, and book-to-market ratio. To allow for differences in firm financial profiles,
we conduct a multiple regression analysis. The second row of each panel in Table 3 shows theresults for each multiple regression test.
Panel A shows that the CRER coefficient is significantly negative. This result implies
that Δα is increasingly negative as CRE holdings levels increase. Panel A also shows that thecoefficients of LEV and BM are significantly positive. Central to our thesis, our main findingis that the coefficient of ΔCRER is significantly negative at the 1% level. This relation holds
true for all our sample periods. These results indicate that the impact of increases in CRE
holdings on Δα is negative and remains consistently negative, regardless of economic
conditions.
Apart from the regression results on Δα, ΔCRER and CRER do not have any significant
effects on ΔRISK during any our sample periods. All things considered, these results indicatethat CRE holdings have a negative impact on Δα but no effect on ΔRISK. This implies that the14

stock market perceives CRE holdings as a negative factor in enterprise performance, even if
the effect of CRE holdings on systematic risk is taken into account.
(Table 3)
Brounen et al. (2005) and Yu and Liow (2009) examine the relationship between excess
return and CRE holdings in the retail industry. In addition, Brounen et al. (2005) report that
CRE holdings have an impact on systematic risk. To clarify whether the impact of CRE
holdings on Δα or ΔRISK varies by industry, we re-estimate equations (2) and (3), sorted byindustry (see Table 4).
Table 4 shows the coefficient of ΔCRER based on the multiple regression analysis fromequations (2) and (3). It reports that the coefficients are negative throughout almost allindustries. Note that only the marine transportation industry, with the lowest ratio of CRE
holdings, has a significant positive coefficient. In this industry, α significantly increases with
CRE holdings. From the market’s perspective, this industry makes effective use of its CRE
holdings. In terms of ΔRISK, we do not find any significant relationship, with the exception ofa few industries. Table 4 reveals that in the oil and coal products industry, systematic riskincreases with any increase in CRE holdings.
Within the retail industry, contrary to Brounen et al. (2005) and Yu and Liow (2009), we
find that the relationship between Δα and ΔCRER is significantly negative. In addition, we do
not find any significant relationship between ΔRISK and ΔCRER.
15

(Table 4)

5. Conclusion
This paper analyzes the impact of changes in CRE holdings on Japanese non-real estate
firms by adopting a different methodology from that of previous studies. As a result, we findthat the stock performance of firms that increased their CRE holdings is, on average,
significantly negative. In addition, our research does not reveal any significant relationshipbetween CRE holdings and systematic risk. Therefore, our results imply that firms that
increase CRE holdings are unable to make effective use of them.
Surprisingly, however, such a trend was observed even when the real estate bubble
economy was fairly swollen. During the period when real estate prices skyrocketed, realestate-heavy firms should have seen their enterprise value grow from increasing the holdingsof their property portfolios even if they could not make effective use of them. Nevertheless,the fact that the stock market does not evaluate firms that increase their CRE holdings very
highly is a phenomenon that requires attention.
Furthermore, we examine the impact of changes in CRE holdings on Japanese non-realestate firms by industry. Unlike Brounen et al. (2005) and Yu and Liow (2009), we findsignificant negative relationship between the stock performance and CRE holdings of firms in16

the retail sector as well as other sectors. One of the reasons is that although they focus on
recent several years, we examine using the long-period data since 1978.
In conclusion, our results show that excessive CRE holdings tend to impair firm
enterprise value, regardless of the market environment. Based on these findings, corporations
should proactively develop more effective CRE strategies by disposing of unnecessary assets
or leasing.

