White Paper on Transparency, Integration, and the Cost of International

Description
This paper investigates the importance of market institutions for the performance of international property investors during the 1996-2007 period. The results show that international property companies under perform local property companies in the early years of the sample period. This under performance is driven by the political environment, the level of economic integration, and the transparency of the real estate market in target countries.

Transparency, Integration, and the Cost
of International Real Estate Investments
Piet M. A. Eichholtz & Nils Gugler & Nils Kok
#
The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract This paper investigates the importance of market institutions for the
performance of international property investors during the 1996–2007 period. The
results show that international property companies underperform local property
companies in the early years of the sample period. This underperformance is driven
by the political environment, the level of economic integration, and the transparency
of the real estate market in target countries. The underperformance of internationals
disappears in the later years of the sample period, and so does the significance of the
aforementioned factors in explaining performance differences among international
companies. These findings suggest that the increased transparency of the global real
estate industry has leveled the playing field for foreign property investors.
Keywords Real estate
.
Transparency
.
Political risk
.
Investments
.
International
Introduction
The path-breaking studies of La Porta et al. (1998, 2000a, 2002) have clearly
demonstrated the importance of the legal institutions underpinning international
capital markets for international investments. Political respect for property rights, a
reliable corporate governance framework, and legal protection of outside share-
holders have all been shown to be beneficial for the development of financial
markets and the valuation of firms. Indeed, Dahlquist et al. (2003) demonstrate that
J Real Estate Finan Econ
DOI 10.1007/s11146-010-9244-5
We thank seminar participants at the 2008 REIT symposium at DePaul University, Chicago, as well as
Joseph Ooij and Andy Naranjo for their helpful comments.
P. M. A. Eichholtz
:
N. Gugler
:
N. Kok (*)
Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands
e-mail: [email protected]
P. M. A. Eichholtz
e-mail: [email protected]
N. Gugler
e-mail: [email protected]
the home bias in equity portfolio holdings by investors is, at least partly, the result of
corporate governance factors in foreign markets. Yet for real estate investments, the
effect of institutional differences across national markets on the shape and
performance of international investment portfolios has not yet been documented.
This is surprising, since the quintessential characteristic of real estate, its immobility,
would make it especially vulnerable to weak institutional settings. This may be part of
the reason why real estate investment, until quite recently, was mainly a local affair.
However, real estate investment has become increasingly global over the past decade.
This globalization is reflected in global investment volumes. For example, over
the 5 year period to 2006, cross-border investments have tripled to a level of US$
116 billion, which amounts to 20% of all property investment worldwide (Hobbs
et al. 2007). The highest fraction of cross-border activity takes place within Europe,
where cross-border investment was US$ 84 billion in 2006. European investors
mainly focus on cross-border investment within their continent, while investors from
America and Asia-Pacific are increasingly active across regions. Among cross-border
investors, the number of listed property companies following a global expansion
strategy is increasing. In addition, those listed property companies that are already
operating internationally are often further broadening their investment strategies.
Arguably, the increase in international real estate capital flows could foster
increasing demand for stronger institutions across global real estate markets. Indeed,
the real estate industry has experienced fundamental changes over the past decade.
The most recently published Real Estate Transparency Index of Jones Lang LaSalle
shows that real estate transparency around the world continues to improve. Nearly
one-half of the markets surveyed demonstrate an improvement in their composite
transparency scores between 2006 and 2008. The largest improvements were made
in previously unstable and intransparent countries like Romania, Ukraine, and Russia
(JLL 2008). Further sources of increased real estate market transparency are the
internationalization of real estate service providers, the rise of common investment
vehicles (Eichholtz and Kok 2007), and more sophisticated and mature performance
benchmarks, like the indices of the Investment Property Databank (IPD).
At the same time, political barriers have been gradually reduced. The
liberalization of capital markets in many countries has increased the economic and
political pressure to create financial instruments acceptable to foreign investors.
These pressures give rise to several forms of functional convergence of markets (La
Porta et al. 2000b). This means that foreigners are—at least in those converging real
estate markets—more likely to face investment conditions similar to locals.
It is likely that the increased transparency reduces the information asymmetry
problems that international real estate investors faced hitherto. Previous evidence on
the market implications of these problems documents that internationally diversified
property companies underperform local property companies by approximately three
percent per year, during the 1984–1995 period (Eichholtz et al. 2001). Reducing the
information disadvantages of international investors could imply that internationally
operating property companies have improved their performance compared to those
companies that operate locally.
This paper studies the performance and performance drivers of internationally
operating property companies relative to their local counterparts during the 1996–2007
period. We find that international property companies underperform both pure-play
P. Eichholtz et al.
local property companies and a synthetic benchmark with similar country weights in
the early years of this period. The underperformance is driven by the political
environment, the level of economic integration, and the transparency of target
countries’ real estate markets. The internationals’ underperformance disappears in the
later years of the sample period, and so does the significance of the aforementioned
factors in explaining performance differences among international companies. We
explain these findings by the increased global transparency of the real estate industry as
a whole, which has leveled the playing field for foreign property investors.
The remainder of this paper is structured as follows. Section Literature Review:
Cross-border Real Estate Investments reviews the literature regarding motives and
obstacles to cross-border property investments. Section Data provides information
on the dataset and descriptive statistics. Section The Performance of Internationals
versus Locals maps the performance difference between internationals and locally
investing property companies, and Section Performance Drivers investigates the
drivers of the performance differences. The last section concludes the paper and
provides implications and limitations.
Literature Review: Cross-border Real Estate Investments
Over the past two decades, a wide range of studies has addressed the diversification
potential of international real estate investments. An excellent review of that
literature can be found in Worzala and Sirmans (2003a, b). Overall, the empirical
evidence indicates that there are significant diversification benefits in global property
investments. Cross-country correlations are found to be low, especially among
markets from different continents (Eichholtz 1996; Eichholtz et al. 1998), although
the results depend on the treatment of currency risk and the time period studied.
Studies of international diversification effects within mixed-asset portfolios, like
Quan and Titman (1997) and Hoesli et al. (2004) provide evidence that there are gains
from international diversification in direct property investments. Liu and Mei (1998)
and Conover et al. (2002) confirm these results for indirect property investments.
Diversification benefits have also been documented when the analysis is limited
to the effects within real estate portfolios. Worzala and Sirmans (2003b) and
Eichholtz (1996, 1997) find strong benefits of international diversification. Several
studies have investigated whether country-specific, continental, or worldwide factors
drive international real estate returns. Eichholtz et al. (1998) report evidence of
continental factors in Europe and in North America, while there is no dominant
continental factor in the Asia-Pacific region. The authors therefore conclude that
European and American real estate investors should diversify across continents. Ling
and Naranjo (2002) find evidence of a worldwide factor in global real estate returns.
However, even after controlling for worldwide systematic risk, country-specific
factors are still highly significant, suggesting international diversification benefits.
There are some exceptions to the consensus on the benefits of international
diversification of property investments. Ziobrowski and Curcio (1991) do not detect
significant diversification gains, and a study by Goetzmann and Wachter (1995)
shows that the 1992 downturn in rents and property values in the U.S. was
experienced by 90% of the international property markets.
Transparency, Integration and Cost of International Real Estate Investments
The benefits of international real estate investments notwithstanding, there are
also costs and risks involved. However, while several studies investigate the risks of
venturing in real estate markets overseas (Geurts and Jaffe 1996; Liao and Mei
1999), studies that address how these risks and costs affect the returns of foreign real
estate investments are scant. Eichholtz et al. (2001) compare the performance of 18
internationally operating property companies (“internationals”) with the performance
of property companies focusing on their local market (“locals”) during the 1984–
1995 period. The results show that internationals underperform locals by 2.7% per
year (on a risk-adjusted basis) and that this underperformance is consistent over
time. The authors show that the results are not driven by transaction costs, leverage,
or currency effects, and attribute the performance difference to informational
disadvantages. However, they do not formally address this hypothesis. The paper
also shows that international property companies can partly overcome informational
disadvantages as they grow in size.
A more recent study by Edelstein et al. (2009) shows that the risk premia for
listed property shares are partly determined by the quality of a country’s legal system
and corporate governance environment, after controlling for country-specific macro
variables and firm-level characteristics like size and capital structure. The notion that
information costs are a main performance driver of internationally diversified
property companies is supported by surveys among institutional investors. These
surveys often identify the lack of local knowledge as an impediment to cross-border
investments, as well as other obstacles, such as political risk, currency risk,
transaction costs, and liquidity issues (Dhar and Goetzmann 2006; Newell and
Worzala 1995, 1997; Worzala 1994). In addition, going international is associated
with a loss of corporate focus that could likewise have an impact on the performance
of international property companies (Boer et al. 2005; Capozza and Seguin 1999).
We will discuss these issues in the remainder of this section.
A critical issue concerning cross-border property investments is foreign investors’
lack of local expertise and the limited access to local market information. This issue is
not specific to the real estate sector, and is well-discussed in the more general finance
literature (Adler and Dumas 1975; Armstrong and Riddick 1998). As private real estate
markets are unlikely to be as informationally efficient as public equity markets, prices
do not necessarily and instantaneously reflect all available and relevant information.
