The purpose of this paper is to provide new empirical evidence on the important role of market transparency in international real estate investment.
The authors apply the augmented panel regression method (or the correlated random effects approach) by using national panel data from 44 countries from 2004 to 2016.
Countries with better accessibility to market information and higher enforceability of regulations have less information asymmetry and attract more inward real estate investment. In contrast, the accounting quality of 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.
Countries lacking market transparency can increase inward investments by providing richer market information to foreign investors and by boosting enforceability of regulation to mitigate the uncertainty of returns on investment. Investors and public sectors in countries facing a saturated real estate market may expand investment by investigating less-explored markets and by seeking bilateral negotiations to secure higher predictability of return on investment in targeted countries.
The authors utilize updated multiple transparency indices instead of a conventional aggregate index to examine how the investment is attributed to different aspects of market transparency and employ the augmented panel regression method for investigation of the intra- and international determinants of the investment.
Sadayuki, T., Harano, K. and Yamazaki, F. (2019), "Market transparency and international real estate investment", Journal of Property Investment & Finance, Vol. 37 No. 5, pp. 503-518. https://doi.org/10.1108/JPIF-04-2019-0043Download as .RIS
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Copyright © 2019, Emerald Publishing Limited
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. Starting from La Porta et al. (1997), who demonstrated the significance of the relationship 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; Jappelli et al., 2005; Galindo and Micco, 2004) have accrued. A large number of such studies on the relationship between legal systems and economic 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 and efficient land use. Recent studies suggest the importance of the transparency of the real estate market in inward real estate investment (Adair et al., 2006; Eichholtz et al., 2011; Falkenbach, 2009; Farzanegan and 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 the study of Levine (2005), the determinants of investment should be carefully examined considering various 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 in international 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 α, between internationally operating real estate companies and domestic real estate companies focusing on local markets from 1996 to 2007. They find that international 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 real estate 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 find that higher transparency is associated with greater investment. Lieser and Groh (2014) collect various socioeconomic and institutional variables across countries covering the period between 2000 and 2009 and conduct an augmented 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 the legal framework, ease of administrative burdens and sociocultural challenges and political stability. Other studies examine the relationship between market transparency and aggregate FDI (Drabek and Payne, 2002; Seyoum and Manyak, 2009; Egger and Winner, 2003). Farzanegan and Fereidouni (2014) analyze panel data for 32 countries between 2004 and 2010 and find that the country fixed effects do not show a statistically significant 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 of inward 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, such as 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 in property services and investment management. This paper differs from other studies using the transparency index by JLL and LaSalle Investment Management (Eichholtz 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 the augmented panel data method (or “correlated random effects approach” as in Wooldridge, 2015) for intra- and international investigations of the determinants 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 size and growth, being constant. In particular, better accessibility to fundamental information on the real estate market and higher enforceability of real estate-related regulations are associated with greater inward investment. However, the accounting quality of corporate governance is negatively correlated with investment, implying that investors prefer investing in real estate in countries with poor accounting quality, which generates greater excess returns for real estate investment. We also find that investment is positively correlated with higher house price growth and 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, the empirical model and results are demonstrated. Finally, the last section draws the conclusions.
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: inward commercial real estate investment, real estate market transparency indices and a set of control variables selected from those used in the study of 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 estate advisory company, Cushman & Wakefield (hereinafter, C&W). As mentioned in the study of Lieser and Groh (2014), the data provided by C&W are considered the highest quality database on international real estate investment. In particular, we use country-level panel data on inward commercial real estate investments as a dependent variable in our analysis. The same database was used in the study of 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. The investment was increasing until the start of the rapid decline due to financial crisis, and then it gradually increased, reaching almost the level prior to the crisis by 2015.
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. In 2016, JLL published a report on the ninth edition of the Global Real Estate Transparency Indices (GRETI), which measures the real estate market transparency in different countries and is constructed based on a survey of 139 constituent factors. We were provided with the biennial panel data from 2004 to 2016 of the composite 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 AII 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, with a 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 less transparent from 2012 to 2014, and (11) Real estate debt information became less transparent after the financial crisis. These indices indicate degrees of transparency, not strictness of regulations or difficulties of transactional procedures. Therefore, a higher score of (8) Regulation, for instance, indicates lower transparency in and/or weaker enforceability of regulation, not a tighter control on regulation.
