1. 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September 25, 2007 Confidential information; please do not circulate

2. 2 Fair Lending Analytics The Division of Insurance and Research provides analytical support for fair lending examinations conducted by the FDIC’s Division of Supervision and Consumer Protection Statistical analysis to compare credit outcomes for a target group (a racial- ethnic minority group or females) with credit outcomes for a control group (NH whites or males)

3. 3 DIR Fair Lending Examination Support  Offsite screening: DIR developed and runs statistical screens using HMDA pricing data to identify banks that appear to be “at risk” in terms of disparities in mortgage loan pricing to racial/ethnic minorities or women  Analytical support for on-site exams: Conduct statistical analysis of loan data to investigate potential pricing disparities identified by FDIC screens or by DSC examiners  Statistical analysis  What measure of pricing will be examined (dependent variable)  What sample of loans to review  What variables to include in the model  What statistical tests to use

4. 4 FDIC-Supervised Banks  Many relatively small institutions  Many in rural areas  Examiner-identified non-mortgage pricing cases  Emphasis on assisting with preliminary screening analysis: Use readily available data to evaluate disparity. If a HMDA outlier, does the suspicious pattern continue in recently collected HMDA data?

5. 5 HMDA Pricing Data  Since 2004, Home Mortgage Disclosure Act Data have included information on “higher-priced” loans  Loans secured by a first lien having an APR of more than 300 basis points above that of an equivalent-maturity Treasury security  Loans secured by subordinate lien having an APR of more than 500 basis points above the APR of an equivalent- maturity Treasury security  2002 Revisions to Reg C by the FRB: “gathering information about the higher-priced segment of the market;”  But, loan characteristics and the underlying interest rate environment affect the extent to which HMDA higher- priced loans capture loans in the subprime or near-prime market

6. 6 HMDA Pricing Data Pricing information reported  APR spread for a higher-priced loans: The APR of the higher-priced loan minus the APR of an equivalent maturity mortgage  HOEPA flag: indicates whether a loan is a HOEPA loan (TILA, reg Z) having rates or fees above a certain percentage or amount. (See the FFIEC website.) Rate-spread is more than 800 basis points (8 percent) for 1st- lien loans and more than 1,000 basis points (10 percent) for subordinate-lien loans. HOEPA points and fees threshold: Total points and fees paid exceed the greater of 8 percent of the total loan amount, or a dollar amount that is adjusted annually for inflation. For 2004, the dollar amount was $499.

7. 7 Other information about application/loan characteristics was also added starting with the 2004 HMDA data  Lien status, manufactured housing flag,  Disposition of mortgage application (e.g. accepted, denied, withdrawn) now includes information about preapprovals HMDA Pricing Data

8. 8 FDIC HMDA Data Screens  In 2005, DIR developed analytical “screens” to flag institutions having the largest pricing disparities on loans  To particular racial/ethnic minorities (compared to the control group of non-Hispanic Whites) and  To females (compared to a control group of loans where a male applicant is present)  We analyze pricing disparities measured for specific categories of loans (loan product types), so that we are comparing “apples to apples”

9. 9  Banks that have the largest, statistically significant, pricing disparities among FDIC-supervised in any particular loan product category for any target group are flagged by the FDIC screens and we provide DSC with lists of flagged institutions  The FDIC screens also flag institutions with large and significant denial rate disparities FDIC HMDA Data Screens

10. 10 Pricing Disparities Examined Disparities in incidence of higher-priced loans: The percent of target group loans that are higher-priced loans minus the percent of control group loans that are higher-priced.  If 60 % of blacks received higher-priced loans, but only 30% of non-Hispanic whites received higher-priced loans, the disparity for the bank would be 30 percentage points Disparities in average rate-spread on higher-priced loans: The average reported rate-spread on higher-priced target group loans minus the average rate spread reported on higher-priced loans to the control group  If the average rate spread on higher-priced loans to blacks is 190 basis points and the average rate spread on the higher-priced loans to non-Hispanic whites is 125 basis points; then the disparity in the average spread is 75 basis points Disparities in incidence of HOEPA loans: The percent of target group loans flagged as HOEPA loans minus the percent of control group loans flagged as HOEPA loans