Local investors, who have superior information, can therefore have a competitive
advantage. At the same time, given that foreign investors are often less well informed,
with less local expertise, they will likely pay too much for their assets and buy more
lemons (Eichholtz et al. 2001). This leads to returns that are likely to be lower than
those of their local counterparts. One potential solution to these informational
disadvantages is to create a stronger local presence. Eichholtz et al. (2001) find that
the underperformance of internationals is reduced for larger firms. They conclude that an
increase in asset base gives foreign investors the possibility to overcome informational
disadvantages by realizing economies of scale in the generation of information.
Regarding political uncertainty, there is consensus in the literature that
internationally operating firms face greater risk than local firms due to increased
political risk exposure. For the real estate sector, Liao and Mei (1999) document a
positive relationship between property returns and a country’s economic efficiency
and economic freedom. Other discriminatory factors range from taxation differences
P. Eichholtz et al.
(Eichholtz and Kok 2007), which may also be found in North-American and
European markets, to planning and building codes that are inconsistently applied to
foreigners. The most extreme risk is the outright expropriation of property by the
government, with no or inadequate compensation. According to a real estate
transparency study by Jones Lang LaSalle (2008), this risk is for example present in
some more exotic markets, like Venezuela and Panama.
Benefits and return opportunities following international diversification are likely
to exist. However, these benefits may as well be obtained by end-investors through
investing in foreign property companies that have a local focus. Intermediate
investors, like listed property companies, should therefore only invest abroad if they
are able to operate in foreign markets at least as efficiently as locals.
Transaction costs, though varying across countries, are not relevant for the
property company’s decision to go abroad. Local companies are likely to face similar
fees and consequently there is no way of escaping these costs for an end-investor
who wants to gain exposure to an international real estate market. Currency risk will
also be unable to explain consistent differences in the risk-adjusted returns of
internationals and locals, as currency movements will similarly enhance or diminish
their returns from a foreign investor’s point of view.
Thus, additional problems that property companies are likely to face in operating
abroad are mainly: higher political risks, increased liquidity problems, informational
disadvantages, and loss of corporate focus. These factors may consistently diminish
the returns to foreign investors.
Data
To investigate the performance of internationally diversified property companies, we
use a sample of 848 property companies from 36 countries around the globe, from
1996 through 2007. The sample is obtained by a combination of two data sources.
First, we use the Global Property Research (GPR) Handbook Database. GPR has
collected information on more than 700 listed property companies in all continents
on a monthly basis since 1984. The database is survivorship-bias free. We include
both property investment companies and hybrids between investors and developers.
Second, we use the Worldscope database, from which we select all companies either
classified as “real estate holding and development company” or “real estate
investment trust” (REIT). Companies are only included in the sample if property
investing is their principal activity. In the merged dataset, companies for which no
financial data is available are excluded, as well as open-ended real estate funds, as
the performance of the latter is artificially smooth.
The remaining companies are classified as “internationals” or “locals”. In years in
which property companies invest at least 25% of their portfolio outside the home
market, companies are considered to be international.
1
We exclude international
1
The home market is the country where the company has its main stock market listing unless it has more
than 75% invested in an other market, which is then considered to be the home market. The cut-off point is
similar to Eichholtz et al. (2001) to ensure comparability of the results. Although the cut-off point is
somewhat arbitrarily chosen, using different cut-off points does not significantly change the results.
Transparency, Integration and Cost of International Real Estate Investments
Hong Kong property companies investing in mainland China, as we consider Hong
Kong and China to be a single national market. This leads to a sample of 67 property
companies that are considered as international for at least 1 year and 465 property
companies that can be classified as locals.
2
Our sample of internationals is
substantially larger than the sample of Eichholtz et al. (2001), who use a sample
of 18 internationals out of a total universe of 360 property companies (in 1996).
Table 1 provides an overview of the international property companies, including
sample statistics and basic information on portfolio composition and size. About half
of the internationals are European companies, and within Europe, most internationals
originate from the Netherlands and the United Kingdom, which both have a long
tradition of investing internationally. This may be due to the large amount of
institutional money on relatively small home markets. Asian property companies
make up a substantial part of the sample as well, with internationals mainly based in
Singapore and Hong Kong. North American companies constitute only about 12%
of the internationals sample and most of the international property companies
originate from Canada rather than the United States. Columns 3, 4, 5, and 6 of
Table 1 provide average returns, standard deviations, the first month for which an
observation is available and the total number of months for which we have data.
Asian property companies have on average the highest returns, but these come with
a high volatility.
Columns 7 through 11 of Table 1 provide information on the portfolio
composition of the internationals as of December 31, 2007, or the last year when
the company was considered to be an international. The data shows that most
companies are mainly active on their own continent, and focus on a limited number
of markets. The most diversified, in terms of countries covered, are the European
internationals with investments in on average 3.91 countries. Prominent examples of
geographically diversified firms in Europe are the French shopping center investor
Klepierre, Germany’s IVG Immobilien, and Wereldhave of the Netherlands. The
least diversified internationals originate from North America, which are mainly
Canadian property companies operating in the United States. An exception is the
US-based Shurgard Storage Centers, a self-storage REIT operating in eight countries
across North America and Europe.
3
While the European and the North American companies tend to invest within
their continents, Asian and especially Australian property companies are active
across regions. Examples of Australian listed property trusts with an international
focus are the Macquarie Countrywide Trust and the Centro Retail Trust, both very
active in the United States. The most diversified company in the sample is the
Singaporean Ascott Group Limited, a serviced residence owner and operator with
properties in Asia-Pacific, Europe, and the Gulf region.
Finally, Table 1 provides information on market capitalization of internationally
oriented property companies. On average, the market capitalization of internationals
is over US$2 billion, with the distribution skewed to the right. The firms with the
2
Property companies can be classified as local or international interchangeably during the sample period,
depending on the country allocation in a given year.
3
Shurgard Storage Centers was acquired in August 2006 and is now part of the U.S. company Public
Storage.
P. Eichholtz et al.
highest market capitalization originate from the Australian continent. This result is
mostly driven by the Westfield Group, an Australian shopping center operator, with
a market capitalization of more than US$ 27 billion.
The Performance of Internationals Versus Locals
Our empirical setup initially follows the method proposed by Eichholtz et al. (2001).
We first construct a value-weighted index solely consisting of international property
companies—the “internationals” index—and a value-weighted index solely consist-
ing of local property companies—the “locals” index. Then, we construct customized
benchmarks for each property company that is classified as international. To set up
these benchmarks, value-weighted return indices are calculated for each country,
only consisting of firms that focus on the local market. These country indices are
then combined to reflect the country allocation of each international property
company.
4
The benchmark is rebalanced yearly, to reflect changes in the
geographical asset allocation of the international. An international’s customized
benchmark contains local property companies only and has the same combined
country allocation as the particular international property company.
Comparing the internationals index with the locals index compares two investment
portfolios that not only differ in investment strategy, but also in terms of geographical
asset allocation. However, the customized benchmark compares two investment
portfolios that only differ in the way how they are set up: through direct foreign
property investments by an international property company or through indirect foreign
property investments in locally operating property companies. These represent the two
alternative methods for an end-investor to build up international real estate exposure.
To assess the performance of the internationals relative to their benchmarks,
monthly performance data for all 848 property companies is retrieved from
Datastream for the period from January 1996 through December 2007. All indices
are retrieved in U.S. dollars to ensure comparability. The total return indices of the
local property companies are then combined into a value-weighted locals index.
Second, the total return indices of local companies are the basis for a set of country
indices. Companies are only included in a country index in years where they actually
meet the criteria to be considered as locals in that country. The weights of the locals
in the country indices are determined by their respective market capitalization:
CI
i;t
¼ CI
i;tÀ1
»
1 þ
P
j¼nt
j¼0
R
j;t
»
CAP
j;tÀ1
À Á
!
P
j¼nt
j¼0
CAP
j;tÀ1
!
2
6
6
6
6
4
3
7
7
7
7
5
ð1Þ
4
The country allocations are derived from asset or sales data in the annual reports, whereby the former
was preferred over latter. While sales are a summary figure of what happened during the year, assets
provide a snapshot of the company at the end of the fiscal year. The country allocations derived from
assets are used for the half year periods before and after the fiscal year end date. Country allocations
derived from sales apply to the one year period before the fiscal year end date.
Transparency, Integration and Cost of International Real Estate Investments
T
a
b
l
e
1
D
e
s
c
r
i
p
t
i
v
e
s
t
a
t
i
s
t
i
c
s
o
f
i
n
t
e
r
n
a
t
i
o
n
a
l
c
o
m
p
a
n
i
e
s
p
e
r
f
o
r
m
a
n
c
e
a
n
d
p
o
r
t
f
o
l
i
o
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
C
o
m
p
a
n
y
C
o
u
n
t
r
y
R
e
t
u
r
n
S
t
.
D
e
v
.
F
i
r
s
t
M
o
n
t
h
n
P
o
r
t
f
o
l
i
o
M
i
x
M
k
t
.
C
a
p
U
S
$
m
l
n
.
A
s
i
a
A
u
s
t
r
a
l
i
a
E
u
r
o
p
e
N
o
r
t
h
A
m
e
r
i
c
a
#
C
o
u
n
t
r
i
e
s
A
s
i
a
C
h
i
n
a
M
o
t
o
r
B
u
s
C
o
m
p
a
n
y
H
o
n
g
K
o
n
g
1
.
2
4
4
.
8
8
j
u
l
-
1
9
9
9
7
2
6
7
.