The combination of these two data sets on investment and market transparency generates unbalanced panel data of 44 countries for a maximum of seven periods (biennial from 2006 to 2014). Figure 3 shows scatter plots of real estate investments against market transparency across countries, where the vertical axis is the time-average real estate investment in logarithmic value and the horizontal axis is the time-average market transparency Composite score. We can observe a clear negative correlation between these variables: the lower the Composite score (i.e. the more transparent the real estate market in a country), the greater the real estate investment is toward the country. Since the volume of real estate investment is not determined solely by market transparency, we need to consider various factors, such as economic size and regulatory strictness, to extract a partial correlation between investment and market transparency.
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 collect all desired variables due to limitations of data accessibility. To retain sufficient numbers of observations in our analysis, we selected two sets of control variables. One set of variables, denoted by Xa, incudes those that are available between 2004 and 2016 for more than 85 percent 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 percent of 43 countries.
We also gathered three additional variables not used in the study of Lieser and Groh (2014) that are expected to influence the inward real estate investment: namely, interest rates, house price growth rates and value added in the service sector per urban land area. 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 is often 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 the impact on the investment, the number of countries for which the data were available was not sufficient, and thus, the money market interest rates were used. The expectation of house price growth may increase the inward investment because the demand for goods/services expands with the number of higher-income consumers in the region and because the appreciation of invested real estate asset value increases collateral value to make further investments. Finally, 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 real estate 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 the study of Lieser and Groh (2014), and the three additional variables are shown in Table I.
3. Empirical analysis
3.1 Estimation model
As with Lieser and Groh (2014), the following augmented panel regression model will be estimated by random effect estimation:
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 cross-border correlations due to differences in the levels of variables and investments across countries.
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 II); then, the two sets along with three additional variables are added separately (Table III). Lastly, the result using 13 transparency subindices is described (Table IV).
Table II describes the results using Composite score as an indicator for market transparency. The first two columns show the within estimates and between estimates of a model using Xa as control variables. The between estimate of the Composite score is −1.331, which is statistically significant at the 1 percent level, implying that a country with a one-point higher time-average Composite score is associated with a lower investment by approximately 74 percent (=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 and enjoyed 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 the non-significance of the within estimate in the sample period of 2004–2016. This explanation is in line with the study of Farzanegan and Fereidouni (2014), who show that the within effect of transparency on FDI inflows to the real estate sector is statistically non-significant, and with Eichholtz et al. (2011), who show that the excess returns between international and domestic real estate companies disappear in the later years of their study period.
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.
The last two columns in Table II show the results using Xb, which contains variables regarding costs/difficulties of purchasing, registering, starting and ending investment procedures in addition to Xa. Although these cost-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 result implies that the Composite score is positively correlated with the cost-related variables, attenuating the coefficient of the Composite score. The estimation results of both models assure the positive relationship between the volume of inward investment and market transparency across countries.
Now, we include additional variables: interest rate, house price growth rate and service-sector value added per urban land area. Because of data limitations, we run three regressions using each of additional variables separately along with Xa and the Composite score as explanatory variables to retain sufficient numbers of observations. Table III shows the estimation results of the coefficients for the Composite score and additional variables. The results of the coefficients for Xa are omitted from the table. In general, a decline in interest rate in a country makes it easier for investors to borrow and boosts investments, by which the within estimate for interest rate is expected to show a negative sign. However, the coefficients are not statistically significant. This result may be attributed to the fact that investors finance cross-board investments in their own countries to some extent, reducing the significance of the correlation between investment and interest rate in host countries. Regarding house price growth, the within estimate is positive and significant at the 10 percent level. As the house price appreciates, investors increase investment, expecting future expansion of market demand and increase in the collateral values of real estate. The value added of the service sector per land area can be interpreted as the land productivity of the service sector. The between estimate shows a negative sign at the 10 percent significance level. This sign implies the possibility that investors invest in countries with a greater potential for increasing demand in the market.
Finally, Table IV 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.
(1) Direct property indices, composed of six constituent factors, measures the accessibility to fundamental information on the real estate market and performance in the targeted country. The positive estimated coefficient suggests that the higher accessibility to and transparency of the fundamental information on the real estate market in the targeted country reduces the information asymmetry between investees and investors across countries, resulting in large real estate investment toward the country.
(8) Regulation, composed of 13 factors, measures the availability, enforceability and predictability of various real estate-related regulations in a country. The negative coefficient for Regulation suggests that a high transparency of such factors can reduce the uncertainty and risk of investment and thereby attract foreign investors.
