Working
Paper
SeriesCGS No.001“Market transparency and real estate investment”

T. Sadayuki, K. Harano, F. Yamazaki

April 2019

College of Economics, Nihon University

Market transparency and real estate investment

T. Sadayuki*, K. Harano**, F. Yamazaki****Faculty of Political Science and Economics, Waseda University**Department of Real Estate Sciences, Meikai University***College of Economics, Nihon University

Abstract
This paper provides new empirical evidence about the important role ofmarket transparency in international real estate investment. We apply themethodology adopted by Lieser and Groh (2014) to updated national panel
data covering 44 countries from 2004 to 2016. The results suggest thatcountries with better accessibility to market information and higher
enforceability of regulations have less information asymmetry and attract
more inward real estate investment. Interestingly, the accounting qualityof corporate governance is negatively correlated with investment,
indicating the possibility that foreign investors enjoy high excess returns
by investing in real estate in countries with poor accounting quality. We
also find that the larger investment is caused by the higher growth rateof house prices and lower land productivity, implying that foreign
investors seek regions with potential for future demand in the market.
Because a large difference in market transparency among countries still
exists, addressing information asymmetry in countries lacking transparency
will promote efficiency in the global market of real estate investments.

1

1

Introduction

The improvement in the availability and comparability of data on developing
countries over the past two decades has provided many valuable insights
into global markets for not only investors but also researchers. Startingfrom La Porta et al. (1997), who demonstrated the significance of therelationship between the development of the financial market and the legal
system, a number of studies on the role of legal systems in financial markets(La Porta et al., 1998, 2002; Graff, 2008) and in economic growth (Levine,
2005; Jappeli et al., 2005; Galindo and Micco, 2004) have accrued. A large
number of such studies on the relationship between legal systems andeconomic growth have sought to understand how markets with diverse legal
systems stimulate different types of investments from foreign investors.
In particular, international real estate investment, accounting for a large
portion of cross-border investment, can play a significant role in economic
growth and urbanization in host countries through capital accumulation andefficient land use. Recent studies suggest the importance of thetransparency of the real estate market in inward real estate investment(Adar et al., 2006; Eichholtz et al., 2011; Falkenbach, 2009; Fazaneganand Fereidouni, 2014; Fereidouni and Masron, 2013; Lieser and Groh, 2014;
Schulte et al., 2005), while evidence on the relationship based on a
sophisticated empirical analysis is scant. As mentioned in Levine (2005),
the determinants of investment should be carefully examined consideringvarious factors, such as legal, social, demographic and natural conditions,as well as endogenous factors inherent in each market.
This paper aims to investigate the role of market transparency ininternational real estate investment by utilizing country-level panel data
to address such various factors and endogeneity issues. It is worth
mentioning the literature that relates to our study. Eichholtz et al. (2011)
analyze the performance, measured by Jensen’s alpha, between
internationally operating real estate companies and domestic real estatecompanies focusing on local markets, during 1996-2007. They find thatinternational real estate companies underperform in the early period, while
underperformance disappears with more transparent conditions in the later
years. They argue that the improvement of the transparency of the realestate industry has recently equalized the conditions for foreign investors.
Fereidouni and Masron (2013) and Lieser and Groh (2014) use panel data to
examine the determinants of international real estate investment.
Fereidouni and Masron (2013) use the corruption perceptions index provided
by Transparency International as a proxy of market transparency and findthat higher transparency is associated with greater investment. Lierser
and Groh (2014) collect various socioeconomic and institutional variables2across countries covering the period between 2000 and 2009 and conduct anaugmented random effect panel regression. They find several factors that
may attract international real estate investment, namely, economic growth,
rapid urbanization, compelling demographics, higher transparency in thelegal framework, ease of administrative burdens and sociocultural
challenges, and political stability. Other studies examine therelationship between market transparency and aggregate FDI (Drabek andPayne, 2002; Seyoum and Mnyak, 2009; Egger and Winner, 2003). Farzaneganand Fereidouni (2014) analyze panel data for 32 countries between 2004 and2010 and find that the country fixed effects do not show a statisticallysignificant relationship between market transparency and FDI inflows to
the real estate sector.
This paper brings new empirical evidence to the literature by focusing
primarily on the role of market transparency in determining the volume ofinward real estate investment, using updated country-level panel data
covering 44 countries from 2004 to 2016. We follow the methodology taken
by Lieser and Groh (2014) and introduce new explanatory variables, suchas interest rate, house price growth, land productivity and market
transparency indices, in our analysis. Key variables are the market
transparency indices that are provided for this research by JLL and LaSalle
Investment Management, a world consultancy company specializing inproperty services and investment management. This paper differs from otherstudies using the transparency index by JLL and LaSalle Investment
Management (Eichholtze et al. 2011; Farzanegan and Fereidouni, 2014; Newell,2016; Sharp, 2013) in that we use an updated transparency index constructing
panel data with a longer time dimension, utilize multiple transparency
indices instead of just an aggregate index to examine how the investment
is attributed to different aspects of market transparency, and employ theaugmented panel data method (or “correlated random effects approach” asin Wooldridge (2015)) for intra- and international investigations of thedeterminants of the investment.
The estimation result suggests that countries with higher market
transparency receive more investment from foreign countries than countries
with lower market transparency, with other factors, such as economic sizeand growth, being constant. In particular, better accessibility tofundamental information on the real estate market and higher enforceabilityof real estate-related regulations are associated with greater inward
investment. However, the accounting quality of corporate governance isnegatively correlated with investment, implying that investors preferinvesting in real estate in countries with poor accounting quality, which
generates greater excess returns for real estate investment. We also find3

