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Real estate market transparency and default on mortgages
Research in International Business and Finance 53 (2020) 101202
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Real estate market transparency and default on mortgages Hassan F. Gholipour*, Reza Tajaddini, Thi Ngoc Tram Pham
T
Swinburne Business School, Swinburne University of Technology, Melbourne, Australia
ARTICLE INFO
ABSTRACT
JEL classification: G21 G28 R30
This paper investigates the relationship between real estate market transparency (RET) and default on mortgages (DOM). Using data from 46 countries for the period of 2006–2016, we find evidence that there is a negative and significant relationship between RET and DOM. This result is robust with the inclusion of control variables and different estimation methods including panel fixed effects and generalized method of moments (GMM). We also find that the association between RET and DOM is more apparent in emerging economies than high-income countries.
Keywords: Real estate transparency Default rate Mortgage Panel data
1. Introduction Jones Lang LaSalle1 (JLL, 2016) defines a transparent real estate market as an open and clearly organized market operating in a legal and regulatory framework that is characterized by a consistent approach to enforce rules and regulations. Respect to private property rights and high ethical and professional standards are also included in JLL’s definition of real estate transparency (RET). RET has improved steadily at the international level over the last decade. New legislation, higher ethical standards, the availability of quality data and more transparency have helped the progress of the real estate industry (JLL, 2016)2 . According to a Global Real Estate Transparency report (JLL, 2016), between 2004 and 2016, the real estate transparency score increased with an average growth rate of 4.5 percent (see Fig. A1 in Appendix A). The improvements in transparency have been mainly driven by three factors: (1) the globalization of the real estate market; (2) the significant growth of capital allocations to real estate; and (3) technological development in areas of performance measurement, market fundamentals and valuation practices. Promoting a culture of “open data” by governments and businesses, especially after the Global Financial Crisis (GFC), has also contributed to higher real estate market transparency (JLL, 2014, 2016). In recent years, the relationships between RET and real estate market activities, such as investments, property development and property occupation, have gained notable attention (e.g. Farzanegan and Gholipour, 2014; Gholipour, 2013; Gholipour and Masron, 2013; JLL, 2006, 2010, 2016; Newell, 2016). This paper extends the literature by investigating whether RET has any significant relationship with defaults on mortgages (DOM). We concentrate on DOM because of the recent rise in DOM and foreclosures in many
Corresponding author. E-mail addresses: [email protected] (H.F. Gholipour), [email protected] (R. Tajaddini). 1 Jones Lang LaSalle is a financial and professional services firm specializing in real estate services and investment management. 2 While there is a general improvement in RET, the level of progress is not consistence across all countries. For example, Romania witnessed the largest improvement in RET in 2008 due to joining to the European Union. Whereas, Austria experienced a sharp decline in the index in 2012 because of low quality of real estate data provision (JLL, 2008; JLL, 2012). For more details about the fluctuations of the RET index of each country please refer to JLL’s annual reports (http://greti.jll.com/greti). ⁎
https://doi.org/10.1016/j.ribaf.2020.101202 Received 17 June 2019; Received in revised form 16 September 2019; Accepted 13 February 2020 Available online 13 February 2020 0275-5319/ © 2020 Elsevier B.V. All rights reserved.
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advanced economies, especially the US, where this contributed to the 2008–2009 GFC (Tajaddini and Gholipour, 2017). On the other hand, there is growing recognition by governments, particularly in emerging economies, that poor RET not only hinders inward foreign real estate investment, but also substantially impacts the quality of life of countries’ citizens and their relationship with local government (JLL, 2014, 2016). Given the adverse consequences of deficient RET, many governments have embraced the policy of open data and increased their expenditures on technological innovation that can boost transparency (JLL, 2014). Nevertheless, there is some evidence that suggests transparency in some forms may have negative side effects3 . In the real estate context, Pavlov et al. (2016) look at the impact of transparency in the mortgage market. The authors argue that as lenders become more aware of the geographical risk of each mortgage as a result of a higher transparency, they may withdraw credit from regions that experience local negative shocks. This withdrawal magnifies the price impact of the original income shock and increases real estate price volatility. Farzanegan and Gholipour (2014) find that higher levels of real estate transparency do not contribute to the expansion of foreign real estate investment in emerging and developed economies. JLL (2008, 2010) also provide evidence that high levels of transparency neither eliminate risks for investors nor guarantee strong investment returns. Given the different views on the link between transparency and outcomes in real estate markets, it would be interesting to empirically examine the effectiveness of governments’ attempts to boost RET on non-performing mortgages. As can be seen from Fig. A2 in Appendix A, there is a strong and negative correlation between growth of RET and DOM (r = - 0.78). However, in order to statistically judge the strength of association between these variables, we need to control for other time-varying economic factors (e.g. household income and house prices) in addition to time constant and regional specific factors (e.g. cultural values (Tajaddini and Gholipour, 2017)). Our study contributes to the literature on the linkage between institutional quality and non-performing loans (NPLs). Although there have been some studies on the effect of institutional variables (e.g. corruption) on NPLs (e.g. Breuer, 2006; Boudriga et al., 2009; Ghosh, 2019; Goel and Hasan, 2011), to the best of our knowledge, no empirical study has examined the relationship between institutional variables related to the real estate market, in particular RET, and DOM across countries. We use international data from 46 countries for the period of 2006–2016 to examine this relationship. Our main findings from panel regression analysis confirm that countries with higher degrees of transparency in their real estate markets have lower rates of DOM. The paper proceeds as follows: Section 2 briefly summarizes the relevant literature and develops the research hypothesis; Section 3 explains the data and model specification; Section 4 presents and discusses the empirical results; and Section 5 concludes the paper. 2. Literature review and hypothesis development In this section, we present rationales on how RET may influence DOM. Our paper most closely relates to the literature examining the effects of institutional quality on NPLs. Previous literature shows that a country’s institutional factors greatly matter for NPLs. For example, Goel and Hasan (2011) find that greater corruption is associated with higher NPLs. They argue that monitorial and enforcement institutions are particularly weaker in countries with higher degrees of corruption. This may imply a lack of a proper antifraud monitoring system or more lenient punishment against corrupt activities. Breuer (2006) provides evidence that, in addition to macroeconomic factors and banking institutions, corruption and ethnic fractionalization increase problem loans in the banking system. More specifically, he argues that more self-interested activities can be observed in countries with higher levels of corruption. Furthermore, self-interested behaviors and proclivities affect the principal-agent relationships between banks and borrowers and may also increase problem loans. Breuer (2006) also observes that the potential for conflicts of interest is higher in countries with more ethnically heterogeneous populations. Zheng et al. (2013) find that cultural values also influence corruption in bank lending. They suggest that, in countries with high scores of collectivism, the interdependence between bank officers and customers leads to higher levels of lending corruption. Boudriga et al. (2009) show that strong political institutions, control of corruption and higher levels of democracy increase the effectiveness of banking supervision and consequently reduce credit risk exposure and problem bank loans. Mankiw (2016; pp. 388) also argues that the high default rates on bank loans during the Asian financial crisis (1997–1998) was mainly due to fact that many Asian banks (that were managed by governments) had been extending loans to those with the most political clout rather than to those with the most profitable investment projects. In this study, we extend the growing literature on the association between institutional quality and NPLs by empirically investigating the relationship between RET (an institutional quality related to the real estate market) and DOM. We point out a number of mechanisms through which RET might affect the DOM. First, one of the primary reasons is weak mortgage underwriting process. In countries with higher levels of RET, central banks and financial regulators implement stricter rules on housing financing and monitor mortgage lenders more closely, which possibly reduces the probability of DOM. In these countries, financial institutions have better access to the financial histories of mortgage applicants and also require them to provide more detailed documentation to verify their financial credibility. The rapid growth of technological innovations has enabled mortgage lenders to assess the ability-to-pay characteristics of borrowers (e.g. income, unemployment and history of previous loan re-payment). Therefore, borrowers with unsatisfactory credit scores may not be able to get mortgages, which in turn reduces the probability of defaults. In addition, in countries with higher levels of RET, there are fewer chances for granting corrupt loans (Barth et al., 2009) to deceitful and unreliable mortgage applicants with relatively higher odds of 3 For the insurance industry, Hirshleifer (1971) shows that transparency can be harmful for insurance opportunities. That is, if the actors of an insurance contract know about each other’s risk in a transparent system, they may decide to not insure each other.
