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

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. Theoretical Motivation.

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

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  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. 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

  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. 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?

  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. 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

  7. Data

  8. Empirical Analysis Part 1: Primary Screening

  9. 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...)

  10. 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)

  11. 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).

  12. 1.1 Contract Choice • Conclusion: • We find evidence that borrowers do select contracts that reveal information about their risk level.

  13. 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)

  14. 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.

  15. 1.2: Lender Response • Conclusion: • Evidence that lender followed standard underwriting protocol.

  16. 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

  17. 1.3 Test for Adverse Selection • If adverse selection based on unobserved risk characteristics is present, then we should find a significant relationship between initialLTV and ex post default. • If adverse selection is not present, then we should observe no systematic relationship between initial LTV and default risk.

  18. 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)

  19. 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)

  20. 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)

  21. 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).

  22. Empirical Analysis Part II: Secondary Screening

  23. 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.

  24. 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.

  25. 2.1: Adverse Selection Counter • Conclusion • Lender does systematically screen borrowers for adverse selection and moral hazard.

  26. 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?

  27. 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.

  28. 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.

  29. 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

  30. 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.

  31. 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.

  32. 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.

  33. 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%.

  34. 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.

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