6
0
.
0
3
2
.
4
0
.
0
2
3
8
7
F
a
r
E
a
s
t
C
o
n
s
o
r
t
i
u
m
I
n
t
.
H
o
n
g
K
o
n
g
4
.
9
8
3
2
.
8
5
a
p
r
-
1
9
9
9
6
0
8
9
.
2
9
.
0
0
.
0
1
.
8
6
2
6
7
H
o
n
K
w
o
k
L
a
n
d
I
n
v
e
s
t
m
e
n
t
H
o
n
g
K
o
n
g
-
0
.
0
6
2
1
.
5
3
j
u
l
-
1
9
9
6
2
1
7
7
.
0
0
.
0
0
.
0
2
3
.
0
3
1
8
9
W
h
e
e
l
o
c
k
A
n
d
C
o
m
p
a
n
y
H
o
n
g
K
o
n
g
3
.
0
3
1
1
.
1
6
a
p
r
-
1
9
9
9
6
0
1
0
0
.
0
0
.
0
0
.
0
0
.
0
2
5
,
3
2
9
W
h
e
e
l
o
c
k
P
r
o
p
e
r
t
i
e
s
H
o
n
g
K
o
n
g
2
.
7
3
8
.
5
1
a
p
r
-
2
0
0
0
8
4
1
0
0
.
0
0
.
0
0
.
0
0
.
0
2
2
,
1
7
7
L
a
n
d
&
G
e
n
e
r
a
l
M
a
l
a
y
s
i
a
1
.
2
3
1
4
.
8
6
o
k
t
-
2
0
0
2
2
4
7
1
.
3
2
8
.
7
0
.
0
0
.
0
2
2
3
M
u
l
p
h
a
I
n
t
e
r
n
a
t
i
o
n
a
l
M
a
l
a
y
s
i
a
1
.
1
4
1
8
.
5
1
j
u
l
-
1
9
9
6
1
3
2
3
5
.
5
6
4
.
5
0
.
0
0
.
0
5
7
0
2
A
l
l
c
o
C
o
m
m
e
r
c
i
a
l
R
E
I
T
S
i
n
g
a
p
o
r
e
4
.
8
3
6
.
6
2
j
u
l
-
2
0
0
6
1
2
5
8
.
8
4
1
.
2
0
.
0
0
.
0
2
4
1
3
A
s
c
o
t
t
G
r
o
u
p
S
i
n
g
a
p
o
r
e
2
.
9
8
1
0
.
8
7
j
u
l
-
2
0
0
0
8
4
3
4
.
9
1
4
.
0
5
1
.
2
0
.
0
1
0
3
2
5
A
s
c
o
t
t
R
e
s
i
d
e
n
c
e
T
r
u
s
t
M
n
g
t
.
S
i
n
g
a
p
o
r
e
4
.
7
3
6
.
3
6
a
p
r
-
2
0
0
6
1
5
1
0
0
.
0
0
.
0
0
.
0
0
.
0
4
8
3
5
C
a
p
i
t
a
l
a
n
d
S
i
n
g
a
p
o
r
e
-
1
.
1
5
1
2
.
6
2
j
u
l
-
1
9
9
9
3
6
8
7
.
9
9
.
1
3
.
0
0
.
0
6
1
2
,
1
5
6
G
u
o
c
o
l
a
n
d
S
i
n
g
a
p
o
r
e
4
.
7
4
1
0
.
5
0
j
a
n
-
2
0
0
3
5
4
1
0
0
.
0
0
.
0
0
.
0
0
.
0
3
2
,
1
5
0
H
o
B
e
e
I
n
v
e
s
t
m
e
n
t
S
i
n
g
a
p
o
r
e
-
2
.
4
1
1
0
.
8
8
j
u
l
-
2
0
0
0
1
2
6
7
.
5
0
.
0
3
2
.
5
0
.
0
2
8
0
H
o
t
e
l
R
o
y
a
l
S
i
n
g
a
p
o
r
e
1
.
3
0
1
2
.
4
2
j
a
n
-
1
9
9
6
1
3
2
7
2
.
1
2
7
.
9
0
.
0
0
.
0
4
1
1
5
L
e
e
K
i
m
T
a
h
H
o
l
d
i
n
g
s
S
i
n
g
a
p
o
r
e
1
.
7
2
1
1
.
3
7
j
a
n
-
1
9
9
6
9
6
7
4
.
6
2
5
.
4
0
.
0
0
.
0
2
1
0
2
M
a
p
l
e
t
r
e
e
L
o
g
i
s
t
i
c
s
T
r
u
s
t
S
i
n
g
a
p
o
r
e
4
.
4
4
5
.
6
8
j
u
l
-
2
0
0
6
1
2
1
0
0
.
0
0
.
0
0
.
0
0
.
0
4
1
,
0
5
1
A
v
e
r
a
g
e
2
.
2
2
1
2
.
4
8
7
7
.
3
1
3
.
7
7
.
4
1
.
6
3
.
7
1
,
6
4
4
A
u
s
t
r
a
l
i
a
/
N
e
w
Z
e
a
l
a
n
d
C
e
n
t
r
o
P
r
o
p
e
r
t
i
e
s
G
r
o
u
p
A
u
s
t
r
a
l
i
a
2
.
5
5
5
.
5
6
j
a
n
-
2
0
0
5
2
4
0
.
0
7
0
.
0
0
.
0
3
0
.
0
2
5
,
5
6
5
C
e
n
t
r
o
R
e
t
a
i
l
G
r
o
u
p
A
u
s
t
r
a
l
i
a
-
0
.
2
2
9
.
5
3
j
a
n
-
2
0
0
6
2
4
0
.
0
5
7
.
4
0
.
0
4
2
.
6
2
2
,
8
5
1
M
a
c
q
u
a
r
i
e
C
o
u
n
t
r
y
w
i
d
e
T
r
u
s
t
A
u
s
t
r
a
l
i
a
1
.
9
4
4
.
7
5
j
a
n
-
2
0
0
3
5
4
0
.
0
2
7
.
0
1
1
.
0
6
2
.
0
5
2
,
4
3
8
M
a
c
q
u
a
r
i
e
O
f
f
i
c
e
T
r
u
s
t
A
u
s
t
r
a
l
i
a
1
.
8
0
3
.
3
2
j
a
n
-
2
0
0
5
3
0
0
.
0
3
9
.
0
7
.
0
5
4
.
0
3
2
,
7
9
6
M
a
c
q
u
a
r
i
e
P
r
o
l
o
g
i
s
T
r
u
s
t
A
u
s
t
r
a
l
i
a
1
.
3
5
3
.
8
0
j
u
l
-
2
0
0
3
4
2
0
.
0
3
.
7
0
.
0
9
6
.
3
3
8
7
6
P. Eichholtz et al.
(
)
C
o
m
p
a
n
y
C
o
u
n
t
r
y
R
e
t
u
r
n
S
t
.
D
e
v
.
F
i
r
s
t
M
o
n
t
h
n
P
o
r
t
f
o
l
i
o
M
i
x
M
k
t
.
C
a
p
U
S
$
m
l
n
.
A
s
i
a
A
u
s
t
r
a
l
i
a
E
u
r
o
p
e
N
o
r
t
h
A
m
e
r
i
c
a
#
C
o
u
n
t
r
i
e
s
W
e
s
t
f
i
e
l
d
G
r
o
u
p
A
u
s
t
r
a
l
i
a
1
.
6
8
4
.
1
1
j
a
n
-
2
0
0
4
3
6
0
.
0
4
7
.
3
1
0
.
0
4
2
.
7
4
2
7
,
0
7
0
I
N
G
M
e
d
i
c
a
l
P
r
o
p
e
r
t
i
e
s
T
r
u
s
t
N
e
w
Z
e
a
l
a
n
d
2
.
2
1
5
.
9
8
j
a
n
-
2
0
0
3
6
0
0
.
0
1
0
0
.
0
0
.
0
0
.
0
2
1
3
2
A
v
e
r
a
g
e
1
.
6
2
5
.
2
9
0
.
0
4
9
.
2
4
.
0
4
6
.
8
3
.
0
5
,
9
6
1
C
o
n
t
i
n
e
n
t
a
l
E
u
r
o
p
e
E
c
o
B
u
s
i
n
e
s
s
-
I
m
m
o
b
i
l
i
e
n
A
G
A
u
s
t
r
i
a
-
0
.
0
2
6
.
4
8
j
u
l
-
2
0
0
6
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
4
5
4
A
l
t
a
r
e
a
F
r
a
n
c
e
2
.
3
1
5
.
2
4
j
u
l
-
2
0
0
5
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
1
,
1
6
7
B
a
i
l
I
n
v
e
s
t
i
s
s
e
m
e
n
t
S
A
F
r
a
n
c
e
0
.
2
0
4
.
0
3
j
u
l
-
1
9
9
9
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
3
6
3
F
D
L
F
r
a
n
c
e
4
.
3
6
1
7
.
0
5
j
u
l
-
2
0
0
6
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
1
,
8
8
3
K
l
e
p
i
e
r
r
e
F
r
a
n
c
e
3
.
4
8
6
.
3
1
j
a
n
-
2
0
0
3
4
8
0
.
0
0
.
0
1
0
0
.
0
0
.
