Screening for Moral Hazard and Adverse Selection: Evidence from the Home Equity Market

1. 1 Screening for Moral Hazard and Adverse Selection: Evidence from the Home Equity Market Sumit Agarwal, Federal Reserve Bank of Chicago Brent W. Ambrose, Penn State University Souphala Chomsisengphet, OCC Chunlin Liu, Univ. of Nevada-Reno

2. 2 Theoretical Motivation Stiglitz and Weiss (1981) Despite the use of interest rate or collateral to screen borrowers, lenders still face imperfect information and are not able to entirely distinguish borrower risks. Overall expected loan profitability declines even when loan rate increases High-risk applicants will accept the higher interest rate while low-risk applicants will exit the applicant pool. Adverse selection problem ? credit rationing Bester (1985) Menu of contracts containing combinations of interest rate & collateral Borrowers contract selection reveals their risk level ex ante High-risk borrowers: select lower collateral requirement (higher rates) Low-risk borrowers: select higher collateral requirement (lower rates) Impact of adverse selection on credit rationing is then eliminated Let me begin with 2 fundamental theory papers that serve as an economic setting for our study. In their 1981 paper, SW argue that despite… In their model, they show that overall expected profit declines even when the lender increases the loan rate because of high-risk applicants will accept the higher rate and stay in the applicant pool, while low-risk applicants will leave the pool. This adverse selection problem leads lender to ration credit. Providing a counter argument to SW, Bester argues that lenders can offer a menu of contracts containing combinations of interest rate and collateral as a risk sorting mechanism. Where borrowers will self-select contract that ex ante reveal their risk level. High-risk borrowers will choose contract that has… While low-risk borrowers will choose contract that has… In turn, the impact of adverse selection on credit rationing is then eliminated.Let me begin with 2 fundamental theory papers that serve as an economic setting for our study. In their 1981 paper, SW argue that despite… In their model, they show that overall expected profit declines even when the lender increases the loan rate because of high-risk applicants will accept the higher rate and stay in the applicant pool, while low-risk applicants will leave the pool. This adverse selection problem leads lender to ration credit. Providing a counter argument to SW, Bester argues that lenders can offer a menu of contracts containing combinations of interest rate and collateral as a risk sorting mechanism. Where borrowers will self-select contract that ex ante reveal their risk level. High-risk borrowers will choose contract that has… While low-risk borrowers will choose contract that has… In turn, the impact of adverse selection on credit rationing is then eliminated.

3. 3 Theoretical Motivation Definitions: Adverse selection is an ex ante event that occurs when potential borrowers respond to credit solicitations offered by banks. Riskier borrowers respond to credit offerings at higher interest rates and/or lower collateral requirements Moral hazard usually refers to the incentives (or lack thereof) for borrowers to expend effort to fulfill their contractual obligations.

4. 4 Our Objectives Research Questions Part 1: Do borrowers self-select loan contracts designed to reveal information about their risk level (Bester, 1985)? Conditional on the borrowers’ contract choice, does adverse selection still exist (Stiglitz and Weiss, 1981)? Part 2: Do lender efforts to mitigate adverse selection and moral hazard problems effectively reduce default risks ex post? If so, by how much? In this paper, we focus on the home equity market. In contrast to previous studies in secured lending, we follow applicants through the … I will come back to this point in the next three slides when I cover the data. Our objectives in this paper can be divided into 2 parts based on the lender’s initial screening and additional screening. We first want to assess whether borrowers self-select ….and conditional on borrower’s contract choice, does …. Then we also want to assess whether secondary screening to mitigate adverse selection and moral hazard problems can effectively reduce default risks ex post. If yes, by how much?In this paper, we focus on the home equity market. In contrast to previous studies in secured lending, we follow applicants through the … I will come back to this point in the next three slides when I cover the data. Our objectives in this paper can be divided into 2 parts based on the lender’s initial screening and additional screening. We first want to assess whether borrowers self-select ….and conditional on borrower’s contract choice, does …. Then we also want to assess whether secondary screening to mitigate adverse selection and moral hazard problems can effectively reduce default risks ex post. If yes, by how much?

5. 5 Home Equity Credit Market Home equity represents a large (and growing) segment of the consumer credit market. Market Size (2005): $702 billion Typical Home Equity Menu: Risk-based pricing according to loan-to-value Less than 80% LTV 80% to 90% LTV Greater than 90% LTV Thus, ideal setting for examining adverse selection and moral hazard.