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defaulting. Higher RET also attracts quality foreign real estate investors (JLL, 2010) who are often looking for second homes or holiday houses. As foreign investors are under more scrutiny and monitoring by lenders, the chances of their defaults on mortgages are expected to be lower. Second, in more transparent countries, property agents and lenders are more likely to follow the professional and ethical standards and more willing to work with borrowers towards a better solution than defaulting in difficult situations. As shown by Seiler et al. (2012), borrowers who do not know basic information about their loans and are also frustrated with their lenders are more likely to default on their mortgages. However, one may argue that the recent US experience shows that complex mortgages confused many borrowers and contributed significantly to mortgage default, even though the US has the highest RET score. Since our sample contains a large number of developed and emerging economies, the former argument may be more dominant. Finally, greater data availability (both historical and expected) on house prices at suburb and regional levels may help homebuyers in forming a more accurate evaluation of their home ownership choices. Homebuyers may gain a better understanding about the ways that their investment choices would impact their future wellbeing. Accordingly, this may limit the chance of irrational or risky investments with a higher probability of default. In short, we propose that higher RET limits the chance of DOM occurrence. That is because lenders in countries with high RET scores usually have better scrutiny and monitoring processes in place and provide higher levels of professional support to borrowers. In these countries mortgage borrowers have also access to more accurate information that helps them in their investment decisionmaking process. Based on the above justifications, we hypothesize that DOM is negatively correlated with RET. 3. Data and methodology 3.1. Data We utilize an unbalanced panel data to explore the relationship between RET and DOM. We use biannual data from 46 countries for 2006, 2008, 2010, 2012, 2014 and 2016. The start year of our sample is 2006 due to the slight changes JLL made to their methodology of index calculation, revising their questionnaires and adding new questions in that year (JLL, 2006, 2014). Therefore, we do not include 2004 and pre-2004 RET index in our analyses because the measurements of indices are not strictly comparable to the 2006–2016 indices. Our sample covers all those countries for which data on RET and DOM are obtainable. The country sample can be found in Table 1. The dependent variable for our analysis is the logarithm of non-performing housing mortgage rate as a measure for DOM. Information on non-performing housing mortgage rate is obtained from Euromonitor International. This is defined as a loan 90 days delinquent where full payment can no longer be expected. It refers to non-performing housing loans as a percentage of outstanding Table 1 Average mortgage default rates and RET index for sample countries (2006-2016). Source: Euromonitor International, Consumer Lending & JLL, LaSalle Investment Management Countries
DOM (%)
RET
Countries
DOM (%)
RET
Argentina Australia Austria Brazil Canada Chile China Colombia Czech Denmark Egypt France Germany Greece Hong Kong Hungary India Indonesia Israel Italy Japan Malaysia Mexico Morocco
3.93 0.63 2.45 2.87 0.35 4.27 0.52 3.17 2.50 1.33 5.45 2.55 2.80 18.57 0.03 8.12 1.53 2.77 1.10 3.60 0.78 3.73 3.63 7.17
1.70 3.74 3.04 2.14 3.67 1.96 1.73 1.10 2.70 3.21 1.26 3.59 3.35 2.21 3.31 2.57 1.84 1.78 2.38 2.96 2.71 2.72 2.17 1.34
Netherlands Norway Philippines Poland Portugal Romania Russia Saudi Arabia Singapore South Africa South Korea Spain Sweden Taiwan Thailand Turkey United Arab Emirates Ukraine United Kingdom USA Venezuela Vietnam
0.43 1.15 8.87 2.30 2.13 11.97 1.47 3.65 1.35 9.28 0.43 3.02 2.20 1.30 4.42 2.85 5.52 20.00 1.73 5.30 1.50 1.20
3.58 3.06 1.97 2.82 2.78 2.07 1.93 1.37 3.30 2.95 2.06 3.10 3.44 2.34 2.01 1.86 1.71 1.57 3.73 3.75 0.77 0.99
Note: DOM is default rate (in percentage). The real estate transparency (RET) index ranges between 1 and 5. A country with a perfect score of 1 would be the country with the highest level of transparency. A country with a score of 5 would be a country with total opacity. In order to facilitate the interpretation of this index, we have reversed the scores (1: total opacity and 5: the highest level of transparency). 3
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balance. Table 1 shows the average of mortgage default rates for the sample countries for the period of 2006–2016. The average value for our sample ranged from 0.03 % (Hong Kong) to 20 % (Ukraine) where a higher percentage indicates a higher rate of DOM. Data for the real estate transparency (RET) index is obtained from the LaSalle Investment Management of Jones Lang LaSalle (JLL). Table 1 shows the average RET index for sample countries for the period of 2006–2016. The United States (1st), the United Kingdom (2nd) and Australia (3rd) are the most transparent markets among our sample countries while Venezuela and Vietnam are at the bottom. Some of the main characteristics of transparent real estate markets are rigorous regulations and legal enforcement, a high degree of institutional real estate ownership, high ethical professional standards, quality market fundamentals research, more public and private performance indices and fair transaction processes. The JLL’s RET index is based on a combination of quantitative market data and information gathered through a survey of the global business network of JLL and LaSalle Investment Management across more than 100 markets. The index has been updated every two years since 1999 and aims to help real estate investors, corporate occupiers, retailers and hotel operators understand important differences when transacting, owning and operating in foreign markets. It is also a helpful gauge for governments and real estate industry organizations who are seeking to improve transparency in their home markets (JLL, 2014) (2016). For each country, JLL uses both quantitative data points and survey questions to calculate the RET scores. The survey data and quantitative measures complement each other. For example, knowing the market coverage and length of a country’s direct real estate index does not provide a full picture, JLL also gathers qualitative data on whether investors actually trust and use the index. JLL’s local research teams, in consultation with business leaders and real estate professionals active in each market, complete the survey. The RET index ranges on a scale from 1 to 5. A country or market with a perfect 1 score has total real estate transparency; a country that scores 5 has total real estate opacity. In order to facilitate the interpretation of this index, we have reversed the scores (1 equals total opacity and 5 equals the highest level of transparency). It should be noted that there are at least two limitations concerning the RET data. Firstly, the JLL’s RET index does not purely focus on residential real estate transparency but includes offices, retail, industrial, hotels and residential properties for developing its RET index. Secondly, JLL did not survey every country from our sample over the period of our study and therefore our panel data are unbalanced. Besides the variable of interest (RET), the relevant theoretical and empirical studies suggest that some macroeconomic and institutional variables are associated with DOM. Based on the ability-to-pay hypothesis and strategic-default hypothesis4, household income, unemployment and changes in house prices are selected as other explanatory variables in the model specification. We expect a negative relationship between income, increases in house prices and DOM, as well as a positive relationship between unemployment and DOM (e.g. Guiso et al., 2013; Seiler et al., 2012). We use gross domestic product (GDP) measured at purchasing power parity (PPP) per household (in logarithm) and unemployment rate (unemployed population as a percentage of the labour force) as measures of income and unemployment, respectively. As a measure for the changes in house prices, we use the logarithm value of Euromonitor’s Index of Housing Prices (2010 = 100). The house price index reflects changes in the prices of residential dwellings in a country5 . We also include another three explanatory variables of non-performing loans in our estimations: income inequality (suggested by Kumhof et al., 2015), quality of institution (suggested by Breuer, 2006; Boudriga et al., 2009; Goel and Hasan, 2011) and financial market development (suggested by Boudriga et al., 2009). We use logarithm of Gini index, the average of six governance indicators of the World Bank6 and domestic credit to private sector by banks (% of GDP) as proxies for income inequality, quality of institutions and financial market development, respectively. The data for all variables are obtained from Euromonitor International. The descriptive statistics of all variables are provided in Table 2. Table A1 presents the correlation matrix and centred Variance Inflation Factors (VIFs)7 . Given the advantage of using panel data to reduce collinearity amongst the variables (Baltagi, 2008), all of the explanatory variables have VIF less than threshold of 10. 3.2. Model Based on the above discussion, the empirical model we use is as follows: DOMit = α + β1 RETit + β2 INCit + β3 UEMit + β4 HPit + β5 GINIit + β6 CRETit + β7 GOVit + vi + Ωt + uit
(1)
where DOM is default rate; RET stands for the real estate transparency index (RET); INC is GDP measured at purchasing power parity 4 According to the ability-to-pay hypothesis, borrowers default on their mortgages when they face a dramatic, negative economic event, such as sudden unemployment, divorce, prolonged illness, or emergency medical care. The strategic-default theory argues that borrowers default because the values of their properties have declined to a level far below the outstanding balance on their loan (Jackson and Kaserman, 1980; Tajaddini and Gholipour, 2017). 5 Another possible channel that RET may affect DOM is through house price volatility. One may argue that higher house price volatility could lead to higher mortgage default, and it is reasonable to hypothesize that lower RET is associated with higher house price volatility. The similar argument can be done for income volatility. However, our empirical results do not support these channels. We included interaction terms between RET and HP as well as RET and INC in the regressions. The coefficients of interaction terms were insignificant, meaning that RET, HP and INC have their own independent impact on DOM. 6 Six dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. For details, see http://info.worldbank.org/governance/wgi/#home. 7 The VIFs are a method of measuring the level of collinearity between the regressors in an equation.