0
8
7
,
4
9
6
A
I
G
I
n
t
e
r
n
a
t
i
o
n
a
l
R
e
a
l
E
s
t
a
t
e
A
G
G
e
r
m
a
n
y
-
1
.
4
5
4
.
7
8
a
u
g
-
2
0
0
2
1
2
6
1
.
6
0
.
0
2
0
.
2
1
8
.
2
4
1
0
2
I
V
G
I
m
m
o
b
i
l
i
n
A
G
G
e
r
m
a
n
y
1
.
8
3
9
.
1
0
j
u
l
-
1
9
9
9
8
4
0
.
0
0
.
0
1
0
0
.
0
0
.
0
7
5
,
0
2
9
S
t
o
d
i
e
k
E
u
r
o
p
a
I
m
m
o
b
i
l
i
e
n
A
G
G
e
r
m
a
n
y
1
.
1
4
1
0
.
8
8
j
u
l
-
1
9
9
9
9
0
0
.
0
0
.
0
1
0
0
.
0
0
.
0
7
2
0
3
R
i
s
a
n
a
m
e
n
t
o
S
p
a
I
t
a
l
y
4
.
0
5
1
2
.
0
6
j
u
l
-
2
0
0
5
2
4
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
2
,
4
6
6
C
o
r
i
o
N
V
N
e
t
h
e
r
l
a
n
d
s
1
.
6
4
4
.
7
1
j
u
l
-
1
9
9
7
1
1
4
4
.
0
0
.
0
9
6
.
0
0
.
0
5
5
,
0
0
5
E
u
r
o
c
o
m
m
e
r
c
i
a
l
P
r
o
p
e
r
t
y
N
e
t
h
e
r
l
a
n
d
s
1
.
4
0
4
.
5
7
j
a
n
-
1
9
9
7
1
2
6
0
.
0
0
.
0
1
0
0
.
0
0
.
0
4
2
,
0
6
7
R
o
d
a
m
c
o
A
s
i
a
N
V
N
e
t
h
e
r
l
a
n
d
s
1
.
3
4
7
.
8
2
a
u
g
-
1
9
9
9
6
1
1
0
0
.
0
0
.
0
0
.
0
0
.
0
7
5
0
0
R
o
d
a
m
c
o
E
u
r
o
p
e
N
e
t
h
e
r
l
a
n
d
s
1
.
8
5
5
.
7
0
a
u
g
-
1
9
9
9
8
9
0
.
0
0
.
0
1
0
0
.
0
0
.
0
6
1
0
,
8
3
0
V
a
s
t
n
e
d
O
f
f
i
c
e
s
I
n
d
u
s
t
r
i
a
l
N
e
t
h
e
r
l
a
n
d
s
1
.
7
0
4
.
7
1
j
u
l
-
2
0
0
1
7
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
3
8
8
6
V
a
s
t
n
e
d
R
e
t
a
i
l
N
V
N
e
t
h
e
r
l
a
n
d
s
1
.
3
5
4
.
5
8
j
u
l
-
1
9
9
8
1
0
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
6
1
,
5
1
2
W
e
r
e
l
d
h
a
v
e
N
V
N
e
t
h
e
r
l
a
n
d
s
1
.
4
2
5
.
0
9
j
u
l
-
1
9
9
6
1
2
6
0
.
0
0
.
0
7
7
.
0
2
3
.
0
8
2
,
5
4
7
N
o
r
g
a
n
i
H
o
t
e
l
s
N
o
r
w
a
y
2
.
3
1
9
.
8
9
j
u
l
-
2
0
0
6
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
4
5
0
5
I
n
m
o
b
i
l
i
a
r
i
a
C
o
l
o
n
i
a
l
S
A
S
p
a
i
n
5
.
8
7
1
8
.
5
1
j
u
l
-
2
0
0
6
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
7
,
8
2
3
M
e
t
r
o
v
a
c
e
s
a
S
A
S
p
a
i
n
3
.
6
1
1
1
.
4
5
j
u
l
-
2
0
0
5
2
4
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
2
,
5
8
4
C
o
l
u
m
n
a
F
a
s
t
i
g
h
e
t
e
r
S
w
e
d
e
n
1
.
2
6
2
3
.
8
9
j
u
l
-
1
9
9
9
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
3
4
2
H
o
m
e
P
r
o
p
e
r
t
i
e
s
A
B
S
w
e
d
e
n
2
.
8
4
4
.
4
4
j
u
l
-
1
9
9
9
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
4
9
8
P
a
n
d
o
x
A
B
S
w
e
d
e
n
1
.
3
7
8
.
3
7
j
u
l
-
2
0
0
1
2
4
0
.
0
0
.
0
1
0
0
.
0
0
.
0
6
2
0
0
R
e
a
l
i
a
A
B
S
w
e
d
e
n
2
.
1
9
1
0
.
8
2
j
u
l
-
1
9
9
9
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
3
4
6
I
n
t
e
r
s
h
o
p
H
o
l
d
i
n
g
A
G
S
w
i
t
z
e
r
l
a
n
d
1
.
4
2
4
.
0
1
j
u
l
-
1
9
9
7
2
4
0
.
0
0
.
0
6
7
.
7
3
2
.
3
3
5
1
0
Z
u
e
b
l
i
n
I
m
m
o
b
i
l
i
e
n
H
o
l
d
i
n
g
s
A
G
S
w
i
t
z
e
r
l
a
n
d
1
.
2
6
4
.
0
6
o
k
t
-
2
0
0
1
6
9
0
.
0
0
.
0
1
0
0
.
0
0
.
0
5
4
3
6
A
v
e
r
a
g
e
1
.
9
5
8
.
3
4
6
.
6
0
.
0
9
0
.
4
2
.
9
4
.
3
1
7
6
Transparency, Integration and Cost of International Real Estate Investments
T
a
b
l
e
1
(
c
o
n
t
i
n
u
e
d
)
C
o
m
p
a
n
y
C
o
u
n
t
r
y
R
e
t
u
r
n
S
t
.
D
e
v
.
F
i
r
s
t
M
o
n
t
h
n
P
o
r
t
f
o
l
i
o
M
i
x
M
k
t
.
C
a
p
U
S
$
m
l
n
.
A
s
i
a
A
u
s
t
r
a
l
i
a
E
u
r
o
p
e
N
o
r
t
h
A
m
e
r
i
c
a
#
C
o
u
n
t
r
i
e
s
U
n
i
t
e
d
K
i
n
g
d
o
m
C
a
p
i
t
a
l
&
R
e
g
i
o
n
a
l
P
L
C
U
K
2
.
4
3
9
.
4
3
j
u
l
-
2
0
0
6
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
2
1
,
9
8
3
C
h
i
n
a
W
e
s
t
e
r
n
I
n
v
e
s
t
m
e
n
t
s
P
L
C
U
K
-
0
.
7
6
2
5
.
5
7
o
k
t
-
2
0
0
4
2
4
4
7
.
3
0
.
0
5
2
.
7
0
.
0
2
2
1
C
L
S
H
o
l
d
i
n
g
s
P
L
C
U
K
2
.
0
0
6
.
6
0
j
u
l
-
2
0
0
0
8
4
0
.
0
0
.
0
1
0
0
.
0
0
.
0
4
1
,
0
7
0
H
a
m
m
e
r
s
o
n
P
L
C
U
K
1
.
7
2
6
.
7
9
j
u
l
-
1
9
9
6
1
3
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
3
9
,
1
4
6
H
a
n
s
t
e
e
n
H
o
l
d
i
n
g
s
P
L
C
U
K
2
.
1
8
5
.
0
6
j
u
l
-
2
0
0
6
1
2
0
.
0
0
.
0
1
0
0
.
0
0
.
0
4
5
3
3
L
o
n
d
o
n
M
e
r
c
h
a
n
t
S
e
c
u
r
i
t
i
e
s
P
L
C
U
K
0
.
7
1
5
.
1
4
o
k
t
-
1
9
9
7
1
2
0
.
0
0
.
0
5
7
.
0
4
3
.
0
2
4
1
0
P
r
o
b
u
s
E
s
t
a
t
e
s
P
L
C
U
K
-
3
.
0
0
1
7
.
6
5
j
a
n
-
1
9
9
8
3
6
0
.
0
0
.
0
1
0
0
.
0
0
.
0
3
1
9
S
e
g
r
o
P
L
C
U
K
1
.
3
4
5
.
8
9
j
u
l
-
2
0
0
2
3
6
0
.
0
0
.
0
8
0
.
0
2
0
.
0
2
6
,
6
8
5
A
v
e
r
a
g
e
0
.
8
3
1
0
.
2
7
5
.
9
0
.
0
8
6
.
2
7
.
9
2
.
8
1
2
5
.
2
4
N
o
r
t
h
A
m
e
r
i
c
a
B
r
o
o
k
f
i
e
l
d
P
r
o
p
e
r
t
i
e
s
C
o
r
p
.
C
a
n
a
d
a
2
.
0
1
8
.
0
6
j
u
l
-
1
9
9
7
8
4
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
6
,
5
5
7
E
x
t
e
n
d
i
c
a
r
e
I
N
C
C
a
n
a
d
a
2
.
2
1
7
.
7
3
j
u
l
-
1
9
9
6
3
6
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
9
8
3
F
i
r
s
t
C
a
p
i
t
a
l
R
e
a
l
t
y
I
N
C
C
a
n
a
d
a
0
.