6. 6 This chart describes better the home equity credit origination process that is captured in our dataset. At the top (Step 1), we observe an applicant choosing for example either a home equity loan or line, a first- or second-lien product, and down payment size. Once the applicant has chosen, the lender screens the application based on observable information and then makes a decision to either accept, reject, or subject the applicant to additional screening in order to induce borrower type or effort. During the secondary screening, the lender makes a counteroffer either to further mitigate moral hazard or to further mitigate adverse selection. We identify a counteroffer designed to induce borrower effort (i.e., to mitigate moral hazard) as one that the lender lowers the LTV and/or changes the product type from loan to line. And we identify a counteroffer designed to induce borrower type (i.e., to mitigate adverse selection) as one in which the lender increases the LTV or changes the product from a line to a loan. Then in step 3, we observe the applicant’s response to the lender’s counteroffer. This chart describes better the home equity credit origination process that is captured in our dataset. At the top (Step 1), we observe an applicant choosing for example either a home equity loan or line, a first- or second-lien product, and down payment size. Once the applicant has chosen, the lender screens the application based on observable information and then makes a decision to either accept, reject, or subject the applicant to additional screening in order to induce borrower type or effort. During the secondary screening, the lender makes a counteroffer either to further mitigate moral hazard or to further mitigate adverse selection. We identify a counteroffer designed to induce borrower effort (i.e., to mitigate moral hazard) as one that the lender lowers the LTV and/or changes the product type from loan to line. And we identify a counteroffer designed to induce borrower type (i.e., to mitigate adverse selection) as one in which the lender increases the LTV or changes the product from a line to a loan. Then in step 3, we observe the applicant’s response to the lender’s counteroffer.

7. 7 Data Home equity contract originations from a large financial institution 108,117 consumers applying for home equity contract from lender’s standardized menu (March - December 2002) 8 Northeastern states: MA, ME, CT, NH, NJ, NY, PA, RI Observe Borrower’s initial contract choice Lender’s primary screening (accept, reject, or additional screening) Lender’s counteroffer Borrower’s response to counteroffer Borrowers’ repayment behavior (origination - March 2005) Other observable information Borrower’s credit quality and purpose for the loan Demographics: income, debts, age, occupation Our sample comes from a large financial institution that originates home equity credits. We have more than 108k consumers applying for one of the lender’s standardized menu of contracts for home equity credits between March & Dec 2002 in the 8 Northeastern states. In the dataset, we directly observe borrower’s initial contract choice, lender’s primary screening, … In addition, we also observe some other information such as the borrower’s credit quality, purpose for taking out the credit, and some demographics. Our sample comes from a large financial institution that originates home equity credits. We have more than 108k consumers applying for one of the lender’s standardized menu of contracts for home equity credits between March & Dec 2002 in the 8 Northeastern states. In the dataset, we directly observe borrower’s initial contract choice, lender’s primary screening, … In addition, we also observe some other information such as the borrower’s credit quality, purpose for taking out the credit, and some demographics.

8. 8 Data This is our sample distribution of the dynamic process captured in our dataset. We have more than 108k applicants choosing a menu of credit contract. Following its initial screening based on observable information, the lender accepted 57% of the applicants, rejected 11% of the applicants, and performed additional screening on 31% of the applicants. Of the 33k that had to be screened again, 68% received a counteroffer to induce borrower effort and 31% received a counteroffer to induce borrower type. Of these 33k that received a counteroffer, about 37% rejected the lender’s counteroffer and 62% accepted the counteroffer. And so we have about 83k borrowers that originated the home equity credit and we are able to follow their repayment pattern. This is our sample distribution of the dynamic process captured in our dataset. We have more than 108k applicants choosing a menu of credit contract. Following its initial screening based on observable information, the lender accepted 57% of the applicants, rejected 11% of the applicants, and performed additional screening on 31% of the applicants. Of the 33k that had to be screened again, 68% received a counteroffer to induce borrower effort and 31% received a counteroffer to induce borrower type. Of these 33k that received a counteroffer, about 37% rejected the lender’s counteroffer and 62% accepted the counteroffer. And so we have about 83k borrowers that originated the home equity credit and we are able to follow their repayment pattern.

