1. HMDA and New Developments in Fair Lending—What We Have Learned Presented by: Joseph T. Lynyak III ReedSmith LLP 1901 Avenue of the Stars – Suite 700 Los Angeles, CA 90067-6078 Tel.(310) 734-5407 1301 K Street NW, Suite 1100 Washington, DC 20005 Tel: (202) 414-9487 jlynyak@reedsmith.com ©2005 ReedSmith LLP. All rights reserved. MBA’s Legal Issues In Mortgage Technology San Diego, California November 30, 2005

2. What’s New in 2004  For the first time, we can identify “higher priced loans” – Those with HOEPA flags – Those that have “reportable rate spreads” – Manufactured housing loans 2

3. What’s New in 2004  We can compare by race or ethnicity – Incidence – either in terms of relative probabilities or “odds ratios” – Magnitudes, levels, severities – to determined whether for higher priced loans minorities pay more on average than do Caucasians or White, Non- Hispanic borrowers 3

4. Different Categories for Comparison  All loans – or subgroups of loans  First (senior) lien loans  Second (junior) lien loans  Conventional loans (not FHA, VA, etc.)  Business related (e.g., gender, race, ethnicity n/a) 4

5. Different Categories for Comparison  Rate spread loans  Purchase, refinance, home improvement  In metropolitan areas – or everywhere  By state, by MSA, by county  With or without early apps 5

6. The Fed (Finally) Issued Its Report  2004 HMDA data reported to the FFIEC and the Fed in March of 2005  Federal Reserve report and public data issued on September 12 th  CDs now available—all 20 gigabits worth  Critical Table 11 data not issued until late September  Enormous volume of data now being analyzed 6

7. 100s of Ways to Use These Numbers  Incidence of likelihood an ethnicity or racial group will receive a reportable loan compared to another racial or ethnic group  Media comparisons made between African Americans and Caucasians and between Hispanics and Whites (e.g., non-Hispanic Caucasians)  Typically higher relative probabilities shown in the prime market 7

8. 100s of Ways to Use These Numbers  Prime Example: 100 loans for a prime lender – 700 White, 100 AA, 200 Hispanic. They have only 10% rate spread loans (100 total) with 40 AA, and 15% Hispanic. Disparity (odds ratio) AA: W is 30/5.7=5.26 and that for Hispanics is 2.63  Nonprime Example: The nonprime lender with same distribution of applicants who has a 50% rate spread incidence across the board. Then 350 Whites, 50 AA and 100 Hispanics have higher priced loans but the disparity (odds ratio) does not exist. Disparity AA: W and Hispanic: W is 50/50=1 8

9. 100 Ways to Use Those Numbers  Rate spread comparisons between ethnicities and racial groups  Comparisons for those borrowers who received reportable loans  Table 11 APR data permits calculation of differences between groups by simple arithmetic  Fed made state aggregation more difficult by not posting data aggregated by state 9

10. The National View: The Odds Ratios 10

11. The National View: Average Differences by Race (Compared to Whites) 11

12. The Rate Spreads Observed 12 Summary of Nationally HMDA Reported Rate Spreads PurchaseRefinance Home Improvement Race/EthnicityStatisticFirst LienJunior LienFirst LienJunior LienFirst LienJunior Lien African American Probability of Reported Rate Spread 31.9761.6934.2845.1142.3037.67 Average Rate Spread 4.186.604.317.504.648.40 Hispanic Probability of Reported Rate Spread 19.6157.4518.6136.9524.8524.31 Average Rate Spread 3.946.304.077.064.447.94 White Non-Hispanic Probability of Reported Rate Spread 8.6129.7712.8323.3219.8215.41 Average Rate Spread 4.086.434.167.144.407.96

13. HOEPA Loans 13 Summary of Nationally HMDA Reported HOEPA Loans RefinanceHome Improvement Race/EthnicityStatisticFirst Lien Junior LienFirst Lien Junior Lien African American Probability of HOEPA 0.18%1.00%0.80%2.54% Hispanic Probability of HOEPA 0.16%1.71%0.60%2.04% White Non-Hispanic Probability of HOEPA 0.14%0.77%0.63%1.26% Clearly, the incidence of HOEPA loans is low—with less than one percent of first lien loans being HOEPA loans.

14. Incidence – Top 5 Prime Lenders 14 Top 5 Prime Lenders Relative Incidence of FLOOC Loans with Reported Rate Spreads Hispanic/Non-Hispanic 1.62 2.13 2.01 1.19 1.14 1.70 1.48 1.20 1.60 1.04 0.00 1.00 2.00 3.00 4.00 5.00 6.00 Lender A Lender B Lender CLender DLender E Odds Ratio PurchaseRefinance

15. Level of Rate Spreads – Top 5 Prime Lenders 15 Top 5 Prime Lenders Difference in FLOOC Mean Rate Spreads (African American - White) 18 19 7 6 -2 -15 17 18 11 -5 -30 -20 -10 0 10 20 Lender ALender BLender CLender DLender E Odds Ratio PurchaseRefinance