8
8
7
.
4
9
j
u
l
-
1
9
9
6
6
6
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
1
1
7
H
&
R
R
E
I
T
C
a
n
a
d
a
1
.
9
0
4
.
9
2
j
u
l
-
2
0
0
5
2
4
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
3
,
0
2
7
R
e
v
e
n
u
e
P
r
o
p
e
r
t
i
e
s
C
o
m
p
a
n
y
C
a
n
a
d
a
0
.
9
1
8
.
4
7
j
u
l
-
1
9
9
6
4
8
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
1
8
7
S
t
e
r
l
i
n
g
C
e
n
t
r
e
c
o
r
p
I
N
C
C
a
n
a
d
a
2
.
3
7
1
1
.
7
6
j
u
l
-
2
0
0
1
3
6
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
4
0
T
G
S
N
o
r
t
h
A
m
e
r
i
c
a
n
R
E
I
T
C
a
n
a
d
a
0
.
5
1
8
.
7
4
j
u
l
-
2
0
0
4
1
2
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
1
8
1
T
r
i
z
e
c
C
a
n
a
d
a
I
N
C
C
a
n
a
d
a
-
0
.
1
5
1
.
5
6
j
a
n
-
1
9
9
7
3
6
0
.
0
0
.
0
0
.
0
1
0
0
.
0
2
3
8
2
S
h
u
r
g
a
r
d
S
t
o
r
a
g
e
C
e
n
t
e
r
s
I
N
C
U
S
A
2
.
7
2
5
.
8
2
j
u
l
-
2
0
0
4
2
4
0
.
0
0
.
0
2
6
.
0
7
4
.
0
8
2
,
7
4
5
A
v
e
r
a
g
e
1
.
4
8
7
.
1
7
0
.
0
0
.
0
2
.
9
9
7
.
1
2
.
7
1
,
5
7
9
P. Eichholtz et al.
where CI
i,t
refers to the country index for country i in month t, R
j,t
is the total return
of the local company j in month t, CAP
j,t
refers to the market capitalization of local
company j in month t, nt is total number of companies that are local in country i in
month t and CI
i,t=0
is 100. Based on the returns of the country indices, we derive the
customized benchmark for each international property company. We weigh the
individual country allocations to mimic the country allocation of that particular
international. We create a customized benchmark for each international company k
as follows:
CB
k;t
¼ CB
k;tÀ1
»
1 þ
X
i¼nk
i¼0
w
k;t
»
R
i;t
À Á
" #
ð2Þ
where CB
k,t
is the customized benchmark for the international company k in month t,
w
k,t
represents the percentage invested in country i in month t by the international k,
R
i,t
is the return of the country index of country i, nk the total number of countries in
which the international k has invested in month t.
Figure 1 presents the value-weighted index consisting of the international
property companies, the value-weighted index consisting of local property
companies, and the market-weighted customized benchmark of the international
companies. All indices are set at 100 in June 1996 and track the performance until
the end of June 2007.
5
The graph suggests that, over this eleven year period, local
property companies have outperformed their international counterparts. While the
internationals index increases from 100 in 1995 to 449 at the end of June 2007, the
locals index increases from 100 to 515 over the same period. These increases
correspond to average annual returns of 13.7% and 15.8%, respectively. This
difference may first of all be due to an information effect hindering performance of
internationals, but it is important to note that the performance difference can also be
partially attributed to an allocation effect, as the composition of the locals index may
differ from the geographical asset allocation of international property companies.
The customized benchmark addresses this allocation effect by mimicking the
portfolio composition of international property companies. However, internationals
still underperform relative to the customized benchmark, with an annual perfor-
mance difference of 2.7%. This corresponds exactly with the 2.7% documented
earlier by Eichholtz et al. (2001), over the period from 1984 through 1995. The
performance difference between internationals and the customized benchmark is
larger than the performance difference between internationals and the locals index.
This may be due to the bias of internationals towards American and European
property markets. Since Asian property companies underperformed in the early part
of the sample, this allocation effect compensates some of the information effect.
These first descriptives suggest that international diversification remains costly in
the global property market, even though markets have become more financially
integrated.
5
Although the sample period ranges from January 1996 to December 2007, this graph focuses on the
period from June 1996 to June 2007. The half-year periods at the beginning and end of the sample period
are excluded because of the limited availability of data for a number of international property companies
during these periods.
Transparency, Integration and Cost of International Real Estate Investments
To further assess whether the return differences between locals and interna-
tionals are indeed an indication for the costs of direct investments in foreign
property markets, we investigate the volatility of the three return series and
estimate risk-adjusted returns. To check the consistency of the performance dif-
ference between internationals and locals over time, we study both the full sample
period and the three sub-periods. Panel A of Table 2 provides the descriptive
statistics for the return indices. The table shows that the lower returns of the
international companies relative to the local index and the customized benchmark are
accompanied by a slightly higher level of risk. The annualized standard deviation of
the internationals, locals and customized benchmark return series are 14.8%, 13%
and 12.8%, respectively. This translates into the highest Sharpe ratio for the
mimicking index.
The results for the sub-periods show that, contrasting to Eichholtz et al. (2001),
the superior performance of locals is not consistent over time. While internationals
underperformed the customized benchmark by an annualized 7.7% in the first
period, their performance in the second and third sub-period is rather similar to the
stock returns of locals. Relative to the customized index, international property
companies even outperform in the second period. This is also reflected in the
Sharpe ratio of internationals, which is considerably lower for internationals than
for locals in the first period, whereas in the second and third period they are at a
comparable level.
The Sharpe ratio measures the excess return over the risk free rate per unit of total
risk, but to evaluate an international’s performance relative to the locals index or the
customized benchmark, we estimate the Jensen’s alpha as a risk-adjusted performance
measure. This enables us to first determine the out- or underperformance of an
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
0
100
200
300
400
500
600
700
Internationals Customized Benchmark Locals
Notes: Figure 1 shows the performance of the internationals index, a locals index, and the customized synthetic
benchmark. The locals index is a value-weighted index of the global universe of domestic property companies.
The customized benchmark represents the summation of all mimicking benchmarks, which are weighted
according to geographical asset allocation of the respective internationals.
Fig. 1 International Property Companies versus Benchmarks 1999–2007
P. Eichholtz et al.
T
a
b
l
e
2
P
e
r
f
o
r
m
a
n
c
e
o
f
i
n
t
e
r
n
a
t
i
o
n
a
l
s
v
e
r
s
u
s
a
l
o
c
a
l
s
p
r
o
p
e
r
t
y
i
n
d
e
x
a
n
d
a
c
u
s
t
o
m
i
z
e
d
b
e
n
c
h
m
a
r
k
.
S
h
a
r
p
e
R
a
t
i
o
a
n
d
J
e
n
s
e
n
’
s
A
l
p
h
a
P
a
n
e
l
A
.
P
e
r
f
o
r
m
a
n
c
e
l
o
c
a
l
s
v
e
r
s
u
s
i
n
t
e
r
n
a
t
i
o
n
a
l
s
F
u
l
l
p
e
r
i
o
d
1
9
9
6
–
2
0
0
0
2
0
0
0
–
2
0
0
4
2
0
0
4
–
2
0
0
7
M
e
a
n
(
%
)
S
t
.
D
e
v
.
(
%
)
S
h
a
r
p
e
r
a
t
i
o
M
e
a
n
(
%
)
S
t
.
D
e
v
.
(
%
)
S
h
a
r
p
e
M
e
a
n
(
%
)
S
t
.
D
e
v
.
(
%
)
S
h
a
r
p
e
r
a
t
i
o
M
e
a
n
(
%
)
S
t
.
D
e
v
.
(
%
)
S
h
a
r
p
e
I
n
t
e
r
n
a
t
i
o
n
a
l
s
i
n
d
e
x
1
3
.
6
6
1
4
.
7
8
0
.
2
7
?
0
.
7
3
1
5
.
2
0
?
0
.
0
1
1
4
.
9
4
1
6
.
3
4
0
.
2
6
2
6
.
7
7
1
1
.
6
1
0
.
6
6
L
o
c
a
l
s
i
n
d
e
x
1
5
.
8
4
1
2
.
9
8
0
.
3
5
3
.
9
1
1
5
.
5
0
0
.
0
7
1
7
.
7
4
1
0
.
5
7
0
.
4
8
2
8
.
1
9
1
1
.
7
8
0
.
7
2
C
u
s
t
o
m
i
z
e
d
b
e
n
c
h
m
a
r
k
i
n
d
e
x
1
6
.
4
0
1
2
.
8
4
0
.
3
7
7
.
0
0
1
2
.
2
6
0
.
1
6
1
4
.
0
7
1
3
.
9
1
0
.
2
9
2
8
.
1
4
1
1
.
7
7
0
.
6
9
P
a
n
e
l
B
.
J
e
n
s
e
n
’
s
A
l
p
h
a
—
d
o
m
e
s
t
i
c
s
’
i
n
d
e
x
?
(
m
o
n
t
h
l
y
i
n
%
)
t
-
s
t
a
t
?
t
-
s
t
a
t
n
R
²
-
a
d
j
.
F
u
l
l
p
e
r
i
o
d
?
0
.
0
5
(
?
0
.
1
9
)
0
.
8
2
(
1
1
.
9
0
)
*
*
*
1
3
2
0
.
5
2
1
9
9
6
–
2
0
0
0
?
