4.3.1.1 Borrower-specific Characteristics
Borrower illiquidity, which arises from shocks to or fluctuations of borrowers’ residual in- come, is a primary borrower-specific factor affecting mortgage termination. Borrowers can become illiquid if their income decreases (e.g., due to unemployment) or if their non-housing non-discretionary expenses (e.g., medical expenses) increase, resulting in insufficient resid- ual income to cover mortgage payment. Income volatility from seasonal employment can increase the risk of borrower illiquidity off-season.
Prior literature (e.g., Morton 1975, Sandor & Sosin 1975, Vandell & Thibodeau 1985,
Elul et al. 2010, Demyanyk et al. 2010,Jagtiani & Lang 2011,Chan et al. 2013) examines multiple characteristics that reflect borrower income (or illiquidity) risk. Some examples include current income, employment status, employment history, length of employment in current job, occupation, number of dependents, marital status, non-housing wealth, and government assistance. The first four characteristics capture the level and stability of bor- rowers’ income in the past, which can predict future income level and stability. The other characteristics reflect non-housing non-discretionary expenses, which predict borrowers’ ability to withstand negative income shocks. Of these borrower illiquidity factors, I can observe only borrower income at origination. I define IN COM E as the ratio of the me- dian borrower income at the MSA-year level to the individual borrower income. Borrower income is in the denominator to ensure that it is increasing in risk. Illiquidity risk increases as borrower income decreases relative to the MSA median income.8
Borrowers’debt burden can affect their ability to pay back their mortgages. Everything else equal, borrowers with greater debt burden are more likely to default on their mortgage than borrowers with lower debt burden because they are more likely to become illiquid over the mortgage term. Prior research (e.g.,Elul et al. 2010,Demyanyk et al. 2010,Chan 7This aggregation assumes that all individual components are equally important, which can reduce the quality of the composite risk score if some components are more important than others.
8In 5.5, I test the sensitivity of my results to defining IN COM E relative to other local area income benchmarks such as MSA median household income from the Census database and MSA personal income per capita from the BEA database.
et al. 2013) documents that indicators of borrower debt burden such as debt payment-to- income, debt-to-income,credit card utilization, andexistence of a second mortgage predict mortgage defaults. Since I do not have access to information about non-mortgage debt, I use the mortgage loan-to-income ratio, in lieu of debt-to-income ratio, as a proxy for debt burden. I argue that this ratio captures overall debt burden to a reasonable degree because a mortgage is typically the largest household debt. I define the mortgage loan-to-income ratio (LT I) as the MSA median mortgage loan-to-income ratio divided by the borrower mortgage loan-to-income ratio, where the MSA median mortgage loan-to-income is the median loan-to-income for all mortgage applications in the MSA during the same year.
Other borrower characteristics associated with mortgage default include credit score, age, existence of coborrower, and race, of which I can observe the last two. Households with two earners are less likely to become illiquid if one earner experiences income shocks. Consistent with this,Chan et al.(2013) find that loans with a coborrower are less likely to default. I create an indicator variableI.N OCOBORR that equals one when a mortgage has no coborrower, and zero otherwise.
Prior research (e.g., Berkovec, Canner, Gabriel & Hannan 1994, Firestone, Van Order & Zorn 2007,Chan et al. 2013,Jiang, Nelson & Vytlacil 2014a) provides evidence of higher likelihood of default for Black and Hispanic borrowers. Berkovec et al.(1994) use a sample of FHA-insured mortgages originated over the period 1987-1989 and find that minority borrowers are more likely to default, after controlling for other determinants of default.
Firestone et al.(2007) examine 3-year fixed-rate mortgages originated over the period 1993- 1997 and purchased by Freddie Mac, and they find that minority borrowers default at a higher rate, even after controlling for other factors. Chan et al.(2013) document a similar relation between race and mortgage default using a sample of first-lien adjustable-rate and 30-year fixed-rate mortgages originated in New York City between 2004 and 2007. Jiang et al.(2014a) also find similar association between borrower race and default using loan- level data for a sample of mortgages originated by a major national mortgage bank during the 2004-2008 period.9 I create two indicator variables I.BLACK and I.HISP AN IC for borrowers’ race and ethnicity, respectively.