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Table 2 Descriptive statistics. Variables
Mean
Median
Max
Min
Std. Dev.
DOM RET INC UEM HP GINI CRET GOV
3.82 2.47 89,065.29 0.073 108.93 0.39 0.81 0.53
2.40 2.47 75,981.35 0.06 100.00 0.39 0.74 0.64
41.00 5 569434.30 0.26 461.80 0.64 2.33 1.87
0.03 0.31 13,828.10 0.007 44.70 0.25 0.11 −1.50
5.23 0.87 68,518.12 0.04 35.19 0.07 0.45 0.84
Notes: DOM is default rate (in percentage), RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP) and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). Table 3 Results of panel fixed-effect regression (full sample). Dependent variable: DOM Independent variables RET INC
(1) −1.333** (0.556)
(2) −0.259** (0.102) −0.393 (0.413) 1.323 (1.328) −0.597*** (0.171) 2.541*** (0.841) 0.167 (0.157) −0.540* (0.328) Yes Yes 0.891 0.860 27.80*** 40.02*** 212
UEM HP GINI CRET GOV Country fixed Period fixed R-squared Adj R-squared F-statistic Hausman Test (Chi-Sq. Statistic) Observation
Yes Yes 0.673 0.601 9.147 12.51** 273
(3) −0.235** (0.098) −1.423* (0.854) 2.059 (1.290) −1.354*** (0.352) 2.374*** (0.852) 0.134 (0.161) Yes Yes 0.893 0.861 28.088*** 32.198*** 218
Notes: DOM is default rate (in percentage), RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP), and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). The asterisks *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Robust standard errors are presented in parentheses. Constant was included in the estimation but not reported.
(PPP) per household; UEM is unemployment rate; HP represents house price index; GINI is Gini index; CRET is domestic credit to private sector by banks (% of GDP); GOV is the average of six governance indicators of the World Bank; vi captures the country fixed effect; and Ωt takes into account the time effect. i = 1,…, N denotes the country, t = 1,…, T denotes the time period and u is an error term. The country fixed effects account for all time-invariant differences across countries, such as cultural factors or geography. The year fixed effects control for factors that vary across time but are constant across countries, such as global business cycles (Baltagi, 2005). It should be noted that we employed a Hausman’s (1978) test to compare the fixed and random effects estimates of coefficients. The Chi-Sq. Statistic of the test was significant at the 5% level, indicating that fixed effects are appropriate for our models. 4. Empirical results 4.1. Main analysis Table 3 presents the results of the country and year fixed effects regressions8 . In column 1 of Table 3, only RET is included as 8
In addition to panel regressions, we also ran cross-sectional regression for each year separately. The (unreported) results indicate that there is a 5
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Table 4 Results of panel fixed effect regressions (sub-samples). Dependent variable: DOM Independent variables
Sample (2006–2008) (1)
Sample (2010–2016) (2)
High-Income Countries (3)
Emerging Economies (4)
RET
−0.102 (0.090) −0.873 (0.852) 0.960 (5.646) −0.494 (0.521) −0.549 (0.920) 1.009** (0.438) −0.539 (0.793) Yes Yes 62
−0.308*** (0.091) −0.600 (0.666) 0.151 (0.198) −0.582* (0.322) 2.232* (1.337) 0.103 (0.386) −0.913* (0.516) Yes Yes 150
0.126 (0.218) 1.241 (0.918) 0.310* (0.186) −0.655** (0.324) 3.086* (1.620) 0.116 (0.321) −1.055** (0.450) Yes Yes 142
−0.371*** (0.122) −1.302* (0.669) 0.260 (0.344) −0.414 (0.286) 4.322** (1.940) 0.005 (0.437) −0.543 (0.464) Yes Yes 70
INC UEM HP GINI CRET GOV County fixed Period fixed Observation
Notes: DOM is default rate (in percentage), RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP) and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). The asterisks *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Robust standard errors are presented in parentheses. Constant was included in the estimation but not reported.
explanatory variable for DOM. In column 2, RET and the control variables are included as explanatory variables for DOM. In column 3, we exclude GOV as a control variable because it is significantly correlated with RET (r = 0.758), although its VIFs value is less than 10 (see Table A1). Nevertheless, we focus on the results presented in column 2 of Table 3 for discussion of our results. The findings show that RET has a negative and significant relationship with DOM.9 In terms of economic magnitude, a one unit increase in the RET index (measured on a scale of 1–5) is associated with a 0.25 drop in DOM (column 2 of Table 3). This finding supports our hypothesis that countries with higher levels of real estate transparency tend to have significantly lower default rates, other things being equal. This is consistent with those of Seiler et al. (2012), showing that borrowers with limited knowledge and information about their loans are more likely to default on their mortgages. Our result also supports the body of literature that emphasizes the importance of transparency between borrowers and lenders as a determinant of banking system stability (Fischer, 1999; Giannetti, 2007). Simply put, transparency helps to limit disruptions in the credit market. Greater transparency on the quality of mortgages enables investors to evaluate credit providers’ performances. This allows investors to allocate appropriate risk premium to the credit providers. In turn, market discipline improves and over-lending problems are limited (Giannetti, 2007). As discussed in Pagano and Volpin (2012), detailed information helps market participants to assess the risk associated with structured debt. Rating agencies can use this information to estimate the probability of default and consequently provide ratings that are more sophisticated. In addition, our finding is in line with studies that highlight the importance of institutional variables in loan market performance. For example, Boudriga et al. (2009) show that the most effective ways to reduce bad loans are to strengthen the legal system, improve transparency and enhance democracy. Similarly, Breuer (2006) shows that in addition to economic determinants, institutional aspects of a country, such as corruption and the degree of ethnic heterogeneity, can influence the number of non-performing loans. In terms of control variables, the coefficient for INC is negative and significant, implying that countries with higher income per household have a lower default rate. This finding provides support for the ability-to-pay hypothesis. In line with the strategic-default hypothesis, we find that countries with increases in house prices (HP) also have lower default rates. 4.2. Analyses for sub-sample countries and sub-periods In addition to the full sample analyses, we disaggregate the sample countries based on the income level and periods. In terms of income level, we follow the World Bank’s country grouping. According to the World Bank,10 high-income economies are those with a gross national income (GNI) per capita of $12,056 or more. Therefore, we have 29 high-income countries (e.g. Australia, Germany and Japan) and 17 countries with income below $12,056 (e.g. Brazil, Malaysia and Thailand). We also divide the period of study into (footnote continued) negative relationship between RET and DOM across most of the sample period. 9 We also regressed DOM on control variables only and the (unreported) results show that the control variables keep the expected signs. 10 See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups 6
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Table 5 Panel GMM regressions results. Dependent variable: Δ DOM Independent variables
Full sample (1)
Δ DOM (-1)
0.146** (0.067) −0.109*** (0.037) 0.702 (0.573) 3.668** (1.667) −0.636*** (0.163) 0.586 (0.477) 0.172 (0.256) −0.293 (0.328) Yes Yes 0.312 0.800 173
Δ RET Δ INC Δ UEM Δ HP Δ GINI Δ CRET Δ GOV Cross-section fixed (first-differences) Period fixed-effect p-value of Sargan statistic p-value of AR(2) test Observations
Notes: DOM is default rate (in percentage), DOM (-1) is one lag of DOM, RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP) and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). The asterisks *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Robust standard errors are presented in parentheses. Constant was included in the estimation but not reported.
