Credit risks and default behavior of mortgagors

CREDIT RISKS AND DEFAULT BEHAVIOR OF MORTGAGORS LIU BO NATIONAL UNIVERSITY OF SINGAPORE 2011 CREDIT RISKS AND DEFAULT BEHAVIOR OF MORTGAGORS LIU BO (M. Ec., TJU; B. Eng., TJU) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF REAL ESTATE NATIONAL UNIVERISYT OF SINGAPORE 2011 ACKNOWLEDGEMENTS The foremost appreciation goes to my supervisor, Associate Professor Sing Tien Foo. I am deeply indebted to his patience, inspiration, and efforts. His meticulous attitude to research plays an exemplary role in the whole process. Special thanks to A/Prof. Fu Yu Ming, who enriches me with the research philosophy and shares the grace from God, giving people courage to discover. Prof. Deng Yong Heng graciously inspires with interesting talks. I feel grateful to Prof. Ong Seow-Eng and A/Prof. Tu Yong for their encouragement to overcome my weakness. Great thankfulness is also to Dr. Seah Kiat Ying, Dr. Lee Nai Jia, Dr. Liao Wen-Chi and our Head A/Prof. Yu Shi Ming, for their discussions and research spirits. Numerous other faculty members at National University of Singapore are acknowledged, as Dr. Husza'R Zsuzsa Reka, Dr. Qian Wenlan. Prof. Danny Ben-Shahar and Prof. Campbell, J. Y. are grateful to generously share their technique and insightful ideas. My gratitude also goes to various visiting professors, named, but not limited to, Anthony B. Sanders, Brent Ambrose, Brent Smith, David Ling, Geoffrey K. Turnbull, James D. Shilling, Jay Sa-Aadu, John L Glascock, Kerry Vandell, Stuart Gabriel, Timothy J. Riddiough, Nancy Wallace for their inspiring suggestions. I also enjoy sharing ideas with the graduate students, in particular, Dr. Fan Gangzhi, i Dr. Li Yun, Dr.Wu Jian Feng, Dr. Sun Jing Bo, Dr. Dong Zhi, Zhao Da Xuan, Shen Huai Sheng, Zhang Hui Ming, Wong Woei Chyuan, Omokolade Ayodeji Akinsomi, and all other peers that I have not named. Seminars by Department of Real Estate, Institution of Real Estate Studies (IRES) and Risk Management Institution (RMI) of National University of Singapore provide a great platform to keep path with the updating researches globally. I gratefully acknowledge the easily approachable administrative staffs in Department of Real Estate, NUS: Ms. Ko Chen, Ms. Zainab, Ms. Kamsinah, and in School of Design & Environment, Ms. Nor‟Aini and Mei Yin. National University of Singapore offers the generous financial support through the dissertation. She, a respectful university, also provides great facilities, soft-and-hard library source of researches, happy study atmosphere and the platform to access the professional excellence. I am particularly appreciative to my parents, Liu Jinhui and Deng Qingxiu, for their morally endless supports and love, and to my brother, Liu Xue, and sister in-law for their animating energy to overcome difficulties. Above all, thanks to my gracious and strong-minded husband, He Lei. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS . i TABLE OF CONTENTS . iii SUMMARY vii LIST OF TABLES . ix LIST OF FIGURES . x Chapter Introduction 1.1 Background . 1.1.1 Mortgage Markets 1.1.2 Mortgage Default and Crisis 1.2 Research Objectives 17 1.3 Knowledge Gaps . 18 1.3.1 Mortgage Choice, and Default . 18 1.3.2 Negative Equity and Mortgage default 22 1.3.3 Market Structure and Mortgage default . 23 1.4 Research Question . 27 1.5 Significance of the Study 29 1.6 Structure of the Study 30 Chapter Literature Review . 31 2.1 Overview . 31 2.2 Mortgage Choice . 32 2.2.1 Borrowers‟ Self-Selection 32 2.2.2 Mortgage Choice and Default 41 2.2.3 Limitation Summary 42 2.3 Non-Strategic Default . 44 iii 2.3.1 Strategic and Non-strategic Default in Option Theory . 44 2.3.2 Credit Score as Determinant 48 2.3.3 Other Default Determinants . 49 2.3.4 Limitation Summary 50 2.4 Market Structure . 50 2.4.1 Contestability and Efficiency . 50 2.4.2 Supply-side Market Structure, Underwriting and Mortgage Risk 51 2.4.3 Limitation Summary 58 Chapter Data Description . 61 3.1 Introduction . 61 3.2 Data Collection . 61 3.2.1 Data Sources and Collected Raw Variables 61 3.2.2 Data Coverage 67 3.3 Facts and Statistics 69 3.4 Data Limitation and Future Enrichment 75 Chapter Self-selection and Default 78 4.1 Introduction . 78 4.2. Mortgage Choice on the Menu . 80 4.2.1 Model Essence Summary . 80 4.2.2 Stochastic Variables Setting . 