0
.
3
0
(
?
0
.
6
4
)
0
.
7
2
(
6
.
9
1
)
*
*
*
4
4
0
.
5
2
2
0
0
0
–
2
0
0
3
?
0
.
3
5
(
?
0
.
6
2
)
1
.
0
8
(
6
.
3
4
)
*
*
*
4
4
0
.
4
8
2
0
0
4
–
2
0
0
7
0
.
6
6
(
1
.
6
7
)
*
0
.
7
3
(
7
.
3
7
)
*
*
*
4
4
0
.
5
5
P
a
n
e
l
C
.
J
e
n
s
e
n
’
s
A
l
p
h
a
—
c
u
s
t
o
m
i
z
e
d
b
e
n
c
h
m
a
r
k
i
n
d
e
x
?
(
m
o
n
t
h
l
y
i
n
%
)
t
-
s
t
a
t
?
t
-
s
t
a
t
n
R
²
-
a
d
j
.
F
u
l
l
p
e
r
i
o
d
?
0
.
2
2
?
1
.
1
3
1
.
0
1
2
0
.
7
1
*
*
*
1
3
2
0
.
7
7
1
9
9
6
–
2
0
0
0
?
0
.
6
2
?
1
.
7
2
*
1
.
0
5
1
0
.
1
3
*
*
*
4
4
0
.
7
1
2
0
0
0
–
2
0
0
3
0
.
0
7
0
.
2
1
1
.
0
3
1
2
.
4
3
*
*
*
4
4
0
.
7
9
2
0
0
4
–
2
0
0
7
0
.
1
2
0
.
4
5
0
.
8
9
1
3
.
2
1
*
*
*
4
4
0
.
8
1
P
a
n
e
l
A
o
f
T
a
b
l
e
2
p
r
o
v
i
d
e
s
a
n
n
u
a
l
i
z
e
d
r
e
t
u
r
n
s
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
f
o
r
t
h
e
f
u
l
l
s
a
m
p
l
e
p
e
r
i
o
d
a
n
d
t
h
r
e
e
s
u
b
-
p
e
r
i
o
d
s
o
f
4
4
m
o
n
t
h
s
.
T
h
e
S
h
a
r
p
e
r
a
t
i
o
i
s
c
a
l
c
u
l
a
t
e
d
o
n
a
m
o
n
t
h
l
y
b
a
s
i
s
a
n
d
r
e
p
r
e
s
e
n
t
s
t
h
e
e
x
c
e
s
s
r
e
t
u
r
n
o
f
t
h
e
i
n
d
e
x
o
v
e
r
t
h
e
r
i
s
k
f
r
e
e
r
a
t
e
d
i
v
i
d
e
d
b
y
t
h
e
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
.
P
a
n
e
l
B
r
e
p
o
r
t
s
t
h
e
r
e
s
u
l
t
s
o
f
t
h
e
J
e
n
s
e
n
’
s
a
l
p
h
a
e
s
t
i
m
a
t
i
o
n
w
i
t
h
t
h
e
i
n
t
e
r
n
a
t
i
o
n
a
l
s
i
n
d
e
x
r
e
g
r
e
s
s
e
d
o
n
t
h
e
d
o
m
e
s
t
i
c
s
’
i
n
d
e
x
a
s
t
h
e
b
e
n
c
h
m
a
r
k
.
P
a
n
e
l
C
r
e
p
o
r
t
s
t
h
e
r
e
s
u
l
t
s
o
f
t
h
e
J
e
n
s
e
n
’
s
a
l
p
h
a
e
s
t
i
m
a
t
i
o
n
w
i
t
h
t
h
e
i
n
t
e
r
n
a
t
i
o
n
a
l
s
’
i
n
d
e
x
r
e
g
r
e
s
s
e
d
o
n
t
h
e
c
u
s
t
o
m
i
z
e
d
d
o
m
e
s
t
i
c
s
’
b
e
n
c
h
m
a
r
k
.
*
*
*
i
n
d
i
c
a
t
e
s
s
i
g
n
i
f
i
c
a
n
c
e
a
t
t
h
e
1
%
l
e
v
e
l
;
*
*
i
n
d
i
c
a
t
e
s
s
i
g
n
i
f
i
c
a
n
c
e
a
t
t
h
e
5
%
l
e
v
e
l
,
a
n
d
*
i
n
d
i
c
a
t
e
s
s
i
g
n
i
f
i
c
a
n
c
e
a
t
t
h
e
1
0
%
l
e
v
e
l
Transparency, Integration and Cost of International Real Estate Investments
international property company relative to local property companies and thus to
eliminate any effects of an international’s country allocation decision.
R
kt
ÀR
ft
¼ a
k
þb
k
R
Bt
ÀR
ft
À Á
þ"
kt
ð3Þ
where R
kt
is the return of the international property company k at time t, R
ft
is the
risk free rate, R
Bt
the return of the locals index or the customized benchmark, and ?
kt
the error term. All returns are denominated in US$, the 1-month U.S. T-bill rate
serves as a proxy for R
ft
.
Panel B of Table 2 provides the results of Eq. (3), using the locals index as the
benchmark. Though statistically insignificant, alphas are consistently negative in
both the full period and the first two periods of the sample. However, international
property companies significantly outperform locally-oriented property companies by
an annualized 8% in the third period.
Panel C shows the results of Eq. (3), estimated with the customized benchmark to
control for allocation effects. For the full sample period, the alpha of ?0.22%
suggests that internationals also underperformed when adjusting for geographical
exposure. However, this result is not statistically significant. The estimated alphas of
the internationals index in the three sub-periods show that it is mainly the first part of
the sample period that drives the results. From 1996 to 2000, internationals show an
annualized risk-adjusted underperformance of 7.5%. This underperformance
disappears in the latter part of the sample period, and even becomes positive—
though not statistically significant.
Finally, to provide an insight into the development of internationals’ relative
performance over time, we estimate Jensen’s alpha for each international company on
an annual basis, based on the performance relative to the customized benchmark. The
resulting mean and median alphas for the individual years are presented in Fig. 2. This
graph also indicates that the performance of international companies relative to
locals has improved over time. While the mean and median alphas are generally
negative in the early years, they increase and even become positive in later years.
6
Performance Drivers
It has been documented that performance differences between property companies
that invest internationally and property companies that focus on their local market
may be explained by a size effect: larger internationals tend to perform relatively
well, possibly because of scale advantages relating to market information (Eichholtz
et al. 2001). In addition, we hypothesize that the performance of internationals may
be affected by: 1) political risk, 2) macroeconomic and market factors, and 3)
company-specific factors.
We expect that a vicious political environment in the target countries is negatively
related to the relative performance of internationals, as this may increase investment
risk and decrease operating efficiency (La Porta et al. 2000b, 2002). With respect to
macroeconomic and market factors, we expect that economic integration between
6
We note that these results have to be interpreted with caution, given that the alpha estimates are based on
annual periods.
P. Eichholtz et al.
countries levels the playing field for international investors. Consequently, interna-
tionals investing in markets that are economically integrated with their home market
may have higher alphas. In addition, differences in the risk-adjusted returns between
internationals and locals are potentially larger in opaque and less liquid property
markets. Regarding firm-specific factors, larger companies potentially benefit from
economies of scale, reducing the information costs in foreign markets and leading to
a higher alpha. Also, the extent to which international property companies are
geographically diversified might influence performance (Boer et al. 2005).
To assess the political environment of different countries, we use the Index of
Economic Freedom (IEF) published by the Heritage Foundation and The Wall Street
Journal. This index provides a measure on the level of economic freedom in countries
around the world and consists of ten sub-indices. We select the subindices “investment
freedom”, “property rights”, and “freedom from corruption”. Investment freedom
examines a country’s policies toward foreign investment and measures whether the
government treats foreign companies similar to local companies. The property rights
index reflects to what extent a country’s laws protect private property rights and also
assesses the risk of expropriation. The corruption index serves as a general indicator
for the strength of a country’s legal framework (Shleifer and Vishny 1993).
To construct a variable that reflects the political environment facing international
property companies, we combine the three indices into a single variable by taking
the equally weighted average of all three values in a given year and for a given
country.
7
Second, reported IEF scores are relative to the complete list of 165
countries covered by this index, of which our sample of 36 countries is a subset. We
7
In 2007, correlations between investment freedom, property rights and freedom from corruption for the
sampled countries range between 77% and 95%.
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
07 06 05 04 03 02 01 00 99 98 97 96
Median Mean
Notes: Figure 2 shows the mean and median performance of the
international property companies relative to their domestic mimicking
indices as measured by the monthly Jensen’s alpha. The Jensen’s
alpha has been estimated over a 12 month period.
Fig. 2 Individual Alphas of International Property Companies 1996–2007
Transparency, Integration and Cost of International Real Estate Investments
therefore calculate standardized values for the 36 countries in our sample for each
individual year. Third, we calculate a political risk index (PRI) for every international
company. The PRI is the average political risk of the foreign markets that the
international invests in during the period it is considered to be an international:
PRI
k
¼
1
yt
X
y¼yt
y¼0
X
i¼nk
i¼0
w
i;y;k
»
z
i;y
!
ð4Þ
where yt refers to the total number of years the company k is considered to be
international, nk is the total number of countries in which the company k has
invested in year y, w
i,y,k
refers to the proportion of foreign investment that company k
allocates to country i in year y and z
i,y
refers to the standardized freedom score for
country i in year y.
