High Risk Consumer
Credit Markets
Jonathan Levin
Gaston Eyskens Lectures
November 2013
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Roadmap
Lectures
1.
2.
3.
4.
Technology and Asymmetric Information
High Risk Consumer Credit Markets
Measuring Inefficiencies from Adverse Selection
Can Markets for Health Insurance Work?
Introduction
 Consumer credit markets long viewed as highly imperfect due to
informational frictions, especially for high-risk borrowers.
 Classic asymmetric information theories => too little credit.
 How should we understand the boom in consumer lending in the early
2000s, especially for high-risk or “subprime” credit?
 Start with a review of relevant background and evidence. Then describe
series of studies on subprime consumer lending looking at
– whether there was a high latent demand for credit; and what pricing /
credit availability changes might have triggered borrowing.
– informational problems facing lenders and whether they were relaxed
in early 2000s by credit scoring, access to credit, etc.
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Asymmetric Information in Credit Markets
 Theory of asymmetric information in credit markets (Jaffee &
Russell, 1976; Stiglitz & Weiss, 1981).
– Adverse selection: high risk individuals more willing to
borrow as they don’t expect to repay  higher interest
rates or larger loans disproportionally attract bad risks.
– Moral hazard: borrowers either can’t repay or have less
incentive to repay loans with high interest rates or high
principal  lenders may refuse to make higher-risk loans.
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Household Credit Constraints
 Evidence of household liquidity constraints
– Excess income sensitivity of household consumption, or even
“pay-check to pay-check” spending: Kuchler, 2013.
– Tax rebate studies: Parker 1999; Souleles, 1999; Johnson et al.
2006, Agarwal et al., 2007, many more.
– Credit card borrowing limits: Gross & Souleles, 2002;
– Labor markets: Card et al. 2007; Chetty, 2008.
 Why does it matter? Presence of household liquidity
constraints has wide-ranging implications.
– Fiscal policy, unemployment and other forms of social
insurance, housing policy, life-cycle models, etc.
– Issues particularly relevant for low-income households.
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Behavioral Economics of Borrowing
 Behavioral economics considerations
– Structure of borrowing --- get money today, pay tomorrow --conducive to myopic behavior (over-optimism, self-control,...)
– Opposite concern from asymmetric info: too much borrowing !
 Examples
– “Payday” lending and high fee credit arrangements.
– Usury laws and other forms of consumer protection
 Particularly if high fees or interest rates, need to be cautious
in assuming households act in their own best interest.
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Rise of Subprime Lending
 US household debt/income: 90% to 120% in 2000-07.
 Growth of “subprime” lending in the US, 1995-2005.
– Payday loan outlets: 1,000 to 25,000
– Americredit auto loans: $230m to $8,454m
– Subprime mortgage originations: $65bn … $625bn
 Housing market had the most macroeconomic consequence,
but expansion of consumer credit was much broader.
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Why the Rise of Subprime Lending?
 Credit supply
– Securitization, high demand for bonds, collateral seen as safe.
– Lenders thought that technology had overcome frictions.
 Credit demand
– Change in price? Interest rates fell, but not by that much ...
perhaps demand response was to another changing “price”?
– Alternatively, outward “shift” in demand, e.g. due to rising
inequality and stagnant incomes – Rajan (2011).
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Background on Subprime Study
 Describe research with Will Adams, Liran Einav and Mark
Jenkins, using company records from a big subprime lender.
 Attempt to provide:
–
–
–
–
Evidence of un-met credit demand among subprime borrowers.
Evidence of adverse selection and moral hazard problems.
Evidence on effects of credit scoring, risk-based pricing.
Insight into operation of credit markets for low income
population: how they work and what are the key features.
 References: Adams, Einav and Levin (2009 AER); Einav, Jenkins
and Levin (2012 EMA and 2013 RJE).
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Setting for the Study
 Large integrated auto sales and finance company
– Purchases cars at auction and re-sells with financing
 Customers: employed but low-income, bad credit
– Applicant incomes: majority are $20-35k
– About 15% are homeowners
– One-third have no bank account.