82 4.2.3 Mortgage Menu and Mortgage Interest Rate . 83 4.2.4 Household‟s Lifetime Utility Maximization 84 4.2.5 Numerical Analysis 87 4.3 Empirical Models 94 4.3.1 Model Specifications 94 4.3.2 Regression Variables 98 iv 4.3.3 Descriptive Statistics 104 4.4 Analysis of Results 109 4.4.1 Conditional Default Rate 109 4.4.2 Borrowers‟ Self-Selection of Mortgage Type . 111 4.4.3 Effects of Mortgage Self-Selection on Mortgage Default Risks 114 4.4.4 Robustness Tests of Borrowers‟ Self-selection Mortgage Type . 120 4.5 Summary . 137 Chapter Non-Strategic Default . 140 5.1 Introduction . 140 5.2 Financial Option on Mortgage Default . 141 5.3 Rational Mortgage Default model . 144 5.3.1 Mortgagor‟s Utility 144 5.3.2 Liquidity Constraint . 150 5.3.3 Relationship with the Classic Option Model 152 5.4 Simulation Analysis 160 5.4.1 Borrowers and Mortgage Specification 160 5.4.2 Simulation Procedure and Results 161 5.5 Empirical Methodology 166 5.5.1 Variables . 166 5.5.2 Empirical Model . 169 5.6 Result Analysis 173 5.6.1 Statistics . 173 5.6.2 Split Population Regression of Mortgage Default 180 5.6.3 Ruthless Default . 185 5.6.4 Suboptimal Default 188 5.7 Summary . 192 Chapter Contestability of Residential Mortgage Market . 194 v 6.1 Introduction . 194 6.2 Theoretical Intuition and Hypotheses 195 6.2.1 Four-Quadrant Model for the Credit Market 195 6.2.2 Hypotheses . 200 6.3 Empirical Methodology 206 6.3.1 Regression Variable 206 6.3.2 Descriptive Statistics 211 6.4 Empirical Methodology and Testing . 218 6.4.1 Hypothesis 1: Banking Market Structure and Mortgage Supply 218 6.4.2 Hypothesis 2: Banking Market Structure and Mortgage Default . 226 6.4.3 Robust Test: Effects of Legislation Risks . 233 6.4.4 Robust Test: Credit Expansion Phases . 239 6.5 Summary . 240 Chapter Contribution and Future Work 241 7.1 Summary . 241 7.1.1 Main Findings 241 7.1.2 Policy Implication 246 7.2 Contribution 248 7.3 Discussion . 252 7.3.1 Limitation and Future Work . 252 7.3.2 Extensions to Asian Markets 256 BIBLIOGRAPHY . 258 Appendix 1: Contestability and Concentration on Banking Markets 272 vi SUMMARY This study adds to the understanding of residential mortgage default in three aspects: (i) borrowers‟ self-selection, (ii) “non-strategic” mortgage default, and (iii) banking market structure effect. Firstly, borrowers‟ contract choice is modeled under life-time utility maximization in the rational consumption of housing and non-housing services. The simulation shows that heterogeneous borrowers have different preference for mortgage contracts (mortgage type and/or LTV), given the mortgage rate and borrowers‟ observable and unobservable characteristics (such as income and credit score) (ceteris paribus). Heckman‟s two-step empirical tests are conducted to study the unobservable risk factor effect (that reflected in borrowers‟ mortgage choice) on mortgage ex-post default risk. Comparative self-selection and self-selection into fixed rate mortgages (FRMs) are found to reduce mortgage default risks. In addition, borrowers, who self-select into adjustable rate mortgages (ARMs), have higher ex-post default probability relative to other borrowers. The self-selection effects are reinforced by high credit scores (FICO) of borrowers. The second part is to explain non-ruthless and suboptimal default behavior of borrowers. A rational default model by borrowers‟ life-time utilities extending beyond the negative equity of mortgage is proposed and simulated. Split population model, which allows the separation of “probability of default” and “time-to-default” and the existence of “non-defaulters”, and have better fitting than normal hazard regression statistically, is used to empirically test the vii proposed rational default model. Although high-risk borrowers (e.g., mortgagors with extremely high LTV, No FICO, and Low FICO) have high probability of becoming defaulters, they are more “non-ruthless” in exercising default options because of their limited credit accessibility. Mortgage characteristics are less important in borrowers‟ decision on suboptimal default supporting that unexpected “trigger event” is critical for suboptimal default behaviors. Thirdly, the question on “how the banking market structures (contestability and concentration) affect non-agency residential mortgage supply and its performance?” is