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

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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

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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

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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.

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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?

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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.

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Data

• Home equity contract originations from a large
  financial institution
• 108,117 consumers applying for home equity
  contract from lenders standardized menu (March -
  December 2002)
• 8 Northeastern states MA, ME, CT, NH, NJ, NY,
  PA, RI
• Observe
• Borrowers initial contract choice
• Lenders primary screening (accept, reject, or
  additional screening)
• Lenders counteroffer
• Borrowers response to counteroffer
• Borrowers repayment behavior (origination -
  March 2005)
• Other observable information
• Borrowers credit quality and purpose for the
  loan
• Demographics income, debts, age, occupation

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Data
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Empirical Analysis

• Part 1 Primary Screening

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1.1 Contract Choice

• Three contract choices ? borrower risk sorting
  mechanism
• LTV ? 80 ? pledging at least 20 cents per dollar
  loan (j1)
• 80 lt LTV lt 90 ? pledging 20-10 cents per dollar
  loan (j2)
• LTV ? 90 ? pledging 10 cents or less per dollar
  loan (j3)
• 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...)

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1.1 Contract Choice Table 3

• Independent Variables
• Borrower Characteristics
• Borrower risk (FICO and FICO2)
• 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

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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 gt 90 than one with LTV
  ? 80.
• Consistent with predictions by Bester (1985).

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1.1 Contract Choice

• Conclusion
• We find evidence that borrowers do select
  contracts that reveal information about their
  risk level.

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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)

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1.2 Lender response (Table 5)

• Lender more likely to conduct additional
  screening or reject contracts with lt 20 cents per
  dollar of collateral than those with gt 20 cents
  per dollar of collateral.
• For example,
• LTV gt 90 contract is 18.4 more likely to be
  rejected (15.8 more likely to be screened again)
  than LTV 80 contract.
• 90 ? LTV gt 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 gt 90 contract lender more likely to reject
  than conduct additional screening.

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1.2 Lender Response

• Conclusion
• Evidence that lender followed standard
  underwriting protocol.

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1.3 Test for Adverse Selection

• Test for the presence of adverse selection
  conditional on the borrowers 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 (jp,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

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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.

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1.3 Competing Risks Model (Table 6)

• Independent Variables
• Borrower Characteristics
• Borrower risk (FICO and FICO2)
• 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 CLTV2)

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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)

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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 lt LTV lt 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)

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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).

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Empirical Analysis

• Part II Secondary Screening

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2.1 Lenders Counteroffer

• Factors that affect the lenders 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.

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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 banks estimated value)
• One percentage point ? in the lenders LTV ratio
  over the borrowers LTV ratio increases by 3.1
  the probability that the lender counteroffers
  with a contract designed to mitigate adverse
  selection.

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2.1 Adverse Selection Counter

• Conclusion
• Lender does systematically screen borrowers for
  adverse selection and moral hazard.

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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?

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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.

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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 lenders mitigation efforts
  introduce additional adverse selection.

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2.3 Effectiveness of counteroffer (Table 11)

• Estimate a competing-risks hazard model
• Test the effectiveness of the lenders 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

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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.

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2.3 Effectiveness of counteroffer (Table 11)

• Considerable difference in the marginal impact
• suggests that the lenders 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.

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Main Conclusions -- 1

• Borrowers 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.

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Main Conclusions -- 2

• Lenders 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.

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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.