Underwriting, Automated Underwriting, and Discrimination

1. Underwriting, Automated Underwriting, and Discrimination • Scott Susin • Economist • FHEO Office of Systemic Investigations • HUD FHEO Policy Conference • July 22, 2010

2. Overview • Underwriting • 3 Cs: Credit, Capacity, Collateral • Automated Underwriting -- what’s automated and what’s not • Not: product choice, verification, appraisal, pricing, marginal/borderline applicants • Facts and figures • AUS reduced denial disparities? Who’s left out?

3. Underwriting Factors: Credit • Foreclosures, bankruptcies, liens and/or judgments • Mortgage delinquencies; Credit delinquencies, repossessions, collections, or charge-offs • Credit accounts: type, age, limits, usage and status of revolving accounts • Recent request for new credit • Combine into a score that predicts default (FICO, Fannie/Freddie proprietary sytems)

4. Underwriting Factors: Capacity • Debt ratios: • monthly housing expense-to-income ratio • monthly debt payment-to-income ratio • Salaried versus self-employed borrower • Cash reserves • Number of borrowers

5. Underwriting Factors: More Capacity • Loan Characteristics: • Product: a 15-, 20-, and 30-year fixed rate, a balloon/reset mortgage, an adjustable rate mortgage, etc. • Purpose of Loan: purchase or refinance (cash-out or no cash-out)

6. Underwriting Factors: Collateral • Borrower's total equity or down payment • Appraisal • Property type: a 1-unit or 2- to 4- unit detached property, Condominium Unit or Manufactured Home • Property use: Primary Residence, Second Home or Investment Property

7. Automated Underwriting Systems • Began to be adopted in mid-1990s, today used for almost every loan • Computer balances different factors rather than human judgment • Underwriting factors enter into a formula that predicts default • Requires data on 100,000s or millions of loans and default outcomes to develop • Fannie Mae: Desktop Underwriter • Freddie Mac: Loan Prospector

8. Automated Underwriting Systems • Feed in credit report, other underwriting factors, AUS provides decision • Decision is Yes/No, Approve/Refer, not Score • Decision has conditions (documentation)

9. “Computers Don’t Discriminate”What’s Not Automated • Before AUS is run • Choice of Product, Lender • AUS says No (Refer) • Manual Underwriting • AUS says Yes (Accept) • Income/Asset Verification • Appraisal • Independent of AUS • Pricing

10. Choice of Lender & Product • Choice of Product • Often made by loan officer/broker • Opportunity for steering • e.g., Lenders where most borrowers don’t document income. • Higher loan price but less work for lender • Choice of Lender • Steer to subprime division, lender • E.g., Baltimore v. Wells Fargo charges Wells steered customers to subprime division

11. AUS returns “refer” – Manual Underwriting • Explain circumstances • Temporary illness, unemployment. Won’t recur. • Borrower probably needs assistance making the case • HDS testing study found that real estate brokers more likely to assist white homebuyers than minorities. Same for mortgage brokers, loan officers?

12. “Lenders Want to Make Loans” • But neither do they want to spend their time on loans that don’t close. • Brokers presumably make a judgment about how to allocate their time, and prejudices can easily enter into their decision

13. AUS Returns “Accept” – Verification Follows • Two common reasons for a loan to be denied are: unable to verify income/assets • Income can be complicated and time-consuming to verify • Skilled trades • Tips, commissions, bonuses • Government programs such as disability • Do LOs make as much effort to verify Minority borrower’s income as whites?

14. Income Verification • Potentially subjective judgments • How much documentation is required? • Letter from government verifying disability income, or from doctor too? • Is income stable, likely to continue? • Letter from employer required? • NY Times: many lenders now assume that women on maternity leave won’t return to work

15. Pricing • Pricing (interest rate, points, and fees) is not determined by AUS. It’s negotiable. • Lenders would like a higher price • Yield Spread Premiums or Overages • Bonuses to broker/LO for selling a higher-rate loan

17. The Unscored: Racial and Ethnic Patterns

18. Are the Unscored Creditworthy? • Catch-22: It’s hard to know because there’s no data on them in credit files • You’d expect: • Many have little experience paying bills (young, thin files) • suggests less creditworthy • Few have major derogatories (bankruptcy, foreclosure, collections) • If they defaulted, they’d have credit scores! • suggests more creditworthy

19. Are the Unscored Creditworthy? • Brookings examined consumers in a few states where utility bills are reported to credit bureaus • Those who have scores only because of utility bills have about average delinquency rates (consistent with scores in the 680-740 range) • So people in other states, without scores but with utility bills in their name, probably also have average scores. • FTC examined use of credit scores to predict auto insurance claims. • Scores are very predictive of insurance claims. • People without scores have about average claims risk.