empirically studied. The results suggest that contestability in the banking market reduces credit supply and concentration in the banking market increases total credit supply during 2000s, when the market faces with the downturn in demand and in the existence of non-bank substitution suppliers. Furthermore, competitive contestability factor increases total credit supply, and reduces ex-post default risks compared with other market structure effects (e.g., monopoly contestable, monopoly inefficiency, and cut-throat competitive market). The results imply that collecting individual information (e.g., income, consumption preference, payment habits, and submarket status) is important in evaluating default risks of borrowers. Keywords: Mortgage, Default, Hazard, Self-Selection, Market Structure, Option, Crisis viii Deng, Y., J. 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Mohanty, 2010, "Geographic Deregulation and Competition in the US Banking Industry," Financial Markets, Institutions & Instruments 19, 63-94. 271 Appendix 1: Contestability and Concentration on Banking Markets How banking concentration and market contestability are measured? Concentration measured by the Gini coefficient indirectly reflects the market power of banks in the local markets. The presence of large banks measured by total bank deposit either in the top 25 in the US banking or control 10% local market share could increase the profitability of other banks (Pilloff 1999). Academics utilize price markups between prices and marginal cost, like Lerner index, H-statistics, to link the inputs cost with output prices Other researchers, however, argue for the use structural measures of competition using the marginal approach (Bresnahan 1982; Lau 1982) and also the input factor elasticity approach by Panzar and Rosse (PR, 1982, 1987). Based on limited data availability52 , three variables are used to capture market structure in different geographic scope: number of banks, Gini coefficient of U. S. banks as market concentration measure and PR H-statistics as market contestability (industry efficiency) measurement, Wharton‟s banking database of the University of Pennsylvania are used as inputs for computing the market structure variables. The data on income statement and bank location are available for the sample periods from 52 One significant distinction making banking industry apart from others is the banking regulation, mainly by governments representing Federal Reserve and the existence of property law (threat of new entrants, possibility of mega bank entering, substitution institutions). It would be better if there are city and zip-level banking regulation information, such as fraction of entry denied (the number of entry application denied as a fraction of the number of applications received from domestic and foreign entities, which can also act as contestability of the market), and activity restrictions (e.g., whether bank activities in the securities, insurance, and real estate markets, ownership, subprime mortgages are restricted or not). 272 1999 to 2008 on a quarterly basis for a total of more than 9000 banks, which give a pooled of 347,356 observations. 1) Bank Number The deregulation of the banking industry and increasing use of technologies including telecommunication, internet banking and automated teller machine (ATM) in the 1990s have resulted in declines in the number of banks over the periods 1999-2008 (Figure A1), except for some states such as Arizona, Alaska, California, Delaware, Florida, Georgia, Idaho, and Louisiana, where the number of bank increases through new banks and/or new branches. The numbers of banks and branches at the local level represent the market reach in the selected neighborhoods. Figure A. 1: Mean of Number of Banks in State Level through years Note: This figure is generated from bank income report of Wharton. The horizontal axis is year, and vertical value is the mean of the number of banks with loan in each state of U.S. at the specific year. 2) Gini Coefficient Measure of Bank Concentration Gini coefficient53 measures “inequality of banks”, which is defined as the area 53 Herfindahl-Hirschman Index of concentration (HHI) is also used, which at city level is calculated using the sum of the squared market shares. The smaller HHI indicates higher