that investment is positively correlated with higher house price growthand lower land productivity, which suggests the possibility that foreign
investors seek regions with potential for future demand in the market.
The following section describes the data used in the analysis. Then, theempirical model and results are demonstrated. Finally, the last section
draws the conclusions.

2

Data

We collected country-by-every-second-year unbalanced panel data of 44
countries, in the period from 2004 to 2016. In this subsection, we introduce
three types of variables used in our analysis: 1) inward commercial realestate investment, 2) real estate market transparency indices, and 3) a
set of control variables selected from those used in Lieser and Groh (2014)
as explanatory variables.
2.1 Inward real estate investment
The data on the inward real estate investment were prepared by a real estateadvisory company, Cushman & Wakefield (hereinafter, C&W). As mentioned in
Lieser and Groh (2014), the data provided by C&W are considered the highest
quality database on international real estate investment.1 In particular,
we use country-level panel data on inward commercial real estate
investments as a dependent variable in our analysis. The same database wasused in Lieser and Groh (2014), covering the period from 2000 to 2009.
Figure 1 shows the time trend of global inward real estate investment in
US$ billion from 2004 to 2017.2 The investment was increasing until the1

Please refer to the C&W website (http://www.cushmanwakefield.com/en) for details on the company andthe data of international real estate investment.
2

Figure 1 was constructed in the following manner. In the following equation,
𝑦𝑖𝑡 = 𝜓𝑖 + 𝜌𝑡 + 𝑒𝑖𝑡

𝑦𝑖𝑡 is the natural logarithmic volume of investment toward country 𝑖 in year 𝑡, 𝜓𝑖 is an individual fixed
effect of country 𝑖, 𝜌𝑡 is a time fixed effect of year 𝑡, and 𝑒𝑖𝑡 is the residuals. Let 𝑦̅𝑖 refer to the time
average of the logarithmic investment toward country 𝑖 and 𝑦̿ be the average of the logarithmic
investment across years and countries. Subtracting 𝑦̅𝑖 from and adding 𝑦̿ to 𝑦𝑖𝑡 yields the following
equation:𝑦𝑖𝑡 − 𝑦̅𝑖 + 𝑦̿ = 𝜓̿ + 𝜌𝑡 + 𝑒̃𝑖𝑡where 𝜓̿ is the average of country fixed effects and 𝑒̃
̅𝑖 + 𝑒̿. In figure 1, the exponentials of
𝑖𝑡 = 𝑒𝑖𝑡 − 𝑒
OLS estimates of 𝜓̿ + 𝜌𝑡 of the equation, i.e., exp(𝜓̿̂ + 𝜌̂𝑡 ), were plotted over time as a solid line, withdashed lines representing 95% significance intervals calculated based on standard deviations.

4

start of the rapid decline due to financial crisis, and then it gradually
increased, reaching almost the level prior to the crisis by 2015.
<<insert Figure 1 here>>
2.2 Real estate market transparency index
The data on real estate market transparency are provided by JLL and LaSalle
Investment Management, a world leading property consultancy company
specializing in property services and investment management.3 In 2016, JLL
published a report on the 9th edition of the Global Real Estate Transparency
Indices (GRETI), which measures the real estate market transparency indifferent countries and is constructed based on a survey of 139 constituent
factors.4 We were provided the biennial panel data from 2004 to 2016 of thecomposite score, which is a comprehensive evaluation of real estate market
transparency, and 13 transparency subindices ([1] Direct property indices,[2] Listed real estate securities, [3] Unlisted fund indices, [4]Valuations, [5] Fundamental data, [6] Financial disclosure, [7] Corporate
governance, [8] Regulation, [9] Land and property registration, [10]Eminent domain, [11] Real estate debt information, [12] Sales transactions,and [13] Occupier services). Table A2 in Appendix 2 lists 139 factors
composing the 13 indices.
Figure 2 shows the time trends of 14 transparency indices (Composite score+ 13 subindices). Each index is a continuum scale ranging from 1 to 5, witha lower value indicating a higher transparency. As shown in the figures,
market transparency has been improved in almost all aspects, except that
[9] Land and property registration and [10] Eminent domain became lesstransparent from 2012 to 2014, and [11] Real estate debt information becameless transparent after the financial crisis. These indices refer to degreesof transparency, not strictness of regulations or difficulties oftransactional procedures. Therefore, a higher score of [8] Regulation, forinstance, indicates lower transparency in and/or weaker enforceability of
regulation, not a tighter control on regulation.
<<insert Figure 2 here>>
The combination of these two data sets on investment and market transparency
generates unbalanced panel data of 44 countries for a maximum of 7 periods

3

Please refer to the JLL website (https://www.us.jll.com/en) for details on the company and the data of thetransparency indices.
4

The latest version of GRETI, published in 2018, is based on 186 factors with an additional index,
Sustainability.

