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

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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.