two sub-periods with reference to the financial crisis - 2008. This is because RET regulations were intensified after 2008 (JLL, 2014, 2016), so the results may vary by sub-periods. We run our regressions for the period of 2006–2008 and 2010–2016 separately. The results are presented in Table 4. Our findings show that RET is inversely and significantly correlated with DOM for the period of 2010–2016, whereas the link between RET and DOM is negative but not statistically significant for the period of 2006–2008, perhaps due to a restricted degree of freedom (columns 1 and 2 of Table 4). Moreover, the results of sub-sample regressions suggest that while there is a negative and statistically significant association between RET and DOM in emerging economies, there is an insignificant link between the two variables for high-income countries. This finding implies that default rates are more sensitive to progress in transparency in the real estate markets of emerging economies compared to high-income countries. 4.3. Issue of persistency of default rate and endogeneity The previous results are based on a static panel fixed effects estimation method. As a robustness check, we apply a dynamic panel model (Holtz-Eakin et al., 1988; Arellano and Bond, 1991) which is estimated by the first-difference generalized method of moments (GMM). The model is useful for our data set because the dynamic panel model is designed for panels with a large number of crosssections and a short time series. Moreover, it is likely that there exists persistence in the dynamics of default rates, such that the previous level of default rates has an influence on the current level. The other reason to use the first-difference GMM method for estimation is to minimize the potential problem of endogenous explanatory variables in Eq. (1). For example, we assume that higher RET decreases the default rate; however, it can be argued that improvements in RET may be the result of increases in default rates. In other words, since the Global Financial Crisis (which was largely caused by DOM), most governments across the world have implemented stricter regulations and legal frameworks, with a call for an increased level of financial as well as real estate market data and performance benchmarks (JLL, 2016).
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Finally, by taking the first difference of the variables in the GMM method, we also address the possible non-stationarity issue with the variables and reduce the severity of multicollinearity between the explanatory variables (Baltagi et al., 2009). In the estimation models, we include one lag of the dependent variable as a regressor and use period dummy variables to control for period fixed effects, taking the first difference of each variable in the regression to remove cross-section fixed effects. We include both internal instruments in the first-difference GMM estimation (one lag of all explanatory variables) as well as an external instrument (local real estate industry shares in national outputs as an instrument, suggested by Goldsmith-Pinkham et al., 2018). As a proxy for this variable, we use the percentage of gross value added (GVA) from real estate, renting and business activities to aggregate national GVA. Data for the variable are obtained from Euromonitor International. Two diagnostic tests are carried out on the GMM estimations. First, the Sargan test of over-identification restrictions is employed to check the validity of the instruments. The null hypothesis of this test is that the instruments are not correlated with the residuals. The failure to reject the null hypothesis indicates that the specification is correct and the instruments are valid (see bottom of Table 5). Second, we use the Arellano-Bond test for second order correlation (AR(2)) in the first differenced residuals. The test indicates that there is no serial correlation, as the p-value of this test is insignificant (see bottom of Table 5). Table 5 shows the results of GMM estimations for the full sample. Similar to the panel fixed effects regressions, the GMM estimation (column 1 of Table 5) shows that real estate transparency is an important determinant of defaults on mortgages, as the coefficient of RET (β = -0.109) is negative and significant at the 1% level. We also find that previous DOM has a positive impact on current DOM in the sample countries, as the coefficient of DOM(-1) is positive and significant. 5. Conclusion In this study, we analyze the relationship between real estate transparency (RET) and the defaults on mortgages (DOM) using data from 46 countries over the period of 2006–2016. To capture the level of transparency in the real estate market, we employ an index developed by JLL. Using this measure, we document a negative relationship between RET and DOM. This result is robust when controlling for other important determinants of DOM as well as using various estimation methods. Our finding lends support to governments’ spending on initiatives to improve transparency in real estate markets which may reduce defaults on mortgages and, to some extent, contribute to the stabilization of financial systems. In our study, we only use the macro (or country-level) datasets. For future research, it may be useful to examine the relationship between RET and DOM using data collected at bank-level across countries. Acknowledgment We would like to thank the anonymous reviewer for his/her constructive and useful comments on earlier version of this article. Appendix A
Table A1 Correlation matrix and VIFs.
DOM INC UEM HP GINI CRET GOV RET
DOM
INC
UEM
1.000 −0.329 0.463 −0.065 0.079 −0.459 −0.346 −0.301
1.000 −0.250 0.094 −0.075 0.352 0.490 0.464
1.000 −0.206 0.216 −0.033 −0.090 0.030
HP
GINI
1.000 0.032 −0.089 −0.176 −0.146
0.032 1.000 −0.049 −0.335 −0.103
CRET
1.000 0.568 0.447
GOV
1.000 0.758
RET
Centred VIFs
1.000
2.723 3.222 2.389 1.198 1.948 2.171 1.300
Notes: DOM is default rate (in percentage), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP), GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5) and RET is the (inversed) real estate transparency index (ranges between 1 and 5). Variance Inflation Factors (VIFs) are a method of measuring the level of collinearity between the regressors in an equation.
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Fig. A1. The Evolution of Global Real Estate Transparency Index (1-5) from 2006 to 2016. Notes: The index ranges between 1 and 5. A country with a perfect score of 1 would be the country with the highest level of transparency. A country with a score of 5 would be a country with total opacity. In order to facilitate the interpretation of this index, we have reversed the scores (1: total opacity and 5: the highest level of transparency). Source: JLL, LaSalle Investment Management.
Fig. A2. Growth rate of DOM (46 markets) and RET (50 markets) over 2006-2016. Source: Data for defaults on mortgages (DOM; in percentage) and real estate transparency (RET) index (ranges between 1 and 5) are collected from Euromonitor International and JLL, LaSalle Investment Management, respectively.
Appendix B. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ribaf.2020. 101202.
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Gholipour, F.H., Masron, A.T., 2013. Real estate market factors and foreign real estate investment. J. Econ. Stud. 40 (4), 448–468. Ghosh, S., 2019. Loan delinquency in banking systems: how effective are credit reporting systems? Res. Int. Bus. Financ. 47, 220–236. Giannetti, M., 2007. Financial liberalization and banking crises: the role of capital inflows and lack of transparency. J. Financ. Intermediation 16 (1), 32–63. Goel, R.K., Hasan, I., 2011. Economy-wide corruption and bad loans in banking: international evidence. Appl. Financ. Econ. 21 (7), 455–461. Goldsmith-Pinkham, P., Sorkin, I., Swift, H., 2018. Bartik Instruments: What, When, Why, and How. NBER Working Paper No. 24408. Guiso, L., Sapienza, P., Zingales, L., 2013. The determinants of attitudes towards strategic default on mortgages. J. Finance LXVIII (4), 1473–1515. Hausman, J.A., 1978. Specification tests in econometrics. Econometrica 46, 1251–1272. Hirshleifer, J., 1971. The private and social value of information and the reward to inventive activity. Am. Econ. Rev. 61 (4), 561–574. Holtz-Eakin, D., Newey, W., Rosen, H.S., 1988. Estimating vector autoregressions with panel data. Econometrica 56 (6), 1371–1395. Jackson, J.R., Kaserman, D.L., 1980. Default risk on home mortgage loans: a test of competing hypotheses. J. Risk Insur. 47, 678–690. JLL, 2006. Real Estate Transparency Index 2006. Jones Lang LaSalle. JLL, 2008. Real Estate Transparency Index 2008. Jones Lang LaSalle. JLL, 2010. Mapping the World of Transparency. Jones Lang LaSalle. JLL, 2012. Global real estate transparency index. Global foresight series 2012. Real estate transparency back on track. Jones Lang LaSalle. JLL, 2014. Real Estate Raises the Bar. Global Real Estate Transparency Index. Jones Lang LaSalle. JLL, 2016. Taking Real Estate Transparency to the Next Level. Global Real Estate Transparency Index. Jones Lang LaSalle. Kumhof, M., Rancière, R., Winant, P., 2015. Inequality, leverage, and crises. Am. Econ. Rev. 105 (3), 1217–1245. Mankiw, N.G., 2016. Macroeconomics, edition 9. Worth Publishers, United States. Newell, G., 2016. The changing real estate market transparency in the European real estate markets. J. Prop. Invest. Financ. 34 (4), 407–420. Pagano, M., Volpin, P., 2012. Securitization, transparency, and liquidity. Rev. Financ. Stud. 25 (8), 2417–2453. Pavlov, A., Wachter, S., Zevelev, A.A., 2016. Transparency in the mortgage market. J. Financ. Serv. Res. 49 (2–3), 265–280. Seiler, M., Seiler, V.L., Lane, M.A., Harrison, D.M., 2012. Fear, shame, and guilt: economic and behavioral motivations for strategic default. Real Estate Econ. 40 (1), 199–233. Tajaddini, R., Gholipour, H.F., 2017. National culture and default on mortgages. Int. Rev. Financ. 17 (1), 107–133. Zheng, X., El Ghoul, S., Guedhami, O., Kwok, C.C., 2013. Collectivism and corruption in bank lending. J. Int. Bus. Stud. 44 (4), 363–390.