5(biennial from 2006 to 2014).5 Figure 3 shows scatter plots of real estate
investments against market transparency across countries, where thevertical axis is the time-average real estate investment in logarithmicvalue and the horizontal axis is the time-average market transparency
Composite score. We can observe a clear negative correlation between thesevariables: the lower the Composite score (i.e., the more transparent thereal estate market in a country), the greater the real estate investment
is toward the country. Since the volume of real estate investment is notdetermined solely by market transparency, we need to consider various
factors, such as economic size and regulatory strictness, to extract apartial correlation between investment and market transparency.
<<insert Figure 3 here>>
2.3 Other explanatory variables
The selection of control variables is based on Lieser and Groh (2014). We
first excluded variables of constituent factors comprising the JLL
transparency indices and then collected as many other variables listed in
Lieser and Groh (2014) as possible. However, we were not able to collectall desired variables due to limitations of data accessibility. To retain
sufficient numbers of observations in our analysis, we selected two setsof control variables. One set of variables, denoted by Xa, incudes thosethat are available between 2004 and 2016 for more than 85% of the 44
countries. The other set of variables, denoted by Xb, includes variables
that are available from 2004 to 2016 for more than 80% of 43 countries.
We also gathered three additional variables not used in Lieser and Groh(2014) that are expected to influence the inward real estate investment:
namely, (1) interest rates, (2) house price growth rates, and (3) value
added in the service sector per urban land area. (1) The interest rate is
expected to be negatively correlated with the volume of real estate
investment because a higher interest rate yields a greater amount repaid
when making a loan to purchase real estate. However, as the investment isoften financed in investors’ countries, the interest rate in a host country
may not have a significant impact on the cross-border investment. Although
long-term interest rates may have been more appropriate to capture theimpact on the investment, the number of countries for which the data wereavailable was not sufficient, and thus, the money market interest rates
were used. (2) The expectation of house price growth may increase the inward
investment because the demand for goods/services expands with the number
5

Basic statistics of real estate investments, Composite score, and number of observations by country aredescribed in Table A1 in Appendix 1.

6

of higher-income consumers in the region and because the appreciation of
invested real estate asset value increases collateral value to make further
investments. (3) Lastly, the value added of the service sector per urban
land area indicates the land productivity of the service sector. The
direction of the effect of land productivity on investment is ambiguous.
On the one hand, foreign investors may prefer investing in commercial realestate in profitable regions, while on the other hand, some investors may
strategically invest in regions that still have room for higher
profitability in the future.
The basic statistics of real estate investments, market transparency
indices, explanatory variables, Xa, selected from Lieser and Groh (2014),
and the three additional variables are shown in Table 1.
<<insert Table 1 here>>3

Empirical analysis

3.1 Estimation modelAs with Lieser and Groh (2014), the following augmented panel regression
model will be estimated by random effect estimation:
̅ 𝑖 𝛃𝐵 + 𝜀𝑖𝑡𝑦𝑖𝑡 = 𝑢𝑖 + 𝛿𝑡 + 𝐓𝐈̈𝑖𝑡 𝛂𝑊 + ̅̅̅
𝐓𝐈𝑖 𝛂𝐵 + 𝐗̈ 𝑖𝑡 𝛃𝑊 + 𝐗
where 𝑦𝑖𝑡 is the natural logarithmic value of the annual inward commercial
real estate investment to country 𝑖 from the rest of the world in year 𝑡; 𝑢𝑖
is country 𝑖’s unobservable fixed effect; 𝛿𝑡 is a year-time fixed effect,
𝐓𝐈 is a vector of market transparency indices; 𝐗 is a vector of control
variables that may affect the investment6; and 𝜀𝑖𝑡 is an error term. ̅̅̅
𝐓𝐈𝑖 and̅
𝐗 𝑖 are the averages of 𝐓𝐈𝑖𝑡 and 𝐗 𝑖𝑡 for country 𝑖 over time, that is,̅̅̅̅ 𝑖 ≡ ∑𝑡 𝐗 𝑖𝑡 ⁄𝑇𝑖 , where 𝑇𝑖 is the number of years for which
𝐓𝐈𝑖 ≡ ∑𝑡 𝐓𝐈𝑖𝑡 ⁄𝑇𝑖 and 𝐗
the data for country 𝑖 are available. 𝐓𝐈̈𝑖𝑡 and 𝐗̈ 𝑖𝑡 indicate
within-transformed variables, that is, 𝐓𝐈̈𝑖𝑡 ≡ (𝐓𝐈𝑖𝑡 − ̅̅̅𝐓𝐈𝑖 ) and 𝐗̈ 𝑖𝑡 ≡
̅ 𝑖 ).
(𝐗 𝑖𝑡 − 𝐗
Here, 𝛂𝑊 and 𝛃𝑊 are within estimators indicating how changes in the
independent variables over time affect the investment within a country.
On the other hand, 𝛂𝐵 and 𝛃𝐵 are between estimators indicating
6

Lieser and Groh (2014) constructed six indices, each of which was composed of four to six row variables,
and used them as explanatory variables to estimate the investment equation. On the other hand, we use allavailable row variables along with transparency indices, for the main purpose of this paper is to examine therelationship between the market transparency and the volume of investment.