4.3.1.2 Loan-specific Characteristics
Prior research (e.g.,Sandor & Sosin 1975, Morton 1975, Campbell & Dietrich 1983,Deng et al. 2000,Elul et al. 2010,Jagtiani & Lang 2011,Chan et al. 2013,Elul 2016) documents
9None of these studies claim a causal link between borrower race and defaults, andChan et al.(2013) argue that the association between race and default may be driven by differential treatment of minority borrowers by the mortgage industry or other unobservable factors correlated to race.
an association between the likelihood of mortgage termination and loan characteristics such as loan-to-value,mortgage payment-to-income, loan amount, the lack of proper docu- mentation (e.g., missing borrower income), mortgage age, and term-to-maturity. Of these characteristics, I can observe the loan amount, missing borrower income, and mortgage age. In addition, I can construct a proxy for loan-to-value ratio. I do not use mortgage age in my thesis because it is equal to zero for all mortgages at origination.
The size of the loan can affect the likelihood of mortgage termination because it affects borrower’s home equity and periodic payments. Elul et al. (2010) and Elul (2016) find that borrowers with larger loans are more likely to default than those with smaller loans, and Jagtiani & Lang (2011) find that jumbo loans are more likely to be foreclosed. I defineLOAN as the ratio of the dollar amount of the mortgage loan to the MSA median mortgage amount.
Lack of proper documentation can reflect lenders’ poor borrower screening that may result in approval of riskier mortgages. Low documentation mortgages can be riskier if the lack of documentation allows high-risk borrowers to obtain mortgages at more favorable terms when they would otherwise not qualify for these mortgages. Consistently,Elul et al.
(2010) show that low documentation mortgages have greater default risk. I define an indicator variable I.N OIN COM E, which equals one if borrower income is missing, as a proxy for loans with low documentation.10
The loan-to-value ratio (LTV), the ratio of the amount borrowed to the value of the property, reflects home equity and the amount the lender can recover in case of default.11
An LTV greater than 100 percent (i.e., negative home equity) can trigger strategic defaults by borrowers who do not want to pay loans that are worth more than the collateral. A large literature (e.g., Sandor & Sosin 1975,Morton 1975,Campbell & Dietrich 1983,Deng et al. 2000, Elul et al. 2010, Jagtiani & Lang 2011) documents a positive association between LTV and the likelihood of mortgage default. I use the loan-to-estimated median value ratio (LT M EDV), defined as the ratio of the loan amount to an estimated census tract median house value, as a proxy for LTV. I adjust the census tract median value of owner-occupied units from the decennial Census data using the changes in MSA-level house price indexes from FHFA to obtain estimated median house values at the census tract level.
10Note that I.IN COM E does not capture all possible types of low documentation mortgages such as those without employment verification.
11LTV is one of the primary risk indicators used in practice. For example, borrowers are required to make a minimum 20 percent down payment, which is equivalent to maximum of 80 percent LTV, to qualify for uninsured conventional mortgages.
4.3.1.3 Property-specific Characteristics
Property characteristics such ascurrent and expected value, current condition, and whether it is owner-occupied can affect mortgage termination risk, of which I can observe only the last one. Chan et al. (2013) find that mortgages secured by an owner-occupied property are more likely to default than mortgages secured by properties not occupied by the owner, which include investment properties. A potential explanation for this finding is that mort- gages secured by investment properties, unlike those secured by owner-occupied properties, are less sensitive to income shocks because the property itself can generate rent income to cover mortgage payments. Another explanation is that borrowers who buy investment properties or second homes (e.g., vacation homes) are high-income borrowers unlikely to default on their mortgage. However, in my validation tests (section 4.5), I find evidence of the opposite relation between owner-occupancy and mortgage risk. I specifically find that BHCs with more mortgages secured by owner-occupied properties have lower mort- gage delinquencies and charge-offs and that owner-occupied mortgages are more likely to have high interest rates. As a result, I reverse-coded the high-risk indicator variable for owner-occupancy and creates the indicator variable I.N OT OW N OCC which equals one if the property is not owner-occupied, and zero otherwise.