concentration level in the 273 between equality line and Lorenz curve divided by total area under the equality line: N G    [( X i  X i 1 )  (Yi  Yi 1 )] i 0 (A1) where Yi is the cumulative proportion (discrete density function) of the measured variable yi sorted in an ascending sequence; X i is the cumulative proportion of the number counts xi . G has its value in a close interval of [0 1], where a value “0” means equality, and a value “1” means inequality. As a measure of market concentration, a Gini coefficient of indicates a high level of concentration in the market. Gini coefficients are computed based on real estate loan value and total asset (equity and debt) from the sample of 72,570 bank-year observations over the 10-year periods. Gini Coefficients on bank concentration level for a panel subsamples of state year are computed,54 which include 541 resulted Gini values from 1999 to 2008 (excluding the missing Gini results for the AS (American Samoa) and FM (Federated States of Micronesia). By the definition of banking market concentration, New York in 1999 may have different Gini from the New York market in 2008. The market city scope. HHIL is firstly used, which is based on loan market share measured by percentage of total loans on total loan of banks in city level. Other general HHIA is based on the market share measured by percentage of total assets on total banks asset (Equity and debts) in city level, which is assumed to measure the bank industry concentration. The regression results of their sign are not changed by using proxy of HHIA and HHIL. 54 One can also use city level Gini. However, in the bank sample, there are totally 4,463 cities, having reports, while in this individual mortgage loan data, there are 16,845 cities. Matching through cities will create some small cities biases, where no banking reports, and also lose quite quantity number of individual mortgage performance data. Cities name is determined based on the post zip code. There are totally 18,443 cities (including municipality, town, village,etc.) following definition of U.S. Census 2000 http://www.census.gov/. Compared with the cities in bank reporting data and individual mortgage data, one can see that not all small cities have banks with real estate loan, and a lot of cities have not reported their bank account information. 274 concentration of neighbor states measured by Gini values change annually. Figure A2 shows that by loan values, the Gini coefficients reflect a relative high level of concentration, where the values fall within a narrow band of 0.965 to 0.940. In comparison, the Gini coefficients by total asset value of between 0.72 and 0.925 reflect a weaker concentration relative the loan value Gini coefficients. The two Gini coefficient series move in an opposite direction during the pre-crisis periods from 2002 to 2006. By loan size, the market shares of banks were diluted over the periods; but more big banks with larger total asset value emerged during the same periods. Empirical studies use the Gini coefficient by loan value from bank income statement. Figure A. 2: Concentrations of Bank Service in U.S. Market Note: This figure shows the histogram of Gini coefficient values based on total national bank reporting sample from Wharton, measuring the concentrations of bank service in U.S. market in general sense for each year from 1999 to 2008. The left histogram is using real estate loan amount of individual banks to gain its inequality level (GiniLoan); while the right side one is using total asset value (debt + equity) of individual banks to gain the inequality level (GiniTotalAsset). The value is quite close to (high inequality), indicating high concentration in banks and real estate loan service, even though the trend of general bank service concentration is different from that of loan service concentration through years. In the later parts, the Gini coefficient based on the loan amount in bank reporting data for the subsamples of each state and each year (from 1999 to 2008). 