Using HMDA Data to Identify Fair Lending Risk

4/12/2019 - By Jennifer Paradise, CRCM

Now that the Home Mortgage Disclosure Act (“HMDA”) reporting deadline has passed, we hope you’ve had the chance to take a deep breath and pat yourself on the back for surviving this whirlwind year of regulatory adjustments to the HMDA regulations, in part due to S. 2155. After you’ve had a chance to catch your breath, you may want to consider using the data, so diligently collected and scrubbed, to evaluate the potential for fair lending issues.

Whether or not your institution was eligible to claim the exemption from reporting certain data fields, if the institution collected all or some of the exempted twenty-six data points you will obviously have more data to work with.

We know that many smaller HMDA reporters don’t have the resources some of the largest reporters do. In the absence of fancy software, there is still much that can be done, beginning with the following steps:

Ensure you are comparing apples to apples. For example, don’t compare the pricing on a 1-4 family primary dwelling purchase with the refinancing of a non-owner-occupied investment property. Appropriately filtering your data accordingly is critical.
To evaluate the potential for disparate treatment in pricing, you can sort the data by ethnicity, race, or sex and determine average pricing using the interest rate field. After this, I recommend that you begin filtering the data by loan type, property type, occupancy, lien status, and loan purpose.
To evaluate the potential for disparate treatment in underwriting, sort the ethnicity, race or sex and filter the data by action taken. Next, separate out the originated versus declined loans. Using the credit score, LTV ratio and DTI ratio, you can sort the declined loans by the highest credit scores, DTI, LTV and sort the originated loans by the lowest credit scores, DTI, LTV; then compare the results and see if any denials to minority applicants had the same (or better) credit scores, DTI, LTV than the lowest white applicant score.

If the pricing analyses indicates potential disparities the next logical action should be full comparative file reviews to rule out discriminatory practices. Look for mitigating factors to support why a white applicant (for example) was charged less than a minority applicant. Perhaps an interest rate discount was approved (review the loan approval to understand the justification), or perhaps the pricing is dependent upon factors such as deposit balances and the minority applicant did not have an existing relationship. Several factors may affect loan pricing, so the key is to review whether there are sufficient and well-documented reasons for variations to the institution’s pricing guidelines.

If the underwriting analyses reveals instances where a white applicant (for example) was approved for a loan request that a similarly situated minority applicant was denied, again, perform comparative file reviews to rule out discriminatory practices. Look for whether policy exceptions were granted and if so, are they well documented and substantiated? Are there other compensating factors that are used in underwriting, such as deposit balances example used above?

You may sort pricing and underwriting discrepancies by the loan officer to evaluate whether there is disparate treatment among certain loan officers that requires a deeper dig into the data.

There is more that can be done using collected HMDA data to identity (or disprove) fair lending violations, but the above analysis will provide a good start. Examiners expect institutions to perform some level of analysis of HMDA data so that it has a clear understanding of lending patterns and practices within your organization.

This article is current as of April 12, 2019 and may not be updated for regulatory changes occurring after this date.

About the Author | Jennifer Paradise, CRCM
Jennifer is a consultant in the Financial Institutions Advisory Group at Saltmarsh, Cleaveland & Gund and has been serving the financial institutions industry since 1990. She is primarily involved in performing fair lending, loan internal audit, loan compliance, and other consulting services for the firm’s financial institution clients throughout the region.