InfoBytes Blog

Spotlight on HDMA: Your Institution's Public HMDA Data and What to Do with It

In an annual rite of autumn, on September 18 the Federal Financial Institutions Examination Council released 2011 Home Mortgage Disclosure Act (HMDA) data for U.S. mortgage lenders.  The public data contains information regarding nearly all home mortgage applications acted on in the prior calendar year, designated by loan purpose (i.e., home purchase, home refinance and home improvement).  The HMDA data covers home loan applications made to over 7,600 U.S. financial institutions, including banks, savings associations, credit unions and mortgage companies, and contains information on approximately 11.7 million applications, 7.1 million originations and 2.9 million purchases.

HMDA data provides a wealth of mortgage industry-related information, including data on application and loan volume, the proportion of loans backed by the Fair Housing Administration and Veterans Administration, and lender concentration in the mortgage market.  However, its most important function and the reason HMDA was enacted is the role the data plays in fair lending enforcement.  Toward this end, the outcome of each home mortgage loan application is classified according to the applicant’s race, ethnicity and gender.  HMDA data further allows analyses based on the site of the subject property, as well as the location of the lender.

It is important to recognize that while HMDA data alone does not prove or disprove discrimination, it is an important component of fair lending examinations.  According to the Federal Reverse Board, which provides a lengthy analysis that accompanies the annual release of HMDA data:

Although the HMDA data include some detailed information about each mortgage transaction, many key factors that are considered by lenders in credit underwriting and pricing are not included.  Accordingly, it is not possible to determine from HMDA data alone whether racial and ethnic pricing disparities reflect illegal discrimination.  However, analysis using the HMDA data can account for some factors that are likely related to the lending process.

In other words, notwithstanding its limitations, HMDA data is often the starting point for regulators seeking indicia of possible discrimination.  Additionally, advocacy groups and the media frequently focus on lending disparities suggested by HMDA data.  These factors, and the very public nature of HMDA data, make it important that lenders fully understand their own data and consider it in the context of what the national HMDA data suggests.

Here’s a list of the key fair lending-related findings from the 2011 data:

• Denial Rates - Black applicants and Hispanic-white applicants had notably higher denial rates than non-Hispanic white applicants.  The denial rates for conventional home-purchase loans were 30.9 percent for blacks, 21.7 percent for Hispanic whites, 14.8 percent for Asians, and 11.9 percent for non-Hispanic whites.
• Higher-Priced Loans - The incidence of higher-priced lending across all products in 2011 was about 3.7 percent, up from 3.2 percent in 2010.  Black and Hispanic-white borrowers were more likely to obtain higher-priced loans than were non-Hispanic white borrowers. 
• Loan Penetration in Distressed Areas - Lending activity has not yet rebounded in neighborhoods experiencing high levels of distress.  Home purchase lending in census tracts identified by the Neighborhood Stabilization Program as being highly distressed declined by a larger percentage since 2010 than less-distressed tracts. This decline was particularly pronounced for lower- and middle-income borrowers.

These industry-wide findings help guide the analyses that individual lenders should conduct on their own HMDA data.  Here are three basic statistical reviews that we recommend each lender undertake to better understand what the data shows:

1. Evaluate the share of applications received from protected class applicants.
2. Compare denial rates for minority applicants and non-Hispanic white applicants.
3. Determine the proportion of higher priced loans originated to minority applicants and non-Hispanic white applicants.

We reiterate that HMDA data alone does not prove or disprove mortgage lending discrimination.  A HMDA review that suggests fair lending concerns should be followed by further statistical analysis that considers credit-related factors that are not part of HMDA, such as credit score and loan-to-value ratios.  Review of individual loan files may also be necessary before making a final determination that there are lending patterns that cannot be explained, and that ameliorative action is necessary.

Nevertheless, it is important to understand your institution’s raw, public HMDA data and be prepared to defend and follow-up on any lending disparities it may suggest.  Put simply, the best way to avoid getting blind-sided by a regulator, community group, or media representative is to know your HMDA data better than they do.

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Warren W. Traiger, counsel at BuckleySandler LLP, advises clients on consumer regulatory matters, particularly fair lending and Community Reinvestment Act (“CRA”) compliance. Mr. Traiger is a nationally recognized expert in CRA and fair lending issues. His research and analysis of mortgage lending data has been cited in publications of the Federal Reserve Banks of San Francisco, Boston, and Dallas, the Federal Deposit Insurance Corporation, and in testimony before the U.S. House Financial Services Committee.

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CFPB and FHFA Partner to Develop National Mortgage Database

The CFPB and the FHFA announced today an agreement to create a National Mortgage Database, the first comprehensive repository of mortgage loan information. The database primarily will be used to support the agencies’ policymaking and research efforts and help regulators better understand emerging mortgage and housing market trends. The database is intended to (i) monitor the health of mortgage markets and consumers, (ii) provide insight on consumer decision making, (iii) monitor new and emerging mortgage products, (iv) consolidate data on first and second lien mortgages for a given borrower, and (v) help policymakers understand consumer debt burden. The press release states that development of the dataset is currently underway and the agencies expect early versions of the full dataset to be complete in 2013. Once completed, the agencies plan to explore opportunities to share database information with other federal agencies, academics, and the public. The database will include information spanning the life of a mortgage loan from origination through servicing and include loan-level data about (i) the borrower’s financial and credit profile, (ii) the mortgage product and terms, (iii) the property purchased or refinanced, and (iv) the ongoing payment history of the loan. The agencies will build the database by matching a nationwide sampling of credit bureau files on borrowers’ mortgages and payment histories with informational files such as the HMDA database and property valuation models. The database will include historical data back to 1998 and will be updated on a monthly basis.

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FFIEC Releases 2011 HMDA Data

On September 18, the Federal Financial Institutions Examination Council (FFIEC) released data collected in 2011 under the Home Mortgage Disclosure Act (HMDA). The data include information on loan amount, loan type and purpose, property type and location, pricing, and applicant characteristics. The FFIEC release notes that the 2011 data reflect that (i) the FHA’s share of first-lien loans declined in 2011, but there remains a heavy reliance on the FHA program, (ii) only a small minority of first lien loans had APRs above the loan price reporting thresholds, and (iii) for conventional home-purchase loans, black and Hispanic white applicants experienced higher denial rates than non-Hispanic white applicants, similar to in prior years. While examiners consider HMDA data when assessing lender compliance with fair lending laws, the FFIEC cautions that such data do not include many potential determinants of creditworthiness and loan pricing, such as the borrower's credit history, debt-to-income ratio, and the loan-to-value ratio.

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