275 3) H-statistics for Bank Competiveness The use of H-statistic (Panzar and Rosse 1982; Panzar and Rosse 1987; Rosse and Panzar 1977) as a measure of contestability and competitiveness in the banking market has been widely adopted in the banking literature (Bikker and Spierdijk 2008; Bikker, Spierdijk and Finnie 2007; Claessens and Laeven 2004; Yildirim and Mohanty 2010). With the state-level bank data in the US, log-version of the H-statistics using the PR approach is estimated as:55 ln   0  1 ln F  2 ln PE  3 ln PCE   (A2) Unlike Bikker, Spierdijk, Finnie (2007) that use the first differences for the variables, the absolute (level) values for the total interest income56  ; annual expense on funds F ; personal expense PE ; and physical capital expense that include furniture, fixture, equipment and auto PCE , are used in the models. From Eq. (A2), the H-statistics at the state-level can be computed as the sum of the three input factor elasticity measures with respects to the bank‟s total revenue57: 55 The small bank sample at zip-code level restricts the estimation of the H-statistics at the zip-code level, and the MSA-level H-statistics are also less susceptible to estimation errors. 56 The total observation number of from year 1999 and 2008 quarterly bank reporting is 347,355 (excluding 17,259 missing explanatory variables, proportionally 4.97% missing). In average, there are about 7000 to 8000 banks reporting every year. Equation (3) is run for 560 times (56 states 10 years) to gain one H-statistics for each state in each year. Hence quarter reports are also included in regressions, following the time series rules based on Finnie, Spierdijk et al. (2007). The resulted competiveness parameters “H-statistics” for each state are extremely stably around the value “1”, which shows highly competiveness by traditional interpretation. However as other control variables are not included for quite a mount of missing data, they are interpreted for comparing the relative competiveness. H-statistic is biased for territory such as AS (American Samoa), FM (Federated States of Micronesia), GU (Guam), and VI (Virgin Islands), as number of bank in these states is smaller than 30. 57 The reasons to not follow the interpretation of Hstatistics of Bikker, Spierdijk, Finnie (2007), where they define monopoly and perfect cartel for ≤ , monopolistic competition or oligopoly for < 𝐻 < 1, perfect competition for = 1, are 1) the definition of H=1 criteria to market structure are based on cross-elasticity, instead of cost-return elasticity. 2) it is found that H-statistic value is affected by including different control variables. 276 H  1  2  3 (A3) Higher  parameters will result in higher H-statistic value. High H-statistic indicates that the banks are more likely to produce their total services at marginal costs. Hence, H-statistic increases with the bank‟s efficiency in its service operation in contestable sense. H-statistic could be interpreted as the state-year level efficiency, which is a proxy of contestability of the banking industry. This measurement follows the definition of contestability “if no price in that market can be in equilibrium when its magnitude is such as to enable an entrant to undercut it and nevertheless earn a profit”(Bailey and Baumol 1983), which actually stress the contestability is to ensuring “the lowest prices consistent with the financial viability of the firm”. Eq. (A2) are run using yearly data for a sample of 50 US states and other territories / islands (that are American Samoa (AS), Federated States of Micronesia (FM), Guam (GU), Puerto Rico (PR), Rhode Island (RI) and Virgin Islands (VI)), and repeat the estimates on yearly basis for the periods 1999-2009. The results as in Figure A3 show that the state-level bank efficiency decreases for the pre-subprime crisis periods from 2001 to 2006 admit increases in the number of loans originated. After 2006, it shows small increases in efficiency in subprime crisis period. 277 Figure A. 3: Mean of State-level H-statistic and the Number of Loans through year Note: The figure shows the State-level H-statistic through year. Higher H-statistic means high bank’s efficiency in its service operation in a contestable state level submarket. The right side vertical line is the total number of loans that originated at the specific years. 278 [...]