7

cross-border correlations due to differences in the levels of variables
and investments across countries.7
3.2 Estimation results
We first look at the estimation results that use the Composite score with
two sets of variables, Xa and Xb, as explanatory variables (Table 2); then,
the two sets along with three additional variables are added separately(Table 3). Lastly, the result using 13 transparency subindices is described(Table 4).
Table 2 describes the results using Composite score as an indicator formarket transparency. The first two columns show the within estimates andbetween estimates of a model using Xa as control variables. The between
estimate of the Composite score is -1.331, which is statisticallysignificant at the 1% level, implying that a country with a one-point higher
time-average Composite score is associated with a lower investment byapproximately 74% (= e−1.331 − 1) with other factors, such as GDP, urban
population, and FDI net flows, being constant. On the other hand, the within
estimate of the Composite score is not statistically significant. These
results suggest the possibility that countries that had a head start on
facilitating a transparent market were immune to foreign competition andenjoyed a significant increase in inward investment (and/or that countries
that enjoyed large inward investments from the early period continuously
engaged in facilitating transparency in the market), which makes the
between estimate statistically significant, while the marginal gain from
improving market transparency diminished since the market transparencies
in global real estate markets were gradually leveled, resulting in thenonsignificance of the within estimate in the sample period of 2004-2016.
This explanation is in line with Farzanegan and Fereidouni (2014), who showthat the within effect of transparency on FDI inflows to the real estate
sector is statistically nonsignificant, and with Eichholtz et al. (2011),
who show that the excess returns between international and domestic realestate companies disappear in the later years of their study period.
<<insert Table 2 here>>Among the control variables in Xa, the between estimates show a positive
sign for GDP, real GDP growth, urban population, telecommunication and FDI
net flows and negative signs for GDP per capita and unemployment rate. The
only within estimate showing a significant sign is the unemployment rate.
7

Please refer to, for instance, Wooldridge (2015) and Mundlak (1978) for further explanations on theaugmented panel regression model.

8The last two columns in Table 2 show the results using Xb, which contains
variables regarding costs/difficulties of purchasing, registering,
starting and ending investment procedures in addition to Xa. Although thesecost-related variables are not constituent factors of the Composite score,
the market transparency can be correlated with these variables, which could
affect the coefficients for the Composite score. As shown in the estimation
result, the between estimate of the Composite score is -0.868, whose
absolute value is smaller than that of the previous estimation without using
cost-related variables. This implies that the Composite score is positively
correlated with the cost-related variables, attenuating the coefficientof the Composite score. The estimation results of both models assure thepositive relationship between the volume of inward investment and market
transparency across countries.
Now, we include additional variables: (1) interest rate, (2) house pricegrowth rate and (3) service-sector value added per urban land area. Becauseof data limitations, we run three regressions using each of additionalvariables separately along with Xa and the Composite score as explanatory
variables to retain sufficient numbers of observations. Table 3 shows theestimation results of the coefficients for the Composite score andadditional variables. The results of the coefficients for Xa are omitted
from the table. (1) In general, the interest rate is expected to have anegative correlation with investment. However, the coefficients are notstatistically significant. This may be attributed to the fact thatinvestors finance cross-board investments in their own countries to someextent, reducing the significance of the correlation between investment
and interest rate in host countries. (2) Regarding house price growth, thewithin estimate is positive and significant at the 10% level. As the house
price appreciates, investors increase investment, expecting future
expansion of market demand and increase in the collateral values of realestate. (3) The value added of the service sector per land area can beinterpreted as the land productivity of the service sector. The between
estimate shows a negative sign at the 10% significance level. This implies
the possibility that investors invest in countries with a greater potential
for increasing demand in the market.
<<insert Table 3 here>>
Finally, Table 4 describes the estimation results using 13 transparency
subindices as explanatory variables in place of the Composite score. We
do not find any significant sign for the within estimates. The between
estimates with significant signs in both regressions are [1] Direct
property indices, [7] Corporate governance, and [8] Regulation.
9