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Full length Article
Real estate market transparency and default on mortgages Hassan F. Gholipour*, Reza Tajaddini, Thi Ngoc Tram Pham
T
Swinburne Business School, Swinburne University of Technology, Melbourne, Australia
ARTICLE INFO
ABSTRACT
JEL classification: G21 G28 R30
This paper investigates the relationship between real estate market transparency (RET) and default on mortgages (DOM). Using data from 46 countries for the period of 2006–2016, we find evidence that there is a negative and significant relationship between RET and DOM. This result is robust with the inclusion of control variables and different estimation methods including panel fixed effects and generalized method of moments (GMM). We also find that the association between RET and DOM is more apparent in emerging economies than high-income countries.
Keywords: Real estate transparency Default rate Mortgage Panel data
1. Introduction Jones Lang LaSalle1 (JLL, 2016) defines a transparent real estate market as an open and clearly organized market operating in a legal and regulatory framework that is characterized by a consistent approach to enforce rules and regulations. Respect to private property rights and high ethical and professional standards are also included in JLL’s definition of real estate transparency (RET). RET has improved steadily at the international level over the last decade. New legislation, higher ethical standards, the availability of quality data and more transparency have helped the progress of the real estate industry (JLL, 2016)2 . According to a Global Real Estate Transparency report (JLL, 2016), between 2004 and 2016, the real estate transparency score increased with an average growth rate of 4.5 percent (see Fig. A1 in Appendix A). The improvements in transparency have been mainly driven by three factors: (1) the globalization of the real estate market; (2) the significant growth of capital allocations to real estate; and (3) technological development in areas of performance measurement, market fundamentals and valuation practices. Promoting a culture of “open data” by governments and businesses, especially after the Global Financial Crisis (GFC), has also contributed to higher real estate market transparency (JLL, 2014, 2016). In recent years, the relationships between RET and real estate market activities, such as investments, property development and property occupation, have gained notable attention (e.g. Farzanegan and Gholipour, 2014; Gholipour, 2013; Gholipour and Masron, 2013; JLL, 2006, 2010, 2016; Newell, 2016). This paper extends the literature by investigating whether RET has any significant relationship with defaults on mortgages (DOM). We concentrate on DOM because of the recent rise in DOM and foreclosures in many
Corresponding author. E-mail addresses: [email protected] (H.F. Gholipour), [email protected] (R. Tajaddini). 1 Jones Lang LaSalle is a financial and professional services firm specializing in real estate services and investment management. 2 While there is a general improvement in RET, the level of progress is not consistence across all countries. For example, Romania witnessed the largest improvement in RET in 2008 due to joining to the European Union. Whereas, Austria experienced a sharp decline in the index in 2012 because of low quality of real estate data provision (JLL, 2008; JLL, 2012). For more details about the fluctuations of the RET index of each country please refer to JLL’s annual reports (http://greti.jll.com/greti). ⁎
https://doi.org/10.1016/j.ribaf.2020.101202 Received 17 June 2019; Received in revised form 16 September 2019; Accepted 13 February 2020 Available online 13 February 2020 0275-5319/ © 2020 Elsevier B.V. All rights reserved.
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advanced economies, especially the US, where this contributed to the 2008–2009 GFC (Tajaddini and Gholipour, 2017). On the other hand, there is growing recognition by governments, particularly in emerging economies, that poor RET not only hinders inward foreign real estate investment, but also substantially impacts the quality of life of countries’ citizens and their relationship with local government (JLL, 2014, 2016). Given the adverse consequences of deficient RET, many governments have embraced the policy of open data and increased their expenditures on technological innovation that can boost transparency (JLL, 2014). Nevertheless, there is some evidence that suggests transparency in some forms may have negative side effects3 . In the real estate context, Pavlov et al. (2016) look at the impact of transparency in the mortgage market. The authors argue that as lenders become more aware of the geographical risk of each mortgage as a result of a higher transparency, they may withdraw credit from regions that experience local negative shocks. This withdrawal magnifies the price impact of the original income shock and increases real estate price volatility. Farzanegan and Gholipour (2014) find that higher levels of real estate transparency do not contribute to the expansion of foreign real estate investment in emerging and developed economies. JLL (2008, 2010) also provide evidence that high levels of transparency neither eliminate risks for investors nor guarantee strong investment returns. Given the different views on the link between transparency and outcomes in real estate markets, it would be interesting to empirically examine the effectiveness of governments’ attempts to boost RET on non-performing mortgages. As can be seen from Fig. A2 in Appendix A, there is a strong and negative correlation between growth of RET and DOM (r = - 0.78). However, in order to statistically judge the strength of association between these variables, we need to control for other time-varying economic factors (e.g. household income and house prices) in addition to time constant and regional specific factors (e.g. cultural values (Tajaddini and Gholipour, 2017)). Our study contributes to the literature on the linkage between institutional quality and non-performing loans (NPLs). Although there have been some studies on the effect of institutional variables (e.g. corruption) on NPLs (e.g. Breuer, 2006; Boudriga et al., 2009; Ghosh, 2019; Goel and Hasan, 2011), to the best of our knowledge, no empirical study has examined the relationship between institutional variables related to the real estate market, in particular RET, and DOM across countries. We use international data from 46 countries for the period of 2006–2016 to examine this relationship. Our main findings from panel regression analysis confirm that countries with higher degrees of transparency in their real estate markets have lower rates of DOM. The paper proceeds as follows: Section 2 briefly summarizes the relevant literature and develops the research hypothesis; Section 3 explains the data and model specification; Section 4 presents and discusses the empirical results; and Section 5 concludes the paper. 2. Literature review and hypothesis development In this section, we present rationales on how RET may influence DOM. Our paper most closely relates to the literature examining the effects of institutional quality on NPLs. Previous literature shows that a country’s institutional factors greatly matter for NPLs. For example, Goel and Hasan (2011) find that greater corruption is associated with higher NPLs. They argue that monitorial and enforcement institutions are particularly weaker in countries with higher degrees of corruption. This may imply a lack of a proper antifraud monitoring system or more lenient punishment against corrupt activities. Breuer (2006) provides evidence that, in addition to macroeconomic factors and banking institutions, corruption and ethnic fractionalization increase problem loans in the banking system. More specifically, he argues that more self-interested activities can be observed in countries with higher levels of corruption. Furthermore, self-interested behaviors and proclivities affect the principal-agent relationships between banks and borrowers and may also increase problem loans. Breuer (2006) also observes that the potential for conflicts of interest is higher in countries with more ethnically heterogeneous populations. Zheng et al. (2013) find that cultural values also influence corruption in bank lending. They suggest that, in countries with high scores of collectivism, the interdependence between bank officers and customers leads to higher levels of lending corruption. Boudriga et al. (2009) show that strong political institutions, control of corruption and higher levels of democracy increase the effectiveness of banking supervision and consequently reduce credit risk exposure and problem bank loans. Mankiw (2016; pp. 388) also argues that the high default rates on bank loans during the Asian financial crisis (1997–1998) was mainly due to fact that many Asian banks (that were managed by governments) had been extending loans to those with the most political clout rather than to those with the most profitable investment projects. In this study, we extend the growing literature on the association between institutional quality and NPLs by empirically investigating the relationship between RET (an institutional quality related to the real estate market) and DOM. We point out a number of mechanisms through which RET might affect the DOM. First, one of the primary reasons is weak mortgage underwriting process. In countries with higher levels of RET, central banks and financial regulators implement stricter rules on housing financing and monitor mortgage lenders more closely, which possibly reduces the probability of DOM. In these countries, financial institutions have better access to the financial histories of mortgage applicants and also require them to provide more detailed documentation to verify their financial credibility. The rapid growth of technological innovations has enabled mortgage lenders to assess the ability-to-pay characteristics of borrowers (e.g. income, unemployment and history of previous loan re-payment). Therefore, borrowers with unsatisfactory credit scores may not be able to get mortgages, which in turn reduces the probability of defaults. In addition, in countries with higher levels of RET, there are fewer chances for granting corrupt loans (Barth et al., 2009) to deceitful and unreliable mortgage applicants with relatively higher odds of 3 For the insurance industry, Hirshleifer (1971) shows that transparency can be harmful for insurance opportunities. That is, if the actors of an insurance contract know about each other’s risk in a transparent system, they may decide to not insure each other.