 Very detailed and complete data
– Applications, originations from June 2001-Dec 2004
– Payments and recoveries tracked through April 2006
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A Subprime Auto Lender
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Description of a Typical Transaction
 Buyer arrives on lot and applies for credit.
– Credit score determines car and financing offer.
 Key offer terms are price (not int. rate) and minimum down.
–
–
–
–
Minimum down payment of $400-1,500
Cars priced at $10-11k, with blue book value $5-6k.
Loans are 3-4 years, most at state APR caps (25-30%)
“Effective” interest rates are higher, up to 80%.
 Buyers finance heavily and default often
– Typical down payment < $1,000, with loan of $10k.
– Majority of loans (60+%) end in (early) default.
– Recoveries are low – 1/3 of defaults are “skips”.
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Defaults Happen Quickly
2.5
Low Risk
Medium Risk
High Risk
2.0
Density
1.5
1.0
0.5
0.0
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Fraction of Loan Paid
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Returns on Loans are Bimodal
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Predictive Risk Scores
 Strong incentive to identify bad loans in advance!
 Predictive scoring achieves significant risk stratification.
Risk Category
Low Risk
Medium Risk
High Risk
Default Rate
44%
64%
71%
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Evidence of liquidity constraints: Demand
 Probit model of purchase decision for applicants
𝑄𝑖 = 1 ⇔ 𝑋𝑖 𝛼𝑋 + 𝛼𝑝 𝑝 + 𝛼𝑑 𝑑 + 𝜀𝑖 ≥ 0
 Compare sensitivity to immediate and deferred payments (i.e.
minimum down payment and car price). If buyers aren’t
liquidity constrained, total payment is what matters
𝐸 𝑃𝑉 𝑜𝑓 𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑠 = 𝐷𝑜𝑤𝑛 + 𝜙 𝑃𝑟𝑖𝑐𝑒 − 𝐷𝑜𝑤𝑛
 Calculate assuming rational expectation of default,
– With 5% annual discount rate, is approximately 0.75.
– With 50% annual discount rate is around 0.5.
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Estimating demand: Down payment changes
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Pricing Variation
 Minimum down
– Multiple changes of $100-500 for a subset of credit
categories; and discrete jumps at credit score thresholds.
 Car price
– Rely on variation in list prices: two major changes in
markup schedule; plus discrete jumps at cost thresholds.
 Lots of robustness checks in the papers.
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Probit Estimates of Purchase Decision
 Estimated marginal effect on purchase probability
dProb/dx
Std Err.
Car Price ($000)
-0.030
(0.006)
Minimum Down ($000)
-0.340
(0.005)
 Demand extremely sensitive to minimum down:
$100 increase in minimum down => 9% fall in demand
 To get same effect, need $900 price increase ($50 a month)!
 Implied annual discount rate: 427%
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Evidence of Liquidity Constraints: Seasonality
 Demand spikes during tax season: Cash sales but not trade-ins!
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Evidence of Liquidity Constraints: Seasonality
 Tax rebates can be large: up to $4,500 due to EITC.
 Figure classifies customers by EITC eligibility, plots demand spike.
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Asymmetric Information
 Could asymmetric info be the cause of liquidity constraints?
 Try to test for moral hazard and/or adverse selection
– Moral hazard: larger loan leads to higher default risk
– Adverse selection: higher default risks take larger loans
 Both imply correlation of loan size + default.
 Ideal experiment to distinguish MH/AS: mandate participation,
1. Randomize loan size to measure MH
2. Allow choice of loan size to measure MH + AS.
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Larger Loans More Likely to Default
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Down Payment as Signal of Repayment
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Identifying Moral Hazard and Adverse Selection
 Starting point: Cox hazard model of default:
ℎ 𝑡𝑖 |𝐿𝑖 , 𝑥𝑖 = 𝑒𝑥𝑝 𝜃𝐿𝑖 + 𝑥𝑖 ′𝛿 ℎ0 𝑡𝑖
 Goal: identify causal effect of loan size L on default (MH).