<<insert Table 4 here>>[1] Direct property indices, composed of six constituent factors,8 measures
the accessibility to fundamental information on the real estate market andperformance in the targeted country. The positive estimated coefficient
suggests that the higher accessibility to and transparency of thefundamental information on the real estate market in the targeted country
reduces the information asymmetry between investees and investors acrosscountries, resulting in large real estate investment toward the country.
[8] Regulation, composed of 13 factors, 9 measures the availability,
enforceability and predictability of various real estate-related
regulations in a country. The negative coefficient for Regulation suggeststhat a high transparency of such factors can reduce the uncertainty andrisk of investment and thereby attract foreign investors.
[7] Corporate governance, composed of four factors,10 reflects the audit
quality of cooperate governance. Among the above three indices withsignificant signs, only Corporate governance shows a positive correlation
with investment: the higher the index is (i.e., the lower the audit qualityof corporate governance), the larger the investment, implying thatinvestors prefer investing in real estate in countries where auditing
standards are less strict. This interpretation is supported by the positive
sign of [6] Financial disclosure, 11 the measure of accessibility andaccountability to financial statements, indicating that a lack of
accountability for financial statements is associated with a large inward
investment. Edelstein et al. (2011) show that real estate security returns
are negatively correlated with the quality of country-specific corporate
8

The six factors comprising Direct property indices include existence of a direct property index,

reliability of the index and extent to which it is used as a benchmark of performance, type of index,
length of national direct property level returns index time series, size of national institutional
investment in the real estate market, and market coverage of the direct property index.
9

The 13 factors comprising Regulation include extent to which the tax code is consistently applied

for domestic / foreign investors, extent to which real estate tax rates are predictable for domestic/ foreign investors, existence / predictability / enforcement of land use rules and zoning, existence/ enforcement of building codes and safety standards for buildings, simplicity of key regulations
in contract law, efficiency of the legal process, and level of contract enforceability for domestic/ foreign investors.
10

The four factors comprising Corporate governance include manager compensation and incentives,use of outside directors and international corporate governance best practice, alignment ofinterests and shareholder power, and free float share of the public real estate market.
11

Factors comprising Financial disclosure include stringency of accounting standards, level ofdetail in / frequency of financial statements, and data disclosure by companies.

10

governance. Although poor corporate governance is expected to amplify
information asymmetries and reduce investment, the increase in excess
returns for real estate investment due to poor accounting quality may
outweigh the issue of asymmetric information. Egger and Winner (2005) and
Glass and Wu (2002) find positive relationships between corruption and FDI
in host countries, suggesting that corruption may be beneficial by allowing
circumvention of regulatory and administrative restrictions (Leff, 1964).

4

Conclusion

This paper investigates the role of market transparency in international
real estate investment. By using updated country-level panel data covering
44 countries from 2004 to 2016, the empirical results confirm the positive
relationship between market transparency and international investment. In
particular, better accessibility to fundamental information on the realestate market and higher enforceability and predictability of realestate-related regulations are strongly associated with larger inward
investment. These factors may attract foreign investors by reducing theasymmetric information between investees and investors across countries.
However, we find a negative relationship between the accounting qualityof corporate governance and investment. The increase in excess returns forreal estate may outweigh the issue of information asymmetry that is
generated when corporate governance lacks accountability in a targetedcountry. We also find that investment is positively correlated with higher
house price growth and lower land productivity, which may reflect that
foreign investors seek regions with potential for future demand in themarket.
Overall, the coefficients for market transparency show significant signs
only in terms of between estimates, not within estimates. This indicatesthat the countries with large inward real estate investment had facilitatedmarket transparency from an early period that this study did not cover.
These countries could have been immune to foreign competition and enjoyedlarge inward investments from the early period continuously engaged in
facilitating transparency in the market, while the marginal gain from
improving market transparency diminished since the market transparencies
in global real estate markets were gradually leveled. That said, there is
still a significant gap among countries in terms of market transparency,
and leveling the gap is necessary to promote efficiency in the global marketof real estate investment.
1112

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Figure 1

The trend of inward real estate investment (USD billion)

8

4
Mean
h/b(95%)2l/b (95%)
115

Figure 2

Market transparency indicesComposite score[1] Direct property indices[2] Listed real estate
securities

5

5

5

4

4

4

3

3

3

2

2

2

1
2004 2006 2008 2010 2012 2014 2016

1

[3] Unlisted fund indices1
2004 2006 2008 2010 2012 2014 20162004 2006 2008 2010 2012 2014 2016[4] Valuations[5] Fundamental data

5

5

5

4

4

4

3

3

3

2

2

2

1
2004 2006 2008 2010 2012 2014 2016

1
2004 2006 2008 2010 2012 2014 2016

[6] Financial disclosure1
2004 2006 2008 2010 2012 2014 2016

[7] Corporate governamnce[8] Regulation

5

5

5

4

4

4

3

3

3

2

2

2

1

1
2004 2006 2008 2010 2012 2014 20162004 2006 2008 2010 2012 2014 2016

[9] Land and property
registration[10] Eminent domain12004 2006 2008 2010 2012 2014 2016[11] Real estate debt
regulation

5

5

5

4

4

4

3

3

3

2

2

2

1

1
2004 2006 2008 2010 2012 2014 20162004 2006 2008 2010 2012 2014 2016[12] Sales transactions[13] Occupier services