9. 9 Empirical Analysis Part 1: Primary Screening

10. 10 1.1: Contract Choice Three contract choices ? borrower risk sorting mechanism LTV ? 80 ? pledging at least 20 cents per dollar loan (j=1) 80 < LTV < 90 ? pledging 20-10 cents per dollar loan (j=2) LTV ? 90 ? pledging 10 cents or less per dollar loan (j=3) Test whether riskier borrowers (lower credit quality) tend to self-select a higher risk contract (offer less collateral) W = borrower credit quality X = control variables (demographics, prop type, loan purpose, etc...) 3 contract choices serve as borrower risk sorting mechanism Contract choices are defined by the LTV (the amount of collateral borrower is willing to pledge). We estimate a multinomial logit model of a borrower contract choice of an 80-90 LTV or LTV > 90 (relative LTV ? 80). Wi represents borrower i’s credit quality as measured by the borrower’s FICO score. Xi represents a vector of control variables. 3 contract choices serve as borrower risk sorting mechanism Contract choices are defined by the LTV (the amount of collateral borrower is willing to pledge). We estimate a multinomial logit model of a borrower contract choice of an 80-90 LTV or LTV > 90 (relative LTV ? 80). Wi represents borrower i’s credit quality as measured by the borrower’s FICO score. Xi represents a vector of control variables.

11. 11 1.1: Contract Choice – Table 3 Independent Variables: Borrower Characteristics: Borrower risk (FICO and FICO^2) Log(Income) Log (Borrower Age) Log (House Tenure) Debt-to-income ratio Contract Characteristics First or Second Lien position indicator Line or Loan indicator Use of funds indicator (refinance, consumption, home improvement) First mortgage indicator Second home indicator Condo indicator Employment Control Variables Employment tenure – Log(Years on the Job) Type of employment self-employed, retired, home-maker Location Control Variables (state)

12. 12 1.1. Contract Choice –Table 3 Less credit-worthy borrowers (lower FICO) are more likely to apply for higher LTV home equity products (pledging less collateral per dollar). For example, Relative to a borrower with a score of 800, a borrower with FICO score of 700 is 18.4% more likely to select an 80-90 LTV contract than one with LTV ? 80. Relative to a borrower with a score of 800, a borrower with FICO score of 700 is 19.6% more likely to apply for a LTV > 90 than one with LTV ? 80. Consistent with predictions by Bester (1985).

13. 13 1.1 Contract Choice Conclusion: We find evidence that borrowers do select contracts that reveal information about their risk level.

14. 14 1.2: Lender response (Table 5) If lender systematically screens for adverse selection and moral hazard, then we should observe a positive correlation between the likelihood of additional screening and collateral offered (LTV), holding all else constant. Multinomial logit model: The likelihood of a lender rejecting an applicant or subjecting an applicant to additional screening based on LTV, borrower risk characteristics, loan characteristics, and other control variables. Base case: loans that were accepted out-right (without additional screening) In response to the borrower’s contract choice, the lender can accept, reject, and subject the borrower to additional screening. We estimate a multinomial logit model to assess factors that may affect the lender’s decision to reject or subject the an applicant to additional screening. Loans that were accepted out-right are used as the base case. In response to the borrower’s contract choice, the lender can accept, reject, and subject the borrower to additional screening. We estimate a multinomial logit model to assess factors that may affect the lender’s decision to reject or subject the an applicant to additional screening. Loans that were accepted out-right are used as the base case.

15. 15 1.2: Lender response (Table 5) Lender more likely to conduct additional screening or reject contracts with < 20 cents per dollar of collateral than those with > 20 cents per dollar of collateral. For example, LTV > 90 contract is 18.4% more likely to be rejected (15.8% more likely to be screened again) than LTV = 80 contract. 90 ? LTV > 80 contract is 8.7% more likely to be rejected (12% more likely to be screened again ) than LTV = 80 contract. 80-90 LTV contract: lender more likely to conduct additional screening than reject. LTV > 90 contract: lender more likely to reject than conduct additional screening.

16. 16 1.2: Lender Response Conclusion: Evidence that lender followed standard underwriting protocol.