 Problem: direct estimate will be confounded if unobservables affect both
default and down payment (i.e. coefficient will measure MH + AS, not MH)
 Solution: jointly model and estimate loan size (using tobit):
𝐿𝑖 ∗ = 𝛼𝐿𝑖 + 𝑥𝑖′ 𝛽 + 𝜀𝑖
𝐿𝑖 =
𝑃𝑖 − 𝑑𝑖
𝑖𝑓 𝐿𝑖 ∗ ≥ 𝑃𝑖 − 𝑑𝑖
𝑖𝑓 𝐿𝑖 ∗ < 𝑃𝑖 − 𝑑𝑖
 Key exclusion restriction: default depends on 𝐿 = 𝑃 − 𝐷 not directly on 𝑃
or 𝑑; exogenous variation in 𝑃, 𝑑 identify 𝜃.
 Implement with two-step control variable method.
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Estimates of Moral Hazard
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Expected Return and Loan Size
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Decomposing Moral Hazard and Adverse Selection
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Contract Pricing under MH and AS
 Einav-Jenkins-Levin: model of consumer demand that includes
– Decision to purchase, how much to finance, repayment
 Paper derives estimating equations from “standard” model of
consumer behavior, but do not require that interpretation.
 Key features of the model
‒ Larger loans to dampen repayment incentives (MH)
‒ Consumers differ along observable and unobservable
dimensions that affect all three decisions (capture AS).
 Estimate “complete” model using variation in min down and car
prices described above to identify AS/MH parameters.
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Model-Based Results
 Effect of car prices (eq. interest rates): repayment incentives…
– Higher car prices => larger stream of payments, but doesn’t last
as long. Doesn’t have much impact on originations.
– Perhaps surprisingly, little incentive to offer interest rate
discounts to low risk applicants (little demand response!).
 Effect of down payment requirements: control selection…
– Higher down payment requirement => fewer originations, but
higher quality and smaller loan sizes, so less defaults.
– Use risk score aggressively: for high risk applicants, use high min
down to avoid bad loans. But want to attract low risk applicants.
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Setting Down Payment Requirements
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Setting Down Payment Requirements
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Credit Scoring and Adverse Selection
 Adoption of credit scoring and risk-based pricing has been a
major innovation in credit markets.
 Model-based analysis (EJL, 2012)
– Risk-based down payment requirements limits AS.
– Also competitive effects (barrier for rivals)
 Natural experiment analysis (EJL, 2013)
– Look at adoption of credit scoring in 2000-2002.
– Match equivalent borrowers before and after adoption.
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Effect of Credit Scoring: Model-Based
 Without scoring, lender is exposed to adverse selection
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Effect of Credit Scoring: Model-Based
 Risk-based pricing forces higher risks to pay more down.
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Effect of Credit Scoring: Model-Based
 And lowers default rates via screening + smaller loan sizes.
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Effect of Credit Scoring: Natural Experiment
 Adoption of credit scoring in 2000-2002
‒ Match equivalent borrowers before and after adoption.
‒ Study change in offered/originated loans to different groups.
 Credit scoring allowed two forms of tailored lending
– Higher down payments for riskier borrower (as above)
– Larger loans (wider set of cars) offered to low risks.
 Overall, very large effect of credit scoring on profitability.
‒ About $1,000 per loan!
‒ Importance of down payments and loan sizes roughly equal.
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Welfare and Behavioral Issues
 We were not able to say much about consumer welfare.
 Expansion of low-income lending presumably
– Helped some households that were credit-constrained
– Allowed other households to get into debt trouble.
 Welfare tricky once we move away from revealed preference
assumption that agents act in own (ex ante) best interest.
 To make progress, need information beyond choice data.
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Conclusions
 Striking feature of the high-risk consumer credit markets
– Willingness of borrowers to take large, high-interest loans
– Can’t rely on borrowers to avoid bad loans => lending is difficult.
 Ability to sort borrowers using predictive models of default
extremely important for profitable lending.
 Down payment requirements, rather than interest rates / prices
appear to be the key screening barrier for buyers.
 Similar features seem to apply in subprime mortgages
– Lower down payment requirements => rapid increase in originations,
with risky borrowers taking on substantial leverage.
– Of course, had the additional feature that collateral value deteriorated
sharply once defaults began and housing market cooled.
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