11. 11 Loan Product Groups Examined  Conventional 1st-lien home purchase, 1-4 family, owner- occupied  Government 1st-lien home purchase, 1-4 family, owner- occupied  Conventional 1st-lien home improvement, 1-4 family, owner- occupied  Conventional 1st-lien refinance, 1-4 family, owner-occupied  Government 1st-lien home improvement and refinance, 1-4 family, owner-occupied  All 1st-lien manufactured housing, owner-occupied (conventional and government; home purchase, home improvement, and refinance)  All 2nd-lien home purchase, owner-occupied (conventional and government; 1-4 family and manufactured housing)  All 2nd-lien home improvement and refinance, owner- occupied (conventional and government; 1-4 family and manufactured housing). Disparities in incidence of higher- priced loans

12. 12 Examples: FDIC HMDA Racial-Ethnic Screens  What is a large disparity?  “Large” is measured relative to the mean disparity evident for FDIC-supervised banks that make loans in the product area to the target group  Choose threshold for flagging banks (in terms of standard deviations from the mean)  Loan products  Conventional first-lien home purchase loans (product 1)  Conventional first-lien home refinance loans (product 4)  Pricing measure  Incidence of higher-priced loan  Mean of reported rate-spreads on higher priced loans

13. 13 Confidential: Please do not circulate

14. 14 Confidential: Please do not circulate

15. 15 Confidential: Please do not circulate

16. 16 Confidential: Please do not circulate

17. 17 FDIC HMDA Outliers  Statistical significance—observed differences in outcomes for the target and the control group would be very unlikely to occur if the source of the differences were just random chance  Harder to find statistical significance with small samples; requires disparities of larger magnitude.  Moreover, our screens do not control for any factor except for the product characteristics that are used to classify loans in a particular product group: lien status, loan purpose, site built versus manufactured housing, owner occupancy, conventional versus govt-lending program

18. 18 Examples of Variables not used in FDIC MDA Screens  HMDA variables:  Loan size, Borrower income (front-end DTI)  Presence of co-applicant  Race of applicant/co-applicant (in gender screens)  Gender of applicant/co-applicant (in race screens)  Market where the loan was made (MSA)  Application was associated with a pre-approval  Legitimate factors used in pricing that are not included in HMDA, such as: credit history, debt service burden, house value (needed to compute LTV)

19. 19 Analytical support for on-site exams:  In depth, bank-specific statistical analysis of loan data to investigate potential pricing disparities identified by FDIC screens or by DSC examiners  What measure of pricing will be examined (dependent variable)  What variables to include in the model  What sample of loans to review  What statistical tests to use  Analysis depends on pricing policies and realities  Criterion Interviews conducted by DSC examiners ascertain pricing policies  Across lending units  Across products offered  Across markets  Available data

20. 20 Modeling Pricing Outcomes Pricing policies and realities  Are there clear non-discriminatory criteria for pricing?  To what extent are outcomes automated (factors specified on rate sheets,) versus judgmental (factors considered in judgmental fashion—such as “customer relationship”)?  To what extent are rate sheet deviations permitted?  What are the YSP compensation agreements?  What are the bank’s markets and the “competitive factors” ?  Is reality consistent with bank policies?  Is there documentation of factors used to price in loan files?  Are exceptions to rate sheets documented?

21. 21 Modeling Pricing Outcomes  Dependent variable—it depends  Note rate or APR  Rate sheet deviation  Yield spread premium  Incidence of loans extended in high-price unit  Explanatory variables—it depends  Examiner criterion interview  Information in electronic files or compiled by DSC examiners from loan files  Hard to include information if the bank doesn’t have it on file

22. 22 Examples of Pricing Factors  Loan Amount  Credit Score  DTI  LTV  Deposit relationships  Performance on past loans

23. 23 Evaluating Outcomes Statistical significance—observed differences in outcomes for the target and the control group would be very unlikely to occur if the source of the differences were just random chance  Again, hard to find significance for small samples  Robustness checks Economic significance—what is the magnitude of the pricing differential?

24. 24 Questions?