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defaulting. Higher RET also attracts quality foreign real estate investors (JLL, 2010) who are often looking for second homes or holiday houses. As foreign investors are under more scrutiny and monitoring by lenders, the chances of their defaults on mortgages are expected to be lower. Second, in more transparent countries, property agents and lenders are more likely to follow the professional and ethical standards and more willing to work with borrowers towards a better solution than defaulting in difficult situations. As shown by Seiler et al. (2012), borrowers who do not know basic information about their loans and are also frustrated with their lenders are more likely to default on their mortgages. However, one may argue that the recent US experience shows that complex mortgages confused many borrowers and contributed significantly to mortgage default, even though the US has the highest RET score. Since our sample contains a large number of developed and emerging economies, the former argument may be more dominant. Finally, greater data availability (both historical and expected) on house prices at suburb and regional levels may help homebuyers in forming a more accurate evaluation of their home ownership choices. Homebuyers may gain a better understanding about the ways that their investment choices would impact their future wellbeing. Accordingly, this may limit the chance of irrational or risky investments with a higher probability of default. In short, we propose that higher RET limits the chance of DOM occurrence. That is because lenders in countries with high RET scores usually have better scrutiny and monitoring processes in place and provide higher levels of professional support to borrowers. In these countries mortgage borrowers have also access to more accurate information that helps them in their investment decisionmaking process. Based on the above justifications, we hypothesize that DOM is negatively correlated with RET. 3. Data and methodology 3.1. Data We utilize an unbalanced panel data to explore the relationship between RET and DOM. We use biannual data from 46 countries for 2006, 2008, 2010, 2012, 2014 and 2016. The start year of our sample is 2006 due to the slight changes JLL made to their methodology of index calculation, revising their questionnaires and adding new questions in that year (JLL, 2006, 2014). Therefore, we do not include 2004 and pre-2004 RET index in our analyses because the measurements of indices are not strictly comparable to the 2006–2016 indices. Our sample covers all those countries for which data on RET and DOM are obtainable. The country sample can be found in Table 1. The dependent variable for our analysis is the logarithm of non-performing housing mortgage rate as a measure for DOM. Information on non-performing housing mortgage rate is obtained from Euromonitor International. This is defined as a loan 90 days delinquent where full payment can no longer be expected. It refers to non-performing housing loans as a percentage of outstanding Table 1 Average mortgage default rates and RET index for sample countries (2006-2016). Source: Euromonitor International, Consumer Lending & JLL, LaSalle Investment Management Countries
DOM (%)
RET
Countries
DOM (%)
RET
Argentina Australia Austria Brazil Canada Chile China Colombia Czech Denmark Egypt France Germany Greece Hong Kong Hungary India Indonesia Israel Italy Japan Malaysia Mexico Morocco
3.93 0.63 2.45 2.87 0.35 4.27 0.52 3.17 2.50 1.33 5.45 2.55 2.80 18.57 0.03 8.12 1.53 2.77 1.10 3.60 0.78 3.73 3.63 7.17
1.70 3.74 3.04 2.14 3.67 1.96 1.73 1.10 2.70 3.21 1.26 3.59 3.35 2.21 3.31 2.57 1.84 1.78 2.38 2.96 2.71 2.72 2.17 1.34
Netherlands Norway Philippines Poland Portugal Romania Russia Saudi Arabia Singapore South Africa South Korea Spain Sweden Taiwan Thailand Turkey United Arab Emirates Ukraine United Kingdom USA Venezuela Vietnam
0.43 1.15 8.87 2.30 2.13 11.97 1.47 3.65 1.35 9.28 0.43 3.02 2.20 1.30 4.42 2.85 5.52 20.00 1.73 5.30 1.50 1.20
3.58 3.06 1.97 2.82 2.78 2.07 1.93 1.37 3.30 2.95 2.06 3.10 3.44 2.34 2.01 1.86 1.71 1.57 3.73 3.75 0.77 0.99
Note: DOM is default rate (in percentage). The real estate transparency (RET) index ranges between 1 and 5. A country with a perfect score of 1 would be the country with the highest level of transparency. A country with a score of 5 would be a country with total opacity. In order to facilitate the interpretation of this index, we have reversed the scores (1: total opacity and 5: the highest level of transparency). 3
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balance. Table 1 shows the average of mortgage default rates for the sample countries for the period of 2006–2016. The average value for our sample ranged from 0.03 % (Hong Kong) to 20 % (Ukraine) where a higher percentage indicates a higher rate of DOM. Data for the real estate transparency (RET) index is obtained from the LaSalle Investment Management of Jones Lang LaSalle (JLL). Table 1 shows the average RET index for sample countries for the period of 2006–2016. The United States (1st), the United Kingdom (2nd) and Australia (3rd) are the most transparent markets among our sample countries while Venezuela and Vietnam are at the bottom. Some of the main characteristics of transparent real estate markets are rigorous regulations and legal enforcement, a high degree of institutional real estate ownership, high ethical professional standards, quality market fundamentals research, more public and private performance indices and fair transaction processes. The JLL’s RET index is based on a combination of quantitative market data and information gathered through a survey of the global business network of JLL and LaSalle Investment Management across more than 100 markets. The index has been updated every two years since 1999 and aims to help real estate investors, corporate occupiers, retailers and hotel operators understand important differences when transacting, owning and operating in foreign markets. It is also a helpful gauge for governments and real estate industry organizations who are seeking to improve transparency in their home markets (JLL, 2014) (2016). For each country, JLL uses both quantitative data points and survey questions to calculate the RET scores. The survey data and quantitative measures complement each other. For example, knowing the market coverage and length of a country’s direct real estate index does not provide a full picture, JLL also gathers qualitative data on whether investors actually trust and use the index. JLL’s local research teams, in consultation with business leaders and real estate professionals active in each market, complete the survey. The RET index ranges on a scale from 1 to 5. A country or market with a perfect 1 score has total real estate transparency; a country that scores 5 has total real estate opacity. In order to facilitate the interpretation of this index, we have reversed the scores (1 equals total opacity and 5 equals the highest level of transparency). It should be noted that there are at least two limitations concerning the RET data. Firstly, the JLL’s RET index does not purely focus on residential real estate transparency but includes offices, retail, industrial, hotels and residential properties for developing its RET index. Secondly, JLL did not survey every country from our sample over the period of our study and therefore our panel data are unbalanced. Besides