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 Tel.(310) K Street NW, Suite 1100 Washington, DC Tel: (202) ©2005 ReedSmith LLP. All rights reserved. MBA’s Legal Issues In Mortgage Technology San Diego, California November 30, 2005

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

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

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

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

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

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

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

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

The National View: The Odds Ratios 10

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

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 Average Rate Spread Hispanic Probability of Reported Rate Spread Average Rate Spread White Non-Hispanic Probability of Reported Rate Spread Average Rate Spread

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.

Incidence – Top 5 Prime Lenders 14 Top 5 Prime Lenders Relative Incidence of FLOOC Loans with Reported Rate Spreads Hispanic/Non-Hispanic Lender A Lender B Lender CLender DLender E Odds Ratio PurchaseRefinance

Level of Rate Spreads – Top 5 Prime Lenders 15 Top 5 Prime Lenders Difference in FLOOC Mean Rate Spreads (African American - White) Lender ALender BLender CLender DLender E Odds Ratio PurchaseRefinance

Level of Rate Spreads – Top 5 Prime Lenders 16 Top 5 Prime Lenders Difference in FLOOC Mean Rate Spreads (Hispanic - Non-Hispanic) Lender A Lender B Lender CLender DLender E Odds Ratio PurchaseRefinance

Incidence – Top 5 Subprime Lenders 17 Top 5 Non-Prime Lenders Relative Incidence of FLOOC Loans with Reported Rate Spreads African American/White Lender F Lender G Lender H Lender I Lender J Odds Ratio PurchaseRefinance

Incidence – Top 5 Subprime Lenders 18 Top 5 Non-Prime Lenders Relative Incidence of FLOOC Loans with Reported Rate Spreads Hispanic/Non-Hispanic Lender FLender GLender HLender ILender J Odds Ratio PurchaseRefinance

Level of Rate Spreads – Top 5 Non-Prime Lenders 19 Top 5 Non-Prime Lenders Difference in FLOOC Mean Rate Spreads (African American - White) Lender FLender GLender HLender ILender J Odds Ratio PurchaseRefinance

Level of Rate Spreads – Top 5 Non-Prime Lenders 20 Top 5 Non-Prime Lenders Difference in FLOOC Mean Rate Spreads (Hispanic - Non-Hispanic) Lender FLender GLender HLender ILender J Odds Ratio PurchaseRefinance

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

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

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

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

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

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

 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

 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

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

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

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

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