17. 17 1.3: Test for Adverse Selection Test for the presence of adverse selection conditional on the borrower’s choice of contract type Examine the loan performance of the 62,251 borrowers whose applications were accepted outright (without additional screening). Competing-Hazard Model of Default & Prepayment: The time to prepayment, Tp, and time to default, Td, are random variables that have continuous probability distributions, f(tj), where tj is a realization of Tj (j=p,d). The joint survivor function conditional on time-varying covariates where gjn(r,H,X) ? time-varying function of the relevant interest rates, property values, loan characteristics, borrower characteristics Z ? macro-economic factors, ?p and ?d ? unobservable heterogeneity factors

18. 18 1.3 Test for Adverse Selection If adverse selection based on unobserved risk characteristics is present, then we should find a significant relationship between initial LTV and ex post default. If adverse selection is not present, then we should observe no systematic relationship between initial LTV and default risk.

19. 19 1.3: Competing Risks Model (Table 6) Independent Variables: Borrower Characteristics: Borrower risk (FICO and FICO^2) Log(Income) Log (Borrower Age) Log (House Tenure) Debt-to-income ratio Contract Characteristics Lender LTV First or Second Lien position indicator Line or Loan indicator Use of funds indicator (refinance, consumption, home improvement) First mortgage indicator Second home indicator Condo indicator Auto pay Time-varying Option Characteristics Current LTV (CLTV and CLTV^2) Prepayment Option Difference in LTV Difference in Housing Value Account Age (Age, Age^2, Age^3) Employment Control Variables Employment tenure – Log(Years on the Job) Type of employment self-employed, retired, home-maker Location and Economic Control Variables (state dummy and unemployment rates)

20. 20 1.3: Evidence of Adverse Selection (Table 6) Observable risk characteristics 100 point ? FICO ? default risks ? 43% (prepay ? 15%) Rate refinancing ? 3.7% less likely to default (2.8% more likely to prepay) No first mortgage ? 6.8% less likely to default (3.1% less likely to prepay) One percentage point higher DTI ? 2.1% more likely to default (2.2% more likely to prepay) ? current LTV (e.g., 1% house price depreciation) ? 4% more likely to default (1% less likely to prepay) than borrowers whose current LTV ? (i.e., house price appreciation)

21. 21 1.3: Evidence of Adverse Selection (Table 6) After controlling for the observable risk characteristics, borrowers with higher initial LTV contract (pledging less collateral per dollar loan) are more likely to default. Relative to borrowers with LTV = 80, those with 80 < LTV < 90 are 2.2% more likely to default (4.5% less likely to prepay) Those with LTV ? 90 are 5.6% more likely to default (6.6% less likely to prepay)

22. 22 1.3: Evidence of Adverse Selection Conclusion: Evidence consistent with the presence of adverse selection on unobservables in the home equity lending market (Stiglitz & Weiss, 1981). Evidence also consistent with findings of adverse selection in the credit card market (Ausubel, 1999).

23. 23 Empirical Analysis Part II: Secondary Screening

24. 24 2.1: Lender’s Counteroffer Factors that affect the lender’s decision to make one of the two counteroffers after the secondary screening. Counteroffer to further mitigate moral hazard: if lender lowers LTV (increasing collateral required per dollar loan to induce borrower effort) and/or switches the product from a home equity loan to a home equity line. Counteroffer to further mitigate adverse selection: if lender increases LTV and/or switches the product from a home equity line-of-credit to a home equity loan (increasing the APR to induce borrower type). Estimate a logit model to assess the likelihood of a lender making a counteroffer designed to mitigate adverse selection.

25. 25 2.1: Adverse Selection Counter (Table 8) Higher risk borrowers less likely to receive adverse selection counter offer. Relative to borrower with a score of 800, borrower with a FICO score of 700 is 24.6% less likely to receive a counteroffer designed to mitigate adverse selection than one designed to mitigate moral hazard. Borrowers who overvalue their property value (relative to the bank’s estimated value) One percentage point ? in the lender’s LTV ratio over the borrower’s LTV ratio increases by 3.1% the probability that the lender counteroffers with a contract designed to mitigate adverse selection. For example, we find that borrowers who are less credit-worthy, who overvalue their property, who has higher debt-to-income ratio are more likely to receive an adverse selection counteroffer. While borrowers who are rate refinancing or who has no first mortgage are less likely to receive an adverse selection counteroffer.For example, we find that borrowers who are less credit-worthy, who overvalue their property, who has higher debt-to-income ratio are more likely to receive an adverse selection counteroffer. While borrowers who are rate refinancing or who has no first mortgage are less likely to receive an adverse selection counteroffer.