the variable of interest (RET), the relevant theoretical and empirical studies suggest that some macroeconomic and institutional variables are associated with DOM. Based on the ability-to-pay hypothesis and strategic-default hypothesis4, household income, unemployment and changes in house prices are selected as other explanatory variables in the model specification. We expect a negative relationship between income, increases in house prices and DOM, as well as a positive relationship between unemployment and DOM (e.g. Guiso et al., 2013; Seiler et al., 2012). We use gross domestic product (GDP) measured at purchasing power parity (PPP) per household (in logarithm) and unemployment rate (unemployed population as a percentage of the labour force) as measures of income and unemployment, respectively. As a measure for the changes in house prices, we use the logarithm value of Euromonitor’s Index of Housing Prices (2010 = 100). The house price index reflects changes in the prices of residential dwellings in a country5 . We also include another three explanatory variables of non-performing loans in our estimations: income inequality (suggested by Kumhof et al., 2015), quality of institution (suggested by Breuer, 2006; Boudriga et al., 2009; Goel and Hasan, 2011) and financial market development (suggested by Boudriga et al., 2009). We use logarithm of Gini index, the average of six governance indicators of the World Bank6 and domestic credit to private sector by banks (% of GDP) as proxies for income inequality, quality of institutions and financial market development, respectively. The data for all variables are obtained from Euromonitor International. The descriptive statistics of all variables are provided in Table 2. Table A1 presents the correlation matrix and centred Variance Inflation Factors (VIFs)7 . Given the advantage of using panel data to reduce collinearity amongst the variables (Baltagi, 2008), all of the explanatory variables have VIF less than threshold of 10. 3.2. Model Based on the above discussion, the empirical model we use is as follows: DOMit = α + β1 RETit + β2 INCit + β3 UEMit + β4 HPit + β5 GINIit + β6 CRETit + β7 GOVit + vi + Ωt + uit
(1)
where DOM is default rate; RET stands for the real estate transparency index (RET); INC is GDP measured at purchasing power parity 4 According to the ability-to-pay hypothesis, borrowers default on their mortgages when they face a dramatic, negative economic event, such as sudden unemployment, divorce, prolonged illness, or emergency medical care. The strategic-default theory argues that borrowers default because the values of their properties have declined to a level far below the outstanding balance on their loan (Jackson and Kaserman, 1980; Tajaddini and Gholipour, 2017). 5 Another possible channel that RET may affect DOM is through house price volatility. One may argue that higher house price volatility could lead to higher mortgage default, and it is reasonable to hypothesize that lower RET is associated with higher house price volatility. The similar argument can be done for income volatility. However, our empirical results do not support these channels. We included interaction terms between RET and HP as well as RET and INC in the regressions. The coefficients of interaction terms were insignificant, meaning that RET, HP and INC have their own independent impact on DOM. 6 Six dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. For details, see http://info.worldbank.org/governance/wgi/#home. 7 The VIFs are a method of measuring the level of collinearity between the regressors in an equation.
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Table 2 Descriptive statistics. Variables
Mean
Median
Max
Min
Std. Dev.
DOM RET INC UEM HP GINI CRET GOV
3.82 2.47 89,065.29 0.073 108.93 0.39 0.81 0.53
2.40 2.47 75,981.35 0.06 100.00 0.39 0.74 0.64
41.00 5 569434.30 0.26 461.80 0.64 2.33 1.87
0.03 0.31 13,828.10 0.007 44.70 0.25 0.11 −1.50
5.23 0.87 68,518.12 0.04 35.19 0.07 0.45 0.84
Notes: DOM is default rate (in percentage), RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP) and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). Table 3 Results of panel fixed-effect regression (full sample). Dependent variable: DOM Independent variables RET INC
(1) −1.333** (0.556)
(2) −0.259** (0.102) −0.393 (0.413) 1.323 (1.328) −0.597*** (0.171) 2.541*** (0.841) 0.167 (0.157) −0.540* (0.328) Yes Yes 0.891 0.860 27.80*** 40.02*** 212
UEM HP GINI CRET GOV Country fixed Period fixed R-squared Adj R-squared F-statistic Hausman Test (Chi-Sq. Statistic) Observation
Yes Yes 0.673 0.601 9.147 12.51** 273
(3) −0.235** (0.098) −1.423* (0.854) 2.059 (1.290) −1.354*** (0.352) 2.374*** (0.852) 0.134 (0.161) Yes Yes 0.893 0.861 28.088*** 32.198*** 218
Notes: DOM is default rate (in percentage), RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP), and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). The asterisks *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Robust standard errors are presented in parentheses. Constant was included in the estimation but not reported.
(PPP) per household; UEM is unemployment rate; HP represents house price index; GINI is Gini index; CRET is domestic credit to private sector by banks (% of GDP); GOV is the average of six governance indicators of the World Bank; vi captures the country fixed effect; and Ωt takes into account the time effect. i = 1,…, N denotes the country, t = 1,…, T denotes the time period and u is an error term. The country fixed effects account for all time-invariant differences across countries, such as cultural factors or geography. The year fixed effects control for factors that vary across time but are constant across countries, such as global business cycles (Baltagi, 2005). It should be noted that we employed a Hausman’s (1978) test to compare the fixed and random effects estimates of coefficients. The Chi-Sq. Statistic of the test was significant at the 5% level, indicating that fixed effects are appropriate for our models. 4. Empirical results 4.1. Main analysis Table 3 presents the results of the country and year fixed effects regressions8 . In column 1 of Table 3, only RET is included as 8
In addition to panel regressions, we also ran cross-sectional regression for each year separately. The (unreported) results indicate that there is a 5
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Table 4 Results of panel fixed effect regressions (sub-samples). Dependent variable: DOM Independent variables
Sample (2006–2008) (1)
Sample (2010–2016) (2)
High-Income Countries (3)
Emerging Economies (4)
RET
−0.102 (0.090) −0.873 (0.852) 0.960 (5.646) −0.494 (0.521) −0.549 (0.920) 1.009** (0.438) −0.539 (0.793) Yes Yes 62
−0.308*** (0.091) −0.600 (0.666) 0.151 (0.198) −0.582* (0.322) 2.232* (1.337) 0.103 (0.386) −0.913* (0.516) Yes Yes 150
0.126 (0.218) 1.241 (0.918) 0.310* (0.186) −0.655** (0.324) 3.086* (1.620) 0.116 (0.321) −1.055** (0.450) Yes Yes 142
−0.371*** (0.122) −1.302* (0.669) 0.260 (0.344) −0.414 (0.286) 4.322** (1.940) 0.005 (0.437) −0.543 (0.464) Yes Yes 70
INC UEM HP GINI CRET GOV County fixed Period fixed Observation
Notes: DOM is default rate (in percentage), RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP) and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). The asterisks *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Robust standard errors are presented in parentheses. Constant was included in the estimation but not reported.