17

References
Brounen, D., Colliander, G., and Eichholtz, P. M. A. (2005). “Corporate real estate and stock
performance in the international retail sector,” Journal of Corporate Real Estate, 7(4), 287–
299.
Brounen, D., and Eichholtz, P. M. A. (2005). “Corporate real estate ownership implications:International performance evidence,” Journal of Real Estate Finance and Economics, 30(4),429–445.
Fama, E. F., and French, K. R. (1993). “Common risk factors in the returns on stocks andbonds,” Journal of Financial Economics, 33(1), 3–56.
Fiorilla, P., Kapas, M., and Liang, Y. (2012). A Bird’s Eye View of Global Real Estate
Markets: 2012 Update. Prudential Real Estate Investors, New Jersey.
Hwang, E. S., Seiler, V. L., and Seiler, M. J. (2005). “Multinational companies’ real asset
ownership and its impact on diversification,” Journal of Corporate Real Estate, 7(4), 326–
338.
Liow, K. H. (2004). “Corporate real estate and stock market performance,” Journal of Real
Estate Finance and Economics, 29(1), 119–140.
Seiler, M. J., Chatrath, A., and Webb, J. R. (2001). “Real asset ownership and the risk andreturn to stockholders,” Journal of Real Estate Research, 22(1), 199–212.

18

Yu, S. M., and Liow. K. H. (2009). “Do retail firms benefit from real estate ownership?”
Journal of Property Research, 26(1), 25–60.
White, H. (1980). “A heteroskedasticity-consistent covariance matrix estimator and a direct
test for heteroskedasticity” Econometrica, 48(4), 817–838.

19

Figure 1: Land prices in Japan from 1970 to 2010
This figure shows the fluctuation of land prices in Japan (1970 prices benchmarked at 100 points).
800

700

600

500

400

300

200

100

Residential land

Building site with an interim use

Commercial land

Quasi‐industrial land

Industrial land

Current building site in an urbanization control area

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

1973

1972

1971

1970

0

Overall average

Source: Land Market Value Publication, published by the Ministry of Land, Infrastructure, Transport andTourism.

20

Table1: CRER by industry
This table summarizes CRER statistics by industry sector in ascending order of mean CRER. The industry
sectors are categorized by the same classification as the TSE (33 sectors). Five sectors—banks, insurance,
securities and commodities futures, electric power and gas, and real estate—are excluded from the sample.

Marine Transportation
Fishery, Agriculture and Forestry
Construction
Wholesale Trade
Electric Appliances
Precision Instruments
Air Transportation
Machinery
Nonferrous Metals
Information & Communication
Pharmaceutical
Iron and SteelTransportation Equipment
MiningChemicals
Pulp and Paper
Glass and Ceramics Products
Metal Products
Textiles and Apparels
Oil and Coal Products
Other Products
Rubber Products
Foods
Services
Retail Trade
Warehousing and Harbor Transportation Service
Land Transportation
Mean/Total

Min
0.006
0.008
0.007
0.006
0.006
0.008
0.042
0.006
0.013
0.006
0.032
0.010
0.026
0.018
0.006
0.059
0.022
0.033
0.006
0.010
0.013
0.044
0.038
0.007
0.016
0.014
0.059
0.019

21

Mean
0.066
0.109
0.109
0.116
0.124
0.128
0.130
0.140
0.149
0.156
0.157
0.167
0.171
0.173
0.174
0.175
0.179
0.193
0.194
0.194
0.195
0.196
0.230
0.242
0.286
0.392
0.401
0.183

Median
0.031
0.079
0.091
0.085
0.107
0.112
0.107
0.127
0.130
0.126
0.141
0.163
0.154
0.169
0.163
0.159
0.163
0.174
0.171
0.170
0.190
0.174
0.212
0.223
0.275
0.412
0.411
0.167

Max
0.500
0.604
0.532
0.589
0.532
0.438
0.423
0.555
0.448
0.550
0.456
0.528
0.604
0.461
0.527
0.424
0.607
0.609
0.615
0.423
0.521
0.597
0.601
0.613
0.615
0.613
0.613
0.541

S.D.
0.083
0.118
0.077
0.101
0.074
0.062
0.090
0.077
0.080
0.128
0.068
0.075
0.078
0.088
0.068
0.067
0.089
0.096
0.114
0.097
0.090
0.104
0.102
0.156
0.129
0.152
0.126
0.096

Sample
353
158
2450
2123
3161
598
90
2647
668
361
655
853
1427
212
2520
445
795
800
1004
227
789
313
1693
1065
1567
351
428
27753