We measure the integration of financial markets by assessing the correlations
between national markets. To this end, we construct a variable that measures the
correlation between an international’s local market and the foreign markets that an
international invests in. For the former, the general market index of the international
company’s home market is used. The latter is represented by an index consisting of
the country indices of the foreign markets that the company invests in. These
country indices are weighted by the proportion of investments that the company
allocates to the individual countries. The weights are adjusted annually to reflect
changes in the company’s country allocation. In a final step, the correlation
(CORREL) between the local and foreign markets is calculated over the period when
the company is considered to be international.
As a proxy for transparency of local property markets, we use the Real Estate
Transparency Index, published by Jones Lang LaSalle (JLL). This measure is
strongly correlated with liquidity.
8
The Real Estate Transparency Index is based on a
structured survey which addresses five key attributes of real estate transparency:
availability of investment performance indices, availability of market fundamentals
data, financial disclosure and governance among listed vehicles, regulatory and legal
factors, and professional and ethnical standards. Based on these attributes, JLL
assigns transparency scores to individual countries ranging from 1 to 5, with 1
representing the highest level of transparency. We construct customized transparency
indices (TI) for each international company. The TI reflects the average transparency
of the foreign markets that an international invests in over the period when it is
considered as international:
TI
k
¼
1
yt
X
y¼yt
y¼0
X
i¼nk
i¼0
w
i;y;k
»
z
i;y
!
ð5Þ
where yt refers to the total number of years the company k is considered to be
international, nk is the total number of countries in which the company k has
invested in year y, w
i,y,k
refers to the proportion of foreign investment that company k
8
JLL (2008) show that liquidity—as measured by the share of global transaction volume relative to the
share of GDP—is highly correlated with transparency across countries. Including both liquidity and
transparency in the cross-sectional analysis would therefore also result in multicollinearity.
P. Eichholtz et al.
allocates to country i in year y and z
i,y
refers to the standardized transparency score
for country i in year y.
To take firm-specific performance drivers into account, we include three
variables. First, following Eichholtz et al. (2001), we include company size, proxied
by the natural logarithm of the average monthly market capitalization of a company
during the period it was international.
Second, we include geographical focus, proxied by the Herfindahl index, to
measure the extent to which a company is truly diversified (Boer et al. 2005;
Capozza and Seguin 1999):
HI
k
¼
1
yt
X
y¼yt
y¼0
X
i¼nk
i¼0
w
k;y
À Á
2
!
ð6Þ
where HI
k
is the average geographical Herfindahl index for company k, yt refers to
the total number of years company k is considered to be international, nk is the total
number of countries in which company k has invested in year y and w
k,y
represents
the percentage invested in country i in year y by company k. The average Herfindahl
index for the individual property companies may range from 1/n to 1, where 1
represents a company that is focused on one country alone and values close to 0
indicate a high level of geographical diversification.
Third, we include a dummy variable taking the value of 1 if an international
property company also invests outside its own continent.
To address the impact of firm- and market-specific variables on the risk-adjusted
performance of internationals, we run the following equations:
a
i
¼ g
0
þg
1
PRI
i
þg
2
CORREL
i
þg
3
SIZE
i
þg
4
HI
i
þg
5
DIST
i
þ"
i
ð7aÞ
a
i
¼ q
0
þq
1
TI
i
þq
2
CORREL
i
þq
3
SIZE
i
þq
4
HI
i
þq
5
DIST
i
þ?
i
ð7bÞ
where ?
i
refers to Jensen’s alpha for company i, as estimated using Eq. (3), PRI
i
to the
political environment, TI
i
to the real estate market transparency, CORREL
i
to the level
of economic integration, SIZE
i
to the average market capitalization of the international
company, HI
i
is a measure of geographical focus, DIST
i
is a dummy variable taking the
value of 1 if company i also invests across continents, and ?
i
and ?
i
are the error terms.
9
Table 3 shows the regression results of Eqs. (7a) and (7b) for the 46 international
property companies that have at last 24 monthly data-points. Panel A presents the
results for the full sample period. Only the size coefficient is statistically significant
and positively related to relative performance. This indicates that larger companies
are indeed able to overcome informational disadvantages by growing in size, which
is in line with Eichholtz et al. (2001). Other studies have also indicated that there is a
scale effect in real estate investing, with an inverse relationship between equity betas
and firm size (Ambrose et al. 2005; Yang 2001). The political environment, the level
of economic integration, the transparency of the real estate market, the geographical
focus and the measure of distance to the local market are not able to explain the
9
Only companies with more than 24 monthly data-points are included to minimize noise and to ensure the
accuracy of the estimates.
Transparency, Integration and Cost of International Real Estate Investments
cross-sectional variation in alpha. The adjusted R
2
s of the two regressions are 0.08
and 0.07, respectively.
A possible explanation for the poor explanatory power of models (7a) and (7b) is
the time variation in the performance of internationals. Figure 2 indicated that the
Table 3 Explaining Jensen’s Alpha, regression results
Equation (7a) Equation (7b)
Panel A. Full period: 01/1996–12/2007
Political risk index ?0.19 ?0.63 –
Transparency index – 0.18 0.50
Economic market integration ?0.93 ?0.71 ?1.10 ?0.85
Company size in log 0.24 2.38** 0.23 2.24**
Geographic Herfindahl index ?1.41 ?0.92 ?2.14 ?1.28
Distance 0.29 0.83 0.32 0.91
Intercept ?0.32 ?0.30 ?0.08 ?0.08
n 46 46
R² adj. 0.08 0.07
Panel B. Sub-period: 01/1996–06/2001
Political risk index 0.44 1.64 –
Transparency index ? 0.50 2.04*
Economic market integration 1.96 1.83* 1.95 1.92*
Company Size in log 0.29 2.80** 0.31 3.21***
Geographic Herfindahl index 0.23 0.15 ?0.47 ?0.29
Distance 0.96 3.32*** 0.74 2.81**
Intercept ?3.63 ?4.76*** ?3.51 ?4.76***
n 23 23
R² adj. 0.59 0.62
Panel C. Sub-period: 07/2001–12/2007
Political risk index ?0.15 ?0.38 –
Transparency index – 0.00 0.00
Economic market integration ?2.45 ?1.25 ?2.62 ?1.36
Company size in log 0.06 0.40 0.05 0.36
Geographic Herfindahl index ?1.58 ?0.75 ?1.83 ?0.82
Distance ?0.24 ?0.45 ?0.25 ?0.48
Intercept 2.28 1.31 2.46 1.44
n 40 40
R² adj. 0.07 0.08
Table 3 reports the results of the OLS regressions on Eqs. (7a) and (7b). The political risk index is based
on the Index of Economic Freedom (IEF), the measure of economic market integration is proxied by the
correlation between the domestic and foreign markets invested in, the transparency of the property market
is measured by the Real Estate Transparency Index of Jones Lang LaSalle, company size is represented by
the natural logarithm of market capitalization, geographical focus is measured by the Herfindahl Index and
distance is a binary dummy that takes the value of 1 if the company invests across continents. White’s
(1980) heteroskedasticity robust t-statistics within parentheses. *** indicates significance at the 1% level;
** indicates significance at the 5% level, and * indicates significance at the 10% level
P. Eichholtz et al.
relative performance of internationals has substantially improved over the sample
period. To further address this issue, we split the sample in two sub-periods of equal
length. The cut-off date is June 2001.
10
The estimated individual alphas for the two sub-periods are not reported here,
but they provide further evidence that the costs of direct investments in foreign
real estate have decreased over time. While in the first 6 years of the sample
period the average alpha was ?0.33%, it increased to 0.57% in the second sub-period.
The difference in means is statistically different from zero. The fraction of alphas
that is negative decreases from 78.3% in the first sub-period to 25% in the second
sub-period.
Panel B of Table 3 presents the OLS results for the sub-period from 1996 through
June 2001. In this period the explanatory power of the model is substantially
stronger, with adjusted R
2
s of 0.59 and 0.62. Political risk (PRI), which represents
the riskiness of the political environment, has a positive though statistically
insignificant coefficient. In line with the sign on the PRI coefficient, real estate
market transparency (TI) is positively and significantly related to the performance of
internationals. In terms of economic significance, the coefficient of 0.50 implies that
investing in markets with a transparency score that is one standard deviation higher,
leads to an annual increase in alpha of 6.0%. This evidence is in line with Liao and
Mei (1999) who study the effect of institutional factors on real estate returns. In a
more general framework, La Porta et al. (2002) find that firms investing in more
transparent countries with a strong institutional framework outperform firms in less
transparent markets.
This implies that there is at least some evidence that the disadvantages faced by
foreign property investors are lower in countries with strong political and market
institutions. The fact that market transparency seems to be more important than
political risk suggests that access to information plays a distinguishing role in the
relative performance of international property investors.