explanatory variable for DOM. In column 2, RET and the control variables are included as explanatory variables for DOM. In column 3, we exclude GOV as a control variable because it is significantly correlated with RET (r = 0.758), although its VIFs value is less than 10 (see Table A1). Nevertheless, we focus on the results presented in column 2 of Table 3 for discussion of our results. The findings show that RET has a negative and significant relationship with DOM.9 In terms of economic magnitude, a one unit increase in the RET index (measured on a scale of 1–5) is associated with a 0.25 drop in DOM (column 2 of Table 3). This finding supports our hypothesis that countries with higher levels of real estate transparency tend to have significantly lower default rates, other things being equal. This is consistent with those of Seiler et al. (2012), showing that borrowers with limited knowledge and information about their loans are more likely to default on their mortgages. Our result also supports the body of literature that emphasizes the importance of transparency between borrowers and lenders as a determinant of banking system stability (Fischer, 1999; Giannetti, 2007). Simply put, transparency helps to limit disruptions in the credit market. Greater transparency on the quality of mortgages enables investors to evaluate credit providers’ performances. This allows investors to allocate appropriate risk premium to the credit providers. In turn, market discipline improves and over-lending problems are limited (Giannetti, 2007). As discussed in Pagano and Volpin (2012), detailed information helps market participants to assess the risk associated with structured debt. Rating agencies can use this information to estimate the probability of default and consequently provide ratings that are more sophisticated. In addition, our finding is in line with studies that highlight the importance of institutional variables in loan market performance. For example, Boudriga et al. (2009) show that the most effective ways to reduce bad loans are to strengthen the legal system, improve transparency and enhance democracy. Similarly, Breuer (2006) shows that in addition to economic determinants, institutional aspects of a country, such as corruption and the degree of ethnic heterogeneity, can influence the number of non-performing loans. In terms of control variables, the coefficient for INC is negative and significant, implying that countries with higher income per household have a lower default rate. This finding provides support for the ability-to-pay hypothesis. In line with the strategic-default hypothesis, we find that countries with increases in house prices (HP) also have lower default rates. 4.2. Analyses for sub-sample countries and sub-periods In addition to the full sample analyses, we disaggregate the sample countries based on the income level and periods. In terms of income level, we follow the World Bank’s country grouping. According to the World Bank,10 high-income economies are those with a gross national income (GNI) per capita of $12,056 or more. Therefore, we have 29 high-income countries (e.g. Australia, Germany and Japan) and 17 countries with income below $12,056 (e.g. Brazil, Malaysia and Thailand). We also divide the period of study into (footnote continued) negative relationship between RET and DOM across most of the sample period. 9 We also regressed DOM on control variables only and the (unreported) results show that the control variables keep the expected signs. 10 See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups 6
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Table 5 Panel GMM regressions results. Dependent variable: Δ DOM Independent variables
Full sample (1)
Δ DOM (-1)
0.146** (0.067) −0.109*** (0.037) 0.702 (0.573) 3.668** (1.667) −0.636*** (0.163) 0.586 (0.477) 0.172 (0.256) −0.293 (0.328) Yes Yes 0.312 0.800 173
Δ RET Δ INC Δ UEM Δ HP Δ GINI Δ CRET Δ GOV Cross-section fixed (first-differences) Period fixed-effect p-value of Sargan statistic p-value of AR(2) test Observations
Notes: DOM is default rate (in percentage), DOM (-1) is one lag of DOM, RET is the (inversed) real estate transparency index (ranges between 1 and 5), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP) and GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5). The asterisks *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Robust standard errors are presented in parentheses. Constant was included in the estimation but not reported.
two sub-periods with reference to the financial crisis - 2008. This is because RET regulations were intensified after 2008 (JLL, 2014, 2016), so the results may vary by sub-periods. We run our regressions for the period of 2006–2008 and 2010–2016 separately. The results are presented in Table 4. Our findings show that RET is inversely and significantly correlated with DOM for the period of 2010–2016, whereas the link between RET and DOM is negative but not statistically significant for the period of 2006–2008, perhaps due to a restricted degree of freedom (columns 1 and 2 of Table 4). Moreover, the results of sub-sample regressions suggest that while there is a negative and statistically significant association between RET and DOM in emerging economies, there is an insignificant link between the two variables for high-income countries. This finding implies that default rates are more sensitive to progress in transparency in the real estate markets of emerging economies compared to high-income countries. 4.3. Issue of persistency of default rate and endogeneity The previous results are based on a static panel fixed effects estimation method. As a robustness check, we apply a dynamic panel model (Holtz-Eakin et al., 1988; Arellano and Bond, 1991) which is estimated by the first-difference generalized method of moments (GMM). The model is useful for our data set because the dynamic panel model is designed for panels with a large number of crosssections and a short time series. Moreover, it is likely that there exists persistence in the dynamics of default rates, such that the previous level of default rates has an influence on the current level. The other reason to use the first-difference GMM method for estimation is to minimize the potential problem of endogenous explanatory variables in Eq. (1). For example, we assume that higher RET decreases the default rate; however, it can be argued that improvements in RET may be the result of increases in default rates. In other words, since the Global Financial Crisis (which was largely caused by DOM), most governments across the world have implemented stricter regulations and legal frameworks, with a call for an increased level of financial as well as real estate market data and performance benchmarks (JLL, 2016).
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Finally, by taking the first difference of the variables in the GMM method, we also address the possible non-stationarity issue with the variables and reduce the severity of multicollinearity between the explanatory variables (Baltagi et al., 2009). In the estimation models, we include one lag of the dependent variable as a regressor and use period dummy variables to control for period fixed effects, taking the first difference of each variable in the regression to remove cross-section fixed effects. We include both internal instruments in the first-difference GMM estimation (one lag of all explanatory variables) as well as an external instrument (local real estate industry shares in national outputs as an instrument, suggested by Goldsmith-Pinkham et al., 2018). As a proxy for this variable, we use the percentage of gross value added (GVA) from real estate, renting and business activities to aggregate national GVA. Data for the variable are obtained from Euromonitor International. Two diagnostic tests are carried out on the GMM estimations. First, the Sargan test of over-identification restrictions is employed to check the validity of the instruments. The null hypothesis of this test is that the instruments are not correlated with the residuals. The failure to reject the null hypothesis indicates that the specification is correct and the instruments are valid (see bottom of Table 5). Second, we use the Arellano-Bond test for second order correlation (AR(2)) in the first differenced residuals. The test indicates that there is no serial correlation, as the p-value of this test is insignificant (see bottom of Table 5). Table 5 shows the results of GMM estimations for the full sample. Similar to the panel fixed effects regressions, the GMM estimation (column 1 of Table 5) shows that real estate transparency is an important determinant of defaults on mortgages, as the coefficient of RET (β = -0.109) is negative and significant at the 1% level. We also find that previous DOM has a positive impact on current DOM in the sample countries, as the coefficient of DOM(-1) is positive and significant. 5. Conclusion In this study, we analyze the relationship between real estate transparency (RET) and the defaults on mortgages (DOM) using data from 46 countries over the period of 2006–2016. To capture the level of transparency in the real estate market, we employ an index developed by JLL. Using this measure, we document a negative relationship between RET and DOM. This result is robust when controlling for other important determinants of DOM as well as using various estimation methods. Our finding lends support to governments’ spending on initiatives to improve transparency in real estate markets which may reduce defaults on mortgages and, to some extent, contribute to the stabilization of financial systems. In our study, we only use the macro (or country-level) datasets. For future research, it may be useful to examine the relationship between RET and DOM using data collected at bank-level across countries. Acknowledgment We would like to thank the anonymous reviewer for his/her constructive and useful comments on earlier version of this article. Appendix A
Table A1 Correlation matrix and VIFs.
DOM INC UEM HP GINI CRET GOV RET
DOM
INC
UEM
1.000 −0.329 0.463 −0.065 0.079 −0.459 −0.346 −0.301
1.000 −0.250 0.094 −0.075 0.352 0.490 0.464
1.000 −0.206 0.216 −0.033 −0.090 0.030
HP
GINI
1.000 0.032 −0.089 −0.176 −0.146
0.032 1.000 −0.049 −0.335 −0.103
CRET
1.000 0.568 0.447
GOV
1.000 0.758
RET
Centred VIFs
1.000
2.723 3.222 2.389 1.198 1.948 2.171 1.300
Notes: DOM is default rate (in percentage), INC is GDP measured at purchasing power parity (PPP) per household (international dollar), UEM is unemployment rate, HP represents house price index (2010 = 100), GINI is Gini index (ranges between 0–1), CRET is domestic credit to private sector by banks (% of GDP), GOV is the average of six governance indicators of the World Bank (ranges between -2.5 to 2.5) and RET is the (inversed) real estate transparency index (ranges between 1 and 5). Variance Inflation Factors (VIFs) are a method of measuring the level of collinearity between the regressors in an equation.
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Fig. A1. The Evolution of Global Real Estate Transparency Index (1-5) from 2006 to 2016. Notes: The index ranges between 1 and 5. A country with a perfect score of 1 would be the country with the highest level of transparency. A country with a score of 5 would be a country with total opacity. In order to facilitate the interpretation of this index, we have reversed the scores (1: total opacity and 5: the highest level of transparency). Source: JLL, LaSalle Investment Management.
Fig. A2. Growth rate of DOM (46 markets) and RET (50 markets) over 2006-2016. Source: Data for defaults on mortgages (DOM; in percentage) and real estate transparency (RET) index (ranges between 1 and 5) are collected from Euromonitor International and JLL, LaSalle Investment Management, respectively.
Appendix B. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ribaf.2020. 101202.
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