Table 2: Univariate analysis results
This table shows the mean of Δα and ΔRISK, where Δα (ΔRISK) measures changes in excess returns (orsystematic risk) relative to the past three years. We divide firms into two groups, based on whether firms
increased or decreased their CRE holdings. Here Increase/Decrease indicates a group of firms that
increased/decreased CRE holdings during the fiscal year and Inc-Dec denotes the median difference of Δα andΔRISK between Increase and Decrease. We exclude firms that did not change CRE holdings from our sample.
Panel A shows the results for the total sample. Panel B shows the results for the subsample from August 1980
to March 1991, the bubble economy period. Panel C shows the results for the subsample from April 1991 toMarch 2005, the period after the bubble economy period. Panel D shows the results for the subsample from
April 2005 to December 2008, the rebound period. The superscripts ***, **, and * indicate that the results ofthe t-test for the mean difference and signed-rank test for the median difference are significantly different fromzero at the 1%, 5%, and 10% levels, respectively.
ΔCRE
Inc-Dec
Increase Decrease
Panel A: Total Sample
Mean-0.239*** 0.080*** -0.319***
t- value
-7.63
2.94
-7.68
Median -0.321*** -0.059 -0.262***z- value-10.080.09-7.53
N
11,212
16,485
Panel B: From Aug 1980 to Mar 1991
Mean
-0.159*** 0.208*** -0.366***
t- value
-3.12
3.80
-4.91
Median -0.247*** 0.070*** -0.317***z- value
-4.47
2.82
-5.07
N
4,640
4,219
Panel C: From Apr 1991 to Mar 2005
Mean
-0.328*** -0.012 -0.311***t- value
-7.84
-0.36
-5.85
Median -0.394*** -0.135*** -0.260***
z- value
-9.73
-2.64
-5.67
N
5,277
9,723
Panel D: From Apr 2005 to Dec 2008
Mean
-0.162 0.221*** -0.382***
t- value
-1.53
2.88
-2.92
Median-0.257**0.014 -0.272***z- value
-2.08
1.55
-2.63
N
1,295
2,543
Δα (%)

ΔCRE
Increase Decrease

Inc-Dec

Meant- valueMedianz- value
N

0.031
1.37
0.005
0.34
11,2120.044**
2.20-0.012
0.58
16,485-0.013
-0.42
0.016
-0.03
-

Mean
t- valueMedianz- valueN-0.049-1.28-0.082**
-2.17
4,640

0.027
0.64
-0.058-0.53
4,219-0.076-1.34-0.024-0.998-Meant- valueMedianz- valueN

0.086*** 0.062**
2.92
2.52
0.028** 0.014*
1.99
1.69
5,277
9,723

0.024
0.63
0.018
0.57
-

ΔRISK (%)

Mean
t- valueMedianz- value
N

22

0.095
1.20
0.096
1.26
1,295

0.005
0.09
-0.067
-0.83
2,543

0.090
0.92
0.163
1.508
-

Table 3: Regression results
This table shows the results from the regression analysis on Δα and ΔRISK in each panel. For each regression,
the first row shows the results from the simple regression analysis and the second row shows result from themultiple regression analysis. Panel A shows the results for the total sample. Panel B shows the results for the
subsample from August 1980 to March 1991 (the bubble economy period). Panel C shows the results for the
subsample from April 1991 to March 2005 (the post-bubble economy period). Panel D shows the results for the
subsample from April 2005 to December 2008 (the rebound period). The t-values, calculated using White’s(1980) heteroskedastic-consistent error method corrected for correlations across observations of a given firm, arein parentheses. The superscripts ***, **, and * indicate significant difference from zero at the 1%, 5%, and 10%
levels, respectively.