5

5

4

4

3

3

2

2

1

1
2004 2006 2008 2010 2012 2014 20162004 2006 2008 2010 2012 2014 2016161
2004 2006 2008 2010 2012 2014 2016

Figure 3

Inward real estate investment versus market transparency

6[CELLRANGE]

Ln(Inward real estate investment)

5[CELLRANGE]

4[CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE] [CELLRANGE][CELLRANGE] [CELLRANGE][CELLRANGE]

3
2
1
0
1
-1

2

[CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE]

3

4[CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE][CELLRANGE] [CELLRANGE][CELLRANGE][CELLRANGE]-2

Time-averaged market transparency (Composite score)

17

5

Table 1

Basic Statistics

Variables
Observation MeanSD
Inward commercial real estate investments (US$M)
273
15.80
46.10
JLL Transparency Index: Composite score
173
2.39
0.67
Real GDP (2010 US$B)
273
1.93
9.44
Real GDP per capita (2010 US$K)
273
0.98
0.94
Real GDP growth (%)
273
0.34
0.45
Unemployment rate (%)
273
7.43
4.65
CPI (consumer price index) growth (%)
273
3.28
3.38
Urban population (% of total population)
273
72.76
15.88
Telecommunication (Fixed telephone subscription per 100 people)
273
35.78
16.80
Domestic credit provided by banking sector (% of GDP)
273
128.6764.75
FDI net flows (US$M)
273
39.70
68.89
Marginal corporate tax rate (%)
208
42.89
12.57
Profit and capital gains tax (%)
208
16.31
7.42
Cost of register property (% of warehouse value)
203
4.54
2.93
Procedures to register property (number)
203
5.44
2.33
Time needed to register property (days)
203
36.05
40.20
Procedures to start a business (number)
231
6.86
3.24
Time needed to start a business (days)
231
19.37
18.25
Cost of business start-up procedures (% of income per capita)
231
7.72
7.99
Minimum capital needed to start a business (% of income per capita)
231
26.12
50.88
Time needed to resolve insolvency (years)
231
2.07
1.39
Cost of resolving insolvency (% of estate)
231
11.24
8.40
Recovery rate (cents on a US$) recouped by creditors through insolvency
231
60.77
26.33
Political stability and absence of violence (indicator)
273
62.39
26.02
Interest rate (%, money market interest rate)
251
3.09
3.47
House price growth (% 2 year average)
186
17.70
23.92
Service sector value added per urban land area (US$B per sq. km)
240
30.16
59.52
Source: Cushman & Wakefield, JLL and LaSalle Investment Management, International Monetary Fund, World Bank

18

Table 2

Estimation results with Composite score
Control variables
Xa
Estimates
WithinBetweenXb
WithinBetween

Ln(GDP)
Ln(GDP per capita)
Real GDP growth
Unemployment rate
CPI growth
Urban population
Telecommunication
Domestic credit provided by banking sector
FDI net flows
Political stability and absence of violence
Marginal corporate tax rate
Profit and capital gains tax
Cost of register property
Procedures to register property
Time needed to register property
Procedures to start a businessTime needed to start a business
Cost of business start-up procedures
Minimum capital needed to start a businessTime needed to resolve insolvency
Cost of resolving insolvency
Recovery rate from insolvency

0.657-0.1560.163-0.044*-0.025-0.0300.007-0.0030.000
0.001

0.084+-0.661**1.056*-0.048+0.016
0.018*
0.020*
0.003
0.005*
0.006-0.465-0.9950.213-0.057*-0.018-0.108+0.011-0.0030.000
0.011
0.096**-0.084*-0.070-0.0060.007**-0.034-0.010-0.001
0.001
0.068
0.014
0.019

0.131**-0.692**
1.502**-0.006-0.0520.031**
0.019**
0.002
0.002-0.014*0.019**
0.019-0.078**-0.243**0.010**
0.145**-0.009-0.062**0.001-0.314**-0.034**-0.002

TI (Composite Score)

0.003-1.331**-0.240-0.868**Observations
Number of countries
Number of parameters
R2 (within)
R2 (between)

273
44
28
0.34580.8420** 0.01%, * 0.05%, + 0.1%, significant levels.

19

203
43
51
0.42070.9267

Table 3

Estimation results with additional variables
Additional variable
Interest Rate

TI (Composite)
Additional var.

Control var.
Observations# countries# parameters
R2 (overall)R2 (within)

House Price growth(% 2 year average)

Ln(Service sector value
added / area)

Estimates
WithinBetweenWithinBetweenWithinBetween-0.032(0.346)-0.025(0.025)-1.262**(0.288)0.014(0.038)-0.065(0.446)0.018+(0.005)-1.239*(0.563)0.070(0.040)-0.363(0.355)-1.794(1.201)-1.378**(0.357)-0.376+(0.213)

Xa
251
41
30
0.35570.8339

Xa
186
32
30
0.40770.7884

Xa
240
40
30
0.34290.8458** 0.01%, * 0.05%, + 0.1%, significant levels. Numbers in parentheses are robust standard errors.
Coefficients for control variables, Xa, are not shown in the table.