16. Level of Rate Spreads – Top 5 Prime Lenders 16 Top 5 Prime Lenders Difference in FLOOC Mean Rate Spreads (Hispanic - Non-Hispanic) -18 -21 -8 -2 98 -32 -4 4 -2 -16 -50 0 50 100 Lender A Lender B Lender CLender DLender E Odds Ratio PurchaseRefinance

17. Incidence – Top 5 Subprime Lenders 17 Top 5 Non-Prime Lenders Relative Incidence of FLOOC Loans with Reported Rate Spreads African American/White 1.18 1.10 1.41 1.29 1.20 1.27 1.09 1.28 1.41 1.36 0.00 1.00 2.00 Lender F Lender G Lender H Lender I Lender J Odds Ratio PurchaseRefinance

18. Incidence – Top 5 Subprime Lenders 18 Top 5 Non-Prime Lenders Relative Incidence of FLOOC Loans with Reported Rate Spreads Hispanic/Non-Hispanic 0.95 0.82 0.91 0.89 0.95 0.97 0.88 0.79 0.53 1.01 0.00 1.00 2.00 Lender FLender GLender HLender ILender J Odds Ratio PurchaseRefinance

19. Level of Rate Spreads – Top 5 Non-Prime Lenders 19 Top 5 Non-Prime Lenders Difference in FLOOC Mean Rate Spreads (African American - White) 13 12 18 12 9 25 12 19 -10 -20 -10 0 10 20 30 Lender FLender GLender HLender ILender J Odds Ratio PurchaseRefinance

20. Level of Rate Spreads – Top 5 Non-Prime Lenders 20 Top 5 Non-Prime Lenders Difference in FLOOC Mean Rate Spreads (Hispanic - Non-Hispanic) -12 -15 9 -11 -5 2 -3 6 -2 -20 -10 0 10 20 30 Lender FLender GLender HLender ILender J Odds Ratio PurchaseRefinance

21. Analyzing and Explaining Data— Regression Analysis—Raw Differences  Controls for only—  Race  Ethnicity  Refers generally to data taken directly from FRB tables 21

22. Analyzing and Explaining Data— Regression Analysis—HMDA Regression  Includes raw difference variables  Loan type  Property type  Lien status  Occupancy type  Loan amount < $100,000  Loan amount between $100,000 and $333,700  Loan amount between $333,700 and $$641,650  Purpose  Tract income level  Income to MSA median  Income to loan amount  Principal city indicator  Tract percent 22

23. Analyzing and Explaining Data—Regression Analysis—HMDA Regression With State/County Controls  Controls for—  HMDA variables  Inserts “dummy” variable for  States  Counties 23

24. Analyzing and Explaining Data—Regression Analysis—Credit Variables and Risk-Based Pricing  Controls for—  HMDA variables  Inserts lender-specific credit factors—such as—  LTV buckets  FICO scores  Prepayment penalty indicators  DTI  Refinance or cash-out indicators  Documentation type  Loan term  Loan product 24

25. Analyzing and Explaining Data—Regression Analysis—Credit Variables and Risk-Based Pricing and State/County Controls  Controls for—  All rate sheet variables  Adds “dummy” variables for states and counties 25

26. Necessary Terminology—Statistical Significance  All data is not created equal—to be useable, data must be “statistically significant”  Non-economists must always focus on economic techniques and nomenclature  Overwhelming weight of authority—if the results are not statistically significant—the data is not legally admissible  Chi Square Test/Fisher Exact Test  T-Test 26

27.  Data has been far more difficult to analyze than previously assumed  Rash of adverse publicity has not occurred to date  Delay in posting Table 11 racial and ethnicity data has further slowed analysis  Determination not to post state-wide data has also complicated analysis—just MSAs 27 Recent Developments

28.  Results confirm effectiveness of risk-based pricing  Raw HMDA data can identify pockets of incidence or rate spread disparities that require further study  Application of increased sophistication of regression analyses can completely explain and/or narrow disparities to geographic or product lines 28

29. Recent Developments  Many lenders have already completed several levels of regression analysis  Verifies risk-based pricing models or significantly limits fair lending concerns to discrete geographic areas  In areas of local concern—broker issues may need to be addressed 29

30. Observations  Fed has verified that 200 or more referrals have been made to the DOJ  Referrals appear to reflect disparities after a HMDA regression analysis  Agencies have hinted at expanding HMDA review to review of APRs on non-reportable loans  Other Federal Banking Agencies in varying states of follow-up  AGs Yet to Weigh-in 30

31. Observations  Homeownership in America is at or near record highs  Risk-based pricing has expanded access to credit & significantly contributed to the growth in the availability of mortgage credit, which fosters increased homeownership  While HMDA data may show some differences in denial rates, industry is effectively serving more borrowers  While differences in loan pricing exist, publicly available HMDA data and other objective risk factors can explain the differences  The price of a mortgage is based on a variety of factors related to the economic risk involved  Financial literacy would help improve credit and shopping to lower prices 31

32. Observations  Regardless of credit-based explanations—should there be a public policy debate to address and to resolve the national disparities and incidence issues between African Americans and Whites?  Is financial education and literacy the key?  Is closer broker monitoring needed? 32