With respect to the influence of firm-specific factors on internationals’ returns,
firm size (SIZE) has a significantly positive impact on the relative performance of an
international in the first sub-period. The extent to which a property company is a true
international investor does not matter for performance, as indicated by the
insignificant coefficients on the Herfindahl index. This contrasts existing evidence
regarding international diversification, which has been shown to be value-destroying
in continental Europe (Boer et al. 2005). However, international property companies
that are active across continents rather than diversifying within their own region
perform significantly better than those that stay within their continent. The difference
in annualized alphas amounts to about 11.5% and 8.9% in Eqs. 7a and 7b,
respectively. This confirms the presence of a “continental factor” in real estate
returns
11
and may indicate that there is still a disconnect in economic drivers
10
Using the same three sub-periods as in Table 2, Panel B, is not optimal as the alphas are estimated over
shorter periods, which increases the noise of the estimations. Moreover, several firms would be excluded
due to an insufficient number of monthly data points.
11
There has been an extensive discussion on the presence of local, regional, continental and global factors
in the literature. See for example Bond et al. (2003), Eichholtz et al. (1998), Hamelink and Hoesli (2004)
and Ling and Naranjo (2002).
Transparency, Integration and Cost of International Real Estate Investments
between different continents—although the recent contagion in financial markets
would suggest otherwise.
The results of the analysis for the second sub-period are substantially different.
Panel C of Table 3 shows that models (7a) and (7b) have lost most of their
explanatory power, and the six independent variables no longer have a significant
impact on the risk-adjusted performance of international property companies. This
may be due to the limited cross-sectional variability of the alphas, as internationals
do not underperform locals in the second half of the sample period. The results may
also imply that investors in internationally oriented property companies now take
country risk into account in their investment decisions—the risk is priced. Last, and
most important, the results for the latter half of the sample period may be an
indication for the increased international convergence in terms of market
transparency. With less variation in the opacity of real estate markets, the economic
implications of differences between countries in their ranking on the Political Risk
Index or the JLL Transparency Index are limited for property investors.
Conclusions and Discussion
This paper compares the performance of internationally operating property
companies with property companies focusing on their local market, for the period
from 1996 through 2007, shedding light on the importance of political and market
institutions for the performance of international property investors. The results show
that international property companies only underperform their local peers in the early
years of the sample period, while the underperformance disappears in the later years.
We show that the underperformance in the early years is driven by the institutional
environment, the level of economic integration, and the real estate market
transparency of the countries that the international companies invest in. Furthermore,
the results show that larger international companies and those companies that also
invest outside their continent perform significantly better. In the later years of the
sample period there are no more signs of underperformance, and all factors lose their
ability to explain performance differences among international property companies.
For end-investors that want to build up exposure to foreign real estate, these
results imply that that they could either buy shares of internationally diversified
property companies or hold a portfolio of foreign property companies that focus on
their home market. Neither of those two strategies is superior given that differences
in the returns have disappeared over the last years.
These results support the increase in cross-border investment activity by listed
property companies observed in recent years. Given that the costs associated with
direct investments in cross-border real estate have decreased, property companies
may well adopt a global investment strategy. However, managers of property
companies should learn from the results in the early years in our sample period. The
analysis shows that foreigners may be at a disadvantage especially in countries with
an unfavorable political environment, an opaque real estate market, and a low level
of economic integration. The companies in our sample mainly invest in countries
that have relatively high scores on these dimensions, and moreover, transparency
scores have improved over the sample period. In less mature markets, such as
P. Eichholtz et al.
Eastern Europe, Turkey and some emerging countries in Asia, the costs of cross-
border investments may still be significant. This is relevant, as listed property
companies and other property investors have now ventured into these countries.
Consequently, managers should carefully evaluate the investment environment
before entering new markets.
An important limitation of this paper is that the sample size remains relatively
small, especially in the early years that we analyze. This may hamper the
generalization of the results and the ability to test more elaborate performance
attribution models. Given that the availability of information and data on international
property markets gradually increases, future research regarding the costs of
international property investment may take the results of this study as a starting point.
Open Access This article is distributed under the terms of the Creative Commons Attribution
Noncommercial License which permits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
References
Adler, M., & Dumas, B. (1975). Optimal international acquisitions. Journal of Finance, 30, 1–19.
Ambrose, B. W., Highfield, M. J., & Linneman, P. D. (2005). Real estate and economies of scale: The case
of REITs. Real Estate Economics, 33, 323–350.
Armstrong, V., & Riddick, L. (1998). Evidence that differences in bankruptcy law among countries affect
firm returns. Working Paper: Washington State University.
Boer, D., Brounen, D., & Op’t Veld, H. (2005). Corporate focus and stock performance: International
evidence from listed property markets. Journal of Real Estate Finance and Economics, 31, 263–281.
Bond, S. A., Karolyi, G. A, & Sanders, A. B. (2003). International real estate returns: A multifactor,
multicountry approach. Real Estate Economics, 31, 481–500.
Capozza, D. R., & Seguin, P. J. (1999). Focus, transparency and value: The REIT evidence. Real Estate
Economics, 27, 587–619.
Conover, C. M., Friday, H. S., & Sirmans, C. F. (2002). Diversification benefits from foreign real estate
investments. Journal of Real Estate Portfolio Management, 8, 17–25.
Dahlquist, M., Pinkowitz, L., & Stulz, R. M. (2003). Corporate governance and the home bias. Journal of
Financial & Quantitative Analysis, 38, 87–110.
Dhar, R., & Goetzmann, W. N. (2006). Institutional perspectives on real estate investing: The role of risk
and uncertainty. Journal of Portfolio Management, 32, 106–116.
Edelstein, R., Qian, W., & Tsang, D. (2009). How do institutional factors affect international real estate
returns? Working Paper: UC Berkeley.
Eichholtz, P. M. A. (1996). Does international diversification work better for real estate than for stocks and
bonds. Financial Analyst Journal, 52, 56–62.
Eichholtz, P. M. A. (1997). How to invest internationally: Region and property type on a global scale. Real
Estate Finance, 14, 51–56.
Eichholtz, P. M. A., & Kok, N. (2007). The EU Reit and the internal market for real estate. Brussels:
European Property Federation.
Eichholtz, P. M. A., Huisman, R., Koedijk, K., & Schuin, L. (1998). Continental factors in international
real estate returns. Real Estate Economics, 26, 493–509.
Eichholtz, P. M. A., Koedijk, K., & Schweitzer, M. (2001). Global property investment and the costs of
international diversification. Journal of International Money & Finance, 20, 349–366.
Geurts, T. G., & Jaffe, A. J. (1996). Risk and real estate investment: An international perspective. Journal
of Real Estate Research, 11, 117–130.
Goetzmann, W. N., & Wachter, S. M. (1995). The global real estate crash: Evidence from an international
database. Singapore: AREUEA International Congress on Real Estate.
Hamelink, F., & Hoesli, M. (2004). What factors determine international real estate security returns? Real
Estate Economics, 32, 437–462.
Transparency, Integration and Cost of International Real Estate Investments
Hobbs, P., Chin, H., & Topintzi, E. (2007). Global real estate investment and performance. London:
RREEF Research.
Hoesli, M., Lekander, J., & Witkiewicz, W. (2004). International evidence on real estate as a portfolio
diversifier. Journal of Real Estate Research, 26, 161–206.
Jones Lang LaSalle (JLL). (2008). From opacity to transparency: Real estate transparecny index. London:
Jones Lang LaSalle.
La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R. (1998). Law and finance. Journal of
Political Economy, 106, 1113–1155.
La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R. (2000). Agency problems and dividend
policies around the world. Journal of Finance, 55, 1–33.
La Porta, R., Shleifer, A., Lopez-De-Silanes, F., & Vishny, R. W. (2000). Investor protection and corporate
governance. Journal of Financial Economics, 58, 3–27.
La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R. (2002). Investor protection and corporate
valuation. Journal of Finance, 57, 1113–1155.
Liao, H., & Mei, J. P. (1999). Institutional factors and real estate. International Real Estate Review, 2, 21–34.
Ling, D. C., & Naranjo, A. (2002). Commercial real estate return performance: A cross-country analysis.
Journal of Real Estate Finance & Economics, 24, 119–142.
Liu, C. H., & Mei, J. P. (1998). The predictability of international real estate markets, exchange risks and
diversification consequences. Real Estate Economics, 26, 3–40.
Newell, G., & Worzala, E. (1995). The role of international property in investment portfolios. Journal of
Property Finance, 6, 55–63.
Newell, G., & Worzala, E. (1997). International real estate: A review of strategic investment issues.
Journal of Real Estate Portfolio Management, 3, 87–96.
Quan, D. C., & Titman, S. (1997). Commercial real estate prices and stock market returns: An
international analysis. Financial Analysts Journal, 53, 21–34.
Shleifer, A., & Vishny, R. W. (1993). Corruption. Quarterly Journal of Economics, 108, 599–617.
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for
heteroskedasticity. Econometrica, 48, 817–838.
Worzala, E. (1994). Overseas property investments: How are they perceived by the institutional investor?
Journal of Property Valuation and Investment, 12, 119–141.
Worzala, E., & Sirmans, C. F. (2003a). International direct real estate investment: A review of the
literature. Urban Studies, 40, 1081–1114.
Worzala, E., & Sirmans, C. F. (2003b). Investing in international real estate stocks: A review of the
literature. Urban Studies, 40, 1115–1149.
Yang, S. (2001). Is bigger better? A re-examination of the scale economies of Reits. Journal of Real Estate
Portfolio Management, 7, 67–77.
Ziobrowski, A., & Curcio, R. (1991). Diversification benefits of us real estate to foreign investors. Journal
of Real Estate Research, 6, 119–142.
P. Eichholtz et al.

doc_503251923.pdf