(7) Corporate governance, composed of four factors, reflects the audit quality of cooperate governance. Among the above three indices with significant signs, only Corporate governance shows a positive correlation with investment: the higher the index is (i.e. the lower the audit quality of corporate governance), the larger the investment, implying that investors 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, the measure of accessibility and accountability 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 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).
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 real estate market and higher enforceability and predictability of real estate-related regulations are strongly associated with larger inward investment. These factors may attract foreign investors by reducing the asymmetric information between investees and investors across countries. However, we find a negative relationship between the accounting quality of corporate governance and investment. The increase in excess returns for real estate may outweigh the issue of information asymmetry that is generated when corporate governance lacks accountability in a targeted country. 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 the market.
Overall, the coefficients for market transparency show significant signs only in terms of between estimates, not within estimates. This result indicates that the countries with large inward real estate investment had facilitated market transparency from an early period that this study did not cover. These countries could have been immune to foreign competition and enjoyed large inward investments from the early period and 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. The results in this paper suggest that, although it may take considerable effort and some time, countries lacking transparency can increase inward investments by two means: consolidating real estate market information to be provided to foreign investors and boosting the enforceability of regulation to mitigate the uncertainty of returns on investment. In particular, constituent factors of (1) Direct property indices and (8) Regulation, listed in Table AII, can serve as effective topics for the government to work on to enhance the potential for growing inward investments. The results also imply that investors and public sectors in countries facing a saturated real estate market may find an opportunity to expand investment in other countries by investigating less-explored markets and by seeking bilateral negotiations to secure a higher predictability of returns on investment in targeted countries. Finally, from a social welfare perspective, leveling the gap of market transparency is essential to promote efficiency in the global market of real estate investment.
|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$K)||273||0.98||0.94|
|Real GDP growth (%)||273||0.34||0.45|
|Unemployment rate (%)||273||7.43||4.65|
|CPI (consumer price index) growth (%)||273||3.28||3.38|
|Urban population (% of total population)||273||72.76||15.88|
|Telecommunication (Fixed telephone subscription per 100 people)||273||35.78||16.80|
|Domestic credit provided by banking sector (% of GDP)||273||128.67||64.75|
|FDI net flows (US$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 to 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 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|
Sources: Cushman & Wakefield, JLL and LaSalle Investment Management, International Monetary Fund, World Bank
Estimation results with Composite score
|Ln(GDP per capita)||−0.156||−0.661**||−0.995||−0.692**|
|Real GDP growth||0.163||1.056*||0.213||1.502**|
|Domestic credit provided by banking sector||−0.003||0.003||−0.003||0.002|
|FDI net flows||0.000||0.005*||0.000||0.002|
|Political stability and absence of violence||0.001||0.006||0.011||−0.014*|
|Marginal corporate tax rate||0.096**||0.019**|
|Profit and capital gains tax||−0.084*||0.019|
|Cost to register property||−0.070||−0.078**|
|Procedures to register property||−0.006||−0.243**|
|Time needed to register property||0.007**||0.010**|
|Procedures to start a business||−0.034||0.145**|
|Time needed to start a business||−0.010||−0.009|
|Cost of business start-up procedures||−0.001||−0.062**|
|Minimum capital needed to start a business||0.001||0.001|
|Time needed to resolve insolvency||0.068||−0.314**|
|Cost of resolving insolvency||0.014||−0.034**|
|Recovery rate from insolvency||0.019||−0.002|
|TI (Composite Score)||0.003||−1.331**||−0.240||−0.868**|
|Number of countries||44||43|
|Number of parameters||28||51|
Notes: *,**,***Significant at 0.05, 0.01 and 0.1 percent levels, respectively
Estimation results with additional variables
|Interest rate||House price growth (% 2-year average)||Ln(service-sector value added/area)|
|TI (Composite)||−0.032 (0.346)||−1.262** (0.288)||−0.065 (0.446)||−1.239* (0.563)||−0.363 (0.355)||−1.378** (0.357)|
|Additional var.||−0.025 (0.025)||0.014 (0.038)||0.018*** (0.005)||0.070 (0.040)||−1.794 (1.201)||−0.376*** (0.213)|
|No. of countries||41||32||40|
|No. of parameters||30||30||30|
Notes: Numbers in parentheses are robust standard errors. Coefficients for control variables, Xa, are not shown in the table. *,**,***Significant at 0.05, 0.01 and 0.1 percent levels, respectively
Estimation results with 13 transparency subindices
|Transparency index (TI)|
|(1) Direct property indices||−0.030||−0.690**||−0.028||−0.767**|
|(2) Listed real estate securities||−0.468||−0.611**||−0.052||−0.252|