26. 26 2.1: Adverse Selection Counter Conclusion Lender does systematically screen borrowers for adverse selection and moral hazard.

27. 27 2.2: Borrower response to counteroffer 2 Logit models of borrower response: the likelihood of a borrower rejecting a “moral hazard” or “adverse selection” counteroffer. Does secondary screen reintroduce adverse selection? Do low credit risk applicants reject counteroffer?

28. 28 2.2. Moral hazard counteroffer (Table 10a) Each one percentage point decrease in the counteroffer interest rate relative to the original interest rate decreases the likelihood of a borrower rejecting the moral hazard counteroffer by 2.4%. If lender estimates a 10 percentage point higher LTV than borrower, then likelihood of borrower rejecting moral hazard counter increases by 0.65%. Indicates that counter offer introduces additional adverse selection.

29. 29 2.2. Adverse Selection Counter (Table 10b) Each one point increase in the counteroffer interest rate over the original interest rate increases the likelihood of a borrower rejecting the counteroffer designed to mitigate adverse selection by 1%. Less risky borrowers (lower FICO scores) more likely to reject counter offer. Results confirm that lender’s mitigation efforts introduce additional adverse selection. The results for impact of the APR Difference is still significant but the economic impact is smaller – a borrower who is offered a higher rate than the original contract rate is only 1 percent more likely to reject the adverse selection counteroffer. Applicants selecting a home equity loan, rate refinancing, or owns a second home are less likely to reject a counteroffer designed to mitigate adverse selection. Moreover, the economic significance of applicants without a first mortgage rejecting a counter offer designed to mitigate adverse selection is much higher than for rejecting a counteroffer designed to mitigate moral hazard. The results for impact of the APR Difference is still significant but the economic impact is smaller – a borrower who is offered a higher rate than the original contract rate is only 1 percent more likely to reject the adverse selection counteroffer. Applicants selecting a home equity loan, rate refinancing, or owns a second home are less likely to reject a counteroffer designed to mitigate adverse selection. Moreover, the economic significance of applicants without a first mortgage rejecting a counter offer designed to mitigate adverse selection is much higher than for rejecting a counteroffer designed to mitigate moral hazard.

30. 30 2.3: Effectiveness of counteroffer (Table 11) Estimate a competing-risks hazard model Test the effectiveness of the lender’s adverse selection and moral hazard mitigation efforts Sample Include all loans accepted following both the primary and secondary screening 83,411 borrowers 2 dummy variables identify Moral hazard counteroffer Adverse selection counteroffer

31. 31 2.3: Effectiveness of counteroffer (Table 11) Relative to loans that did not receive additional screening, the risk of default ex post declines by 12.2 percent for loans that the lender ex ante required additional collateral and/or switched the contract from a home equity loan to a home equity line. Relative to loans that did not receive additional screening, the risk of default ex post declines by 4.2 percent for loans where the lender ex ante reduced the required collateral and/or switched the contract from a credit line to a home equity loan.

32. 32 2.3: Effectiveness of counteroffer (Table 11) Considerable difference in the marginal impact suggests that the lender’s effort to mitigate moral hazard ex ante is more effective than the effort to mitigate adverse selection in reducing the risk of default risk ex post. consistent with lender being relatively more successful in inducing additional borrower effort ex post.

33. 33 Main Conclusions -- #1 Borrower’s choice of credit contract does reveal information about her risk level. Less credit-worthy borrowers are more likely to select a contract requiring less collateral Even after controlling for observable risk characteristics, lender continues to face adverse selection problems due to unobservable information.

34. 34 Main Conclusions -- #2 Lender’s efforts ex ante to mitigate adverse selection and moral hazard can be effective in reducing credit losses ex post. Secondary screening and counteroffer designed to mitigate moral hazard reduce default risk ex post by 12%. Additional screening and counteroffer to mitigate adverse selection reduce default risk ex post by 4%.

35. 35 Main Conclusions -- #3 Mitigation efforts impose costs (higher prepayment rates) Moral hazard mitigation increase the risk of prepayment by 11%. Adverse selection mitigation increase the risk of prepayment by 2.9%. Direct impact on secondary market investors and their ability to predict prepayment speeds on a securitized portfolio.