4.3.1.4 Geographic-specific Characteristics
Prior research (e.g., Sandor & Sosin 1975, Vandell & Thibodeau 1985, Elul et al. 2010,
Ghent & Kudlyak 2011, Chan et al. 2013, Anacker 2015) documents that mortgage ter- mination risk is associated with local area characteristics such as current and expected housing market conditions, median house value, home ownership rate, percentage of vacant units, median house age, neighborhood life cycle, local area unemployment rate, median income, minority percentage, and neighborhood desirability rating. I can observe all these characteristic except for neighborhood desirability rating, which is a function of the other geographic characteristics.
Local area housing market conditions affect property values, which in turn affect the likelihood of negative home equity. Using census-tract-level data about foreclosure starts during the period January 2007 to June 2008, Anacker (2015) documents that mortgages in areas with low median house value are more likely to be foreclosed than those in areas with high median house value. Anacker (2015) also finds that home ownership rate, va- cancy rate, and median house age are positively associated with foreclosure rates, and that census tracts in central cities and mature suburbs have greater default rate than those in developing suburbs.
I create the following variables to capture local area housing market condition.
M EDV ALU E is the average value of owner-occupied units, averaged to the MSA level, divided by the median value of owner-occupied units in the census tract.12 I use the change
in MSA-level house price indexes over the previous one-year and five-year periods (∆HP I1 and ∆HP I5, respectively) as proxies for the local area housing market condition. I create an indicator variable I.LIF ECY CLE that equals one if a census tract is a central tract or is in a mature suburb, and zero otherwise. A central census tract is one in the central areas of a city, which are more mature than the peripheral and suburban areas. A mature suburb is a suburban area with a median house age of 30 years or more. I group central cities and mature suburbs together because Anacker (2015) finds that tracts in central cities and mature suburbs exhibit similar mortgage foreclosure rate, which is higher than for tracts in developing suburbs. Other proxies for local area housing market conditions include the proportion of owner-occupied properties (OW N RAT E) and the proportion of vacant properties (V ACRAT E).
Local area unemployment rate and economic condition can be associated with mortgage risk if they reflect current and expected borrower illiquidity or housing market condition.
Elul et al. (2010) document that an increase in county unemployment rate is positively associated with mortgage default. Chan et al. (2013) find that neighborhoods with lower median income exhibit more defaults than those with higher median income.
I use the following variables to measure local area unemployment rate and economic conditions. U N EM P RAT Eis the county unemployment rate from the BLS database, and ∆U N EM P RAT E is the annual change in U N EM P RAT E. I use the personal income data from BEA to create four county-level local area income measures, namely P ERIN C,
EARP ERJ OB, N ON HOU SW LT H, and GOV ASSIST. I use local area income data from the BEA database instead of the HMDA/CRA census database because the BEA database is updated annually and contains more up to date information. P ERIN C is the MSA personal income per capita divided by the county personal income per capita. Simi- larly, EARP ERJ OB is the MSA average earnings per job divided by the county average earnings per job. N ON HOU SW LT H is the MSA non-housing wealth per capita divided by the county non-housing wealth per capita. GOV ASSIST is the county government assistance per capita divided by the MSA government assistance per capita.
Chan et al. (2013) find that mortgage default rate increases with the percentage of Black population in the neighborhood, after controlling for other default risk indicators.
12The MSA average value of owner-occupied is the census tract-level median value of owner-occupied units averaged over all census tracts in the MSA, used as a proxy for the MSA median value of owner- occupied units.
They argue that this association “likely indicates the differential treatment of Black neigh- borhoods by the mortgage industry” (Chan et al. 2013, p.100). To reflect the local area minority percentage, I use the percentage of Black population (BLACKP ERC) and His- panic population (HISP AN ICP ERC) in the census tract. The corresponding high-risk indicatorsI.BLACKP ERC andI.HISP AN ICP ERCare equal to one if the census tract has a majority of Black and Hispanic population, respectively.
Sandor & Sosin (1975) and Vandell & Thibodeau (1985) show that neighborhood de- sirability rating is negatively associated with mortgage risk premium. While I do not have access to such desirability ratings, I argue that the other geographic factors discussed above proxy for neighborhood desirability. For example, current and expected housing market conditions, unemployment rates, median income, minority percentage, and median house value can all influence the desirability rating of a neighborhood. Krysan, Couper, Farley & Forman(2009) show that a neighborhood’s social class and racial composition affect its desirability, providing evidence supporting the claim that the above socio-economic factors affect neighborhood desirability.