Dependent variable

Const

ΔCRE

CRE

Independent variable
LnSizeLEVBM

Panel A: Total Sample-0.021 -6.707***(-1.21)(-7.30)Δα-0.500*** -5.655*** -0.568*** 0.010 0.309*** 0.376***(-3.09)(-6.13)(-3.47)(0.77)(3.30)(9.61)
0.039*** -0.113
3.24
-0.17
ΔRISK
-0.201*
0.120
0.016
0.009
0.137** 0.074***(-1.77)(0.18)(0.14)(0.97)(2.15)(2.87)
Panel B: From Aug 1980 to Mar 1991
0.085*** -9.353***(2.57)(-5.07)Δα-1.763*** -6.891*** 0.404 0.076*** 0.248 1.775***(-5.95)(-3.68)(1.18)(3.37)(1.42)(11.96)0.002-2.167(0.08)(-1.61)ΔRISK-0.243-1.636-0.2320.017-0.20
0.506***(-1.11)(-1.18)(-0.90)(0.97)(-1.46)(4.73)
Panel C: From Apr 1991 to Mar 2005
-0.107*** -4.851***(-4.73)(-4.31)Δα-0.135 -4.325*** -0.527** -0.003-0.145 0.282***(-0.61)(-3.84)(-2.51)(-0.19)(-1.14)(6.32)
0.068*** 0.851(4.21)(1.07)
ΔRISK
0.296*
0.998
0.169 -0.040*** 0.331*** -0.003(1.91)(1.25)(1.11)(-3.13)(3.88)(-0.11)Panel D: From Apr 2005 to Dec 2008
0.094 -10.676***(1.53)(-3.75)Δα-1.585*** -7.316*** -0.592
0.012 2.064*** 0.706***(-2.69)(-2.57)(-1.22)(0.26)(6.26)(4.78)
0.036
0.296(0.83)(0.13)
ΔRISK-1.608*** 0.679-0.416 0.129*** 0.331
0.148(-3.87)(0.30)(-1.20)(3.82)(1.42)(1.38)

23

R2

N

0.002

27753

0.005

27753

0.000

27753

0.000

27753

0.003

8891

0.019

8891

0.000

8891

0.003

8891

0.001

15017

0.004

15017

0.000

15017

0.001

15017

0.004

3845

0.017

3845

0.000

3845

0.003

3845

Table 4: Multiple regression analysis results by industry
This table shows the coefficients of ΔCRER estimated from multiple regression analysis by industry, based onequations (2) and (3). The t-values, calculated using White’s (1980) heteroskedastic-consistent error method
corrected for correlations across observations of a given firm, are in parentheses. The superscripts ***, **, and *indicate significantly difference from zero at the 1%, 5%, and 10% levels, respectively.
Dependent variable :Industry
Marine Transportation
Fishery, Agriculture and Forestry
Construction
Wholesale Trade
Electric Appliances
Precision Instruments
Air Transportation
Machinery
Nonferrous Metals
Information & Communication
Pharmaceutical
Iron and SteelTransportation Equipment
MiningChemicals
Pulp and Paper
Glass and Ceramics Products
Metal Products
Textiles and Apparels
Oil and Coal Products
Other Products
Rubber Products
Foods
Services
Retail Trade
Warehousing and Harbor Transportation Service
Land Transportation

Δα (%)
t -value
Coef
31.424**
2.54-13.803-1.40-12.757*** -3.21-0.431-0.13-4.101-1.02-19.478** -2.52
9.693
0.54
-8.960*** -2.64-11.569-1.85-3.215-0.36-2.125-0.36-18.69*** -2.62-0.0250.00-9.566-1.43-8.590*** -2.98-5.127-0.91-14.069** -2.39-2.071-0.37-10.153*-1.83-12.232
-1.43
2.193
0.33
-5.595-0.79-6.740**
-2.39
0.439
0.12
-4.008*-1.68-3.777-0.87-4.684-1.25

ΔRISK (%)
Coeft -value
2.040
0.30
11.3571.41-3.455
-1.11
3.129
1.07
-1.530-0.60-2.771-0.66-0.596-0.02-2.034-0.96-0.343-0.08
15.181*
1.92-7.651*
-1.91
3.556
0.75
3.886
1.17
3.382
0.49
3.966
1.45
-2.232-0.54-1.214-0.28-2.595-0.69-0.233-0.08
16.350*** 3.00
-1.260-0.38-6.004-0.68-4.399*
-1.83
2.318
0.93
0.978
0.55
-3.510-1.00-1.514
-0.39

N
353
158
2450
2123
3161
598
90
2647
668
361
655
853
1427
212
2520
445
795
800
1004
227
789
313
1693
1065
1567
351
428

[2013.1.29 1112]

24