|(3) Unlisted fund indices||−0.245||0.007||−0.114||0.036|
|(5) Fundamentals data||−0.291||−0.137||−0.078||−0.041|
|(6) Financial disclosure||0.077||0.405**||0.126||0.230|
|(7) Corporate governance||−0.137||0.870**||0.058||0.726**|
|(9) Land and property registration||−0.06||−0.613**||0.001||0.339|
|(10) Eminent domain||0.020||0.521**||−0.117||−0.113|
|(11) Real estate debt regulation||0.171||−0.212***||0.131||−0.049|
|(12) Sales transactions||−0.177||0.195||−0.214||0.958**|
|(13) Occupier services||0.058||−0.178||−0.057||0.399|
|No. of countries||44||43|
|No. of parameters||52||75|
Notes: Coefficients of control variables, Xa and Xb, are not shown in the table. **,***Significant at 0.01 and 0.1 percent levels, respectively
Basic statistics on investment and market transparency by countries
|Commercial real estate investment (US$ B)||Market transparency (Composite score)|
13 transparency subindices and 139 constituent factors
|(1) Direct property indices||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 series|
|Size of institutional invested real estate market|
|Market coverage of direct property index|
|(2) Listed real estate securities indices||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|
|(3) Private real estate fund indices||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|
|(4) Valuations||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|
|(5) Market fundamentals data||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)|
|(6) Financial disclosure||Stringency of accounting standards|
|Level of detail in financial statements|
|Frequency of financial statements|
|Availability of financial reports in English|
|(7) Corporate governance||Manager compensation and incentives|
|Use of outside directors and international corporate governance best practice|
|Alignment of interests/shareholder power|
|Free float share of the public real estate market|
|(8) Regulation||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|
|(9) Land and property registration||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|
|(10) Eminent domain/compulsory purchase||Notice period given for compulsory purchase|
|Fairness of compensation to owners in compulsory purchase|
|Ability to challenge compulsory purchase in court of law|
|(11) Real estate debt information||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|
|(12) Sales transactions||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|
|(13) Occupier services||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|
Please refer to the C&W website (www.cushmanwakefield.com/en) for details on the company and the data of international real estate investment.
Figure 1 was constructed in the following manner. In the following equation:
Please refer to the JLL website (www.us.jll.com/en) for details on the company and the data of the transparency indices.
The latest version of GRETI, published in 2018, is based on 186 factors with an additional index, Sustainability.
Basic statistics of real estate investments, Composite score, and number of observations by country are described in Table AI.
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 all available row variables along with transparency indices, for the main purpose of this paper is to examine the relationship between the market transparency and the volume of investment.
The positive sign of the between estimate for interest rate reflects differences in capital accumulations by countries, where a higher interest rate implies a lower capital intensity within country, reflecting a higher marginal product of capital. Such countries with scarce capital stock have a greater potential to grow economically and therefore attract inward investments for new buildings and infrastructure.
In Table IV, some between estimates show significant signs either in the model using Xa or in the model using Xb but not in both. This result implies that these indices are correlated with additional variables in Xb or that the impacts are not statistically robust. In this paper, we focus on the three indices that show significant impacts regardless of the choice of control variables to interpret results that can be concluded with conviction.
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.
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.
The four factors comprising Corporate governance include manager compensation and incentives, use of outside directors and international corporate governance best practice, alignment of interests and shareholder power, and free float share of the public real estate market.
Factors comprising Financial disclosure include stringency of accounting standards, level of detail in/frequency of financial statements, and data disclosure by companies.
The between estimate of  Sales transactions is also positive and significant but only in the model using Xb. This subindex is expected to have both positive and negative impacts on investment because the higher transparency in sales transaction is associated with less information asymmetry and better accounting quality. The inclusion of the additional variables in Xb may have controlled for the positive impact and resulted in picking up the negative correlation (i.e., showing the positive sign).
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The authors thank Jones Lang LaSalle Incorporated for providing valuable data and Fumio Shinohara for research assistance. This work is supported by the Center for Global Studies on Culture and Society Grant, Nihon University.