... and spatial default clustering in default can be seen in some areas (Figure 1.6) In 2006, mortgage default concentrates in few states in the western part of the U.S (state of California), the eastern part of U.S., and Florida At the same time, geographic concentration of U.S banks in 2006 shows similar distributions with that of mortgage default (see Figure 1.7) The spatial distributions of banks and. .. equity position, and the default is known as “nonstrategic default Some borrowers are “non-ruthless” mortgagors, who do not default with out -of- money put option on negative house equity While others, known as “sub-optimal” defaulters, default with in-the-money put option (Deng, Pavlov and Yang 2005; Deng and Quigley 2008; Kau and Keenan 1995; Kau et al 1993; Lehnert and Passmore 2006; Vandell 1995) “non-ruthless”... paying the mortgage Credit score is a kind of statistical analysis score of a borrower‟s perceived creditworthiness 2 FICO risk score is a kind of credit scoring method developed by Fair Isaac&Co and universal in the residential mortgage field with a range of 300 to 850 There are three largest credit bureaus issuing borrowers credit report and FICO scores including Experian, Transunion and Equifax Strictly... high liquidity (Boot and Thakor 1993; DeMarzo 2005; Downing, Jaffee and Wallace 2005; Greenbaum Anjan and Stuart 1987; Hess and Smith 1988; Heuson, Passmore and Sparks 2001; Passmore and Sparks 1996; Pennacchi 1988) Exogenous defaults in the choice of securitization level (Downing, Jaffee and Wallace 2005; Downing, Stanton and Wallace 2005; Downing, Stanton and Wallace 2008), and whether securitization... banks and non-banks) are less stringent Their businesses are mainly in the subprime sectors where borrowers consist of low credit quality households.1 FICO2 is widely accepted by the lenders as observable information for credit evaluation to capture the risks of mortgagors Borrowers‟ credit history that includes delinquency (late payments), the amount of 1 Creditworthiness means the perception of how... based on some the implicit assumptions of exogenous 19 default risk and lender‟s zero/maximized profit, which might not be true in real economic On the other hand, borrowers walk away from the unamortized house in the event of residential mortgage default In standard financial option framework, mortgage holders have a put option their default option to default and a call option to prepayment The financial... Figure 1 7: Spatial Distribution of Market Concentration of U.S Banks in 2006 12 Figure 1 8: Default and Market Concentration 13 Figure 1 9: The Distribution Scatter of Gini Coefficient and State-level Aggregated Default Ratio 14 Figure 4 1: Utility Level under Different Combination of LTV and House size 91 Figure 4 2: Optimal Combination of LTV and House size to Maximize Life... utilities of housing and non-housing consumption 22 Empirical studies show that heterogeneous borrowers have different default behaviors (Deng 1997; Deng and Gabriel 2006; Deng and Quigley 2008; Deng, Quigley and Van Order 2000; Ong, Sing and Teo 2007), which support the existence of “nonstrategic behaviors” in residential mortgage market (Nonstrategic borrowers are defined as borrowers, whose default. .. that credit has been established, length of residence, and negative credit records (e.g., default, personal bankruptcies) are determinants of FICO scores When lenders use risk-based pricing to incorporate the credit history information into their mortgage pricing, borrowers with credit scores are assigned with different credit spreads Automatic underwriting reduces the operating costs of originating and. .. income, creditworthiness, borrowers‟ initial wealth, preference to house consumption and default tendency) 17 B) Non-Strategic Default The objective is to build a rational model to explain financial and nonfinancial incentives to mortgage default Theoretical model on mortgage default behaviors is proposed to explain the question of “Why do borrowers behave non-ruthlessly or sub-optimally on mortgage default? ” . CREDIT RISKS AND DEFAULT BEHAVIOR OF MORTGAGORS LIU BO NATIONAL UNIVERSITY OF SINGAPORE 2011 2 CREDIT RISKS AND DEFAULT BEHAVIOR OF MORTGAGORS. Split population model, which allows the separation of “probability of default and “time-to -default and the existence of “non-defaulters”, and have better fitting than normal hazard regression. for credit evaluation to capture the risks of mortgagors. Borrowers‟ credit history that includes delinquency (late payments), the amount of 1 Creditworthiness means the perception of how