20

Table 4

Estimation results with 13 transparency subindices
Control variables
Xa
Estimators
WithinBetweenTransparency Index (TI)
[1] Direct property indices[2] Listed real estate securities[3] Unlisted fund indices[4] Valuations[5] Fundamentals data
[6] Financial disclosure[7] Corporate governance[8] Regulation[9] Land and property registration[10] Eminent domain[11] Real estate debt regulation[12] Sales transactions[13] Occupier services-0.030-0.468-0.2450.116-0.2910.077-0.137
0.121
-0.06
0.020
0.171
-0.1770.058

Observations# countries# parameters
R2 (within)
R2 (between)-0.690**-0.611**
0.007
0.127
-0.1370.405**
0.870**-0.872**-0.613**0.521**-0.212+
0.195-0.178273
44
520.33340.9253Xb
WithinBetween-0.028-0.052-0.1140.105-0.0780.126
0.058-0.0980.001-0.1170.131-0.214-0.057-0.767**-0.2520.036
0.539-0.0410.230
0.726**-1.605**0.339-0.113-0.0490.958**
0.399
203
43
75
0.4652
0.9772** 0.01%, * 0.05%, + 0.1%, significant levels. Coefficients of control variables, Xa and Xb, arenot shown in the table.

21

Appendix 1.
Table A1. Basic statistics on investment and market transparency by countries
Country
IOC

AUS
AUT
BEL
BGR
BRA
CAN
CHE
CHN
CZE
DEU
DNK
ESP
FIN
FRA
GBR
GRC
HKG
HRV
HUN
IDN
IND
IRL
ITA
JPN
KOR
MEX
MYS
NLD
NOR
NZL
PHL
POL
PRT
ROU
RUS
SGP
SVK
SWE
THA
TUR
UKR
USA
VNM
ZAF

Commercial real estate
investment (US$ B)
Mean
SD

Observations

7
7
7
5
7
3
7
6
7
7
6
7
7
7
7
7
7
5
7
4
7
6
7
7
7
7
4
7
7
4
4
7
7
7
5
7
5
7
5
7
6
7
4
5

22.7
3.2
3.3
0.4
4.4
11.5
4.5
21.1
2.1
44.3
2.9
9.7
4.5
27.4
73.8
0.2
13.9
0.3
0.8
0.3
2.5
2.3
7.9
37.1
7.3
2.0
2.4
11.1
5.9
1.8
0.2
4.0
1.4
0.9
6.7
8.3
0.5
16.3
0.8
0.9
0.4
257.7
0.3
1.6

Market Transparency(Composite score)
Mean
SD
9.2
1.2
1.2
0.4
1.8
3.3
2.7
12.4
1.2
23.3
1.4
5.2
2.1
7.6
27.5
0.2
4.4
0.3
0.4
0.2
1.1
2.2
3.6
12.3
4.1
1.4
0.9
4.5
2.6
0.9
0.2
1.6
0.9
0.6
2.4
2.1
0.3
3.8
0.3
0.5
0.2
121.8
0.1
0.9

22

1.44
2.27
2.13
3.34
2.81
1.66
1.90
3.20
2.47
1.87
2.01
2.17
1.67
1.67
1.34
2.90
1.89
3.07
2.56
3.14
3.20
1.98
2.17
2.44
3.05
3.09
2.44
1.51
2.20
1.58
2.99
2.25
2.49
3.10
2.92
1.93
2.79
1.77
2.91
3.12
3.74
1.35
3.77
2.22

0.11
0.13
0.19
0.23
0.29
0.06
0.16
0.34
0.29
0.13
0.13
0.17
0.07
0.20
0.07
0.21
0.08
0.17
0.28
0.30
0.36
0.21
0.20
0.27
0.23
0.33
0.13
0.07
0.16
0.05
0.17
0.30
0.26
0.52
0.09
0.12
0.27
0.08
0.20
0.43
0.20
0.07
0.19
0.10

Appendix 2
Table A2. 13 transparency subindices and 139 constituent factors
Indices
Constituent Factors[1] Direct
Property
Indices[2] Listed Real
EstateSecurities
Indices[3] Private
Real Estate
Fund Indices[4] Valuations[5] Market
Fundamentals
Data[6] Financial
Disclosure[7] Corporate
Governance

Existence of Direct Property Index
Reliability of the Index and Extent to which it is Used as a Benchmark of Performance
Type of Index (Valuation Based vs. Notional)
Length of Direct Property Level Returns Index Time SeriesSize of Institutional Invested Real Estate Market
Market Coverage of Direct Property Index
Dominant Type of Listed Real Estate Securities (i.e. Long-term Holders of Real Estate vs. Homebuilders
and Conglomerates)
Use of Listed Real Estate Securities Data on the Real Estate Market
Years Since the First Commercial Real Estate Company was Listed
Value of Public Real Estate Companies as % of GDP
Existence of a Domestic Listed Real Estate Index and Its Use as a Benchmark
Existence of an International Listed Real Estate Index and Its Use as a Benchmark
Length of Public Real Estate Index Time Series
Existence of a Domestic Fund Index and Its Use as a Benchmark
Existence of International Fund Index and Its Use as a Benchmark
Length of Unlisted Fund Index Time Series
Independence and Quality of Third-Party Appraisals
Use of Market-based Appraisal Approaches
Competition in the Market for Valuation Services
Frequency of Third-Party Real Estate Appraisals
Existence and Length of Time Series on Property Rents (Office, Retail, Industrial, Residential)
Existence and Length of Time Series on Take-up/Absorption (Office, Retail, Industrial, Residential)
Existence and Length of Time Series on Vacancy (Office, Retail, Industrial, Residential)
Existence and Length of Time Series on Yields/Cap Rates (Office, Retail, Industrial, Residential, Hotels)
Existence and Length of Time Series on Capital Values (Office, Retail, Industrial, Residential, Hotels)
Existence and Length of Time Series on Investment Volumes (Office, Retail, Industrial, Residential,
Hotels)Existence and Length of Time Series on Revenue per Available Room for Hotels
Existence and Geographical Coverage of a Database of Individual Buildings (Office, Retail, Industrial,
Residential, Hotels, Alternatives)
Existence and Geographical Coverage of a Database of Leases (Office, Retail, Industrial, Residential,
Hotels, Alternatives)
Existence and Geographical Coverage of a Database of Property Transactions (Office, Retail, Industrial,
Residential, Hotels, Alternatives)
Proportional Coverage of Databases on Individual Buildings (Office, Retail, Industrial, Residential, Hotels,
Alternatives)
Proportional Coverage of Databases of Leases (Office, Retail, Industrial, Residential, Hotels, Alternatives)
Proportional Coverage of Databases of Property Transactions (Office, Retail, Industrial, Residential,
Hotels, Alternatives)
Stringency of Accounting Standards
Level of Detail in Financial Statements
Frequency of Financial Statements
Availability of Financial Reports in English
Manager Compensation and Incentives
Use of Outside Directors and International Corporate Governance Best Practice
Alignment of Interests / Shareholder PowerFree Float Share of the Public Real Estate Market

23

Indices[8] Regulation[9] Land andProperty
Registration[10] Eminent
Domain /Compulsory
Purchase[11] Real
Estate Debt
Information[12] Sales
Transactions[13] Occupier
Services

Constituent Factors
Extent to which the Tax Code is Consistently Applied for Domestic Investors
Extent to which Real Estate Tax Rates are Predictable for Domestic Investors
Extent to which the Tax Code is Consistently Applied for Foreign Investors
Extent to which Real Estate Tax Rates are Predictable for Foreign Investors
Existence of Land Use Rules and Zoning
Predictability of Changes in Land Use and Zoning
Enforcement of Land Use Rules and Zoning
Existence of Building Codes and Safety Standards for Buildings
Enforcement of Building Codes and Safety Standards for Buildings
Simplicity of Key Regulations in Contract Law
Efficiency of the Legal Process
Level of Contract Enforceability for Domestic Investors
Level of Contract Enforceability for Foreign Investors
Existence of Land Registry
Accessibility of Land Registry Records to Public
Availability of Title Insurance
Accuracy of Land Registry Records
Completeness of Land Registry Records on Ownership
Completeness of Public Records on Transaction Prices
Completeness of Public Records on Liens and Easements
Notice Period Given for Compulsory Purchase
Fairness of Compensation to Owners in Compulsory Purchase
Ability to Challenge Compulsory Purchase in Court of Law
Existence and Length of Time Series on Commercial Real Estate Debt Outstanding
Existence and Length of Time Series on Maturities and Originations of Real Estate Loans
Existence and Length of Time Series of Delinquency and Default Rates of Commercial Real Estate Loans
Availability of Data on Loan-to-Value Ratios for Commercial Real Estate Loans
Availability of Data on Margin Rates for Commercial Real Estate Loans
Requirements for Lenders to Monitor Cash Flows and Collateral Value of Property with Loan Facilities
Requirements for Lenders to Carry Out Real Estate Appraisals
Penalties for Non-Compliance with Requirements
Quality and Availability of Pre-Sale Information
Fairness of the Bidding Process
Confidentiality of the Bidding Process
Professional and Ethical Standards of Property Agents
Enforcement of Professional and Ethical Standards of Property Agents
Availability of Professional Third-Party Facilities and Project Management Companies
Providers of Property Management Services Known to Occupiers
Service Expectations for Property Management Clear to Occupiers
Alignment of Occupier and Property Manager Interests
Frequency of Service Charge Reconciliation
Accuracy and Level of Detail in Service Charge Reports
Ability for Tenants to Audit Landlord's Accounts and Challenge Discrepancies

Source: JLL

24