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7 Fair Lending HMDA Questions to Kickstart Your Compliance

5 min read
Sep 8, 2015

[Updated Sept. 29, 2017]

With the release of public 2014 HMDA data quickly approaching, it's a perfect time to talk about how you can identify key fair lending risks with HMDA data analysis. Ask these seven questions to help determine your risk exposure, and kickstart your compliance.

Every fall, usually in mid-September, the regulators release public HMDA (Home Mortgage Disclosure Act) data from the year prior. This data represents a starting line, and its release is like the shot signaling the start of the race, kicking off a rush of activity and analysis for fair lending.

The regulators and community groups will review this newly public data for disparities that may indicate discrimination, while financial institutions will try to identify and mitigate Fair Lending risks before issues arise. (Even if you don't file HMDA, you must comply with Fair Lending and data analysis is an important element of compliance risk management.)

When reviewing your loan data for fair lending risk, there are 7 key questions you need to ask:  

1. Are We Conducting Statistical Analysis of Our Data?

This is a general program risk, but if you're a bank, mortgage company, credit union or auto lender and you're not yet analyzing your Fair Lending data for disparities that may indicate discrimination, then it's time to start. Even institutions that don't file HMDA should conduct Fair Lending data analysis.

We all know that Fair Lending requires that similarly situated borrowers be treated equally, regardless of race, gender, ethnicity, age, marital status, religious denomination, or any other prohibited basis characteristic. Statistical analysis of your data will help you determine if you are treating similarly situated individuals equally.

Without analysis, your institution is operating with a large, high-risk blind spot. Roughly half of the FFIEC guidelines for Fair Lending reference comparative statistical analysis of your loan data. 

There are lots of methods for conducting this kind of analysis. If you're interested in learning more about the benefits of using a software, click here.

2. Are We Marketing Our Services Equally to All Members of the Communities We Serve?

This is commonly referred to as Marketing Risk. When assessing your marketing risk, you're trying to determine if you're reaching all members of your community equally.

To do so, compare the prohibited basis applicants to the demographic make-up of communities in your market area. If 20 percent of your market is Hispanic, and only 2 percent of your applications are coming from Hispanic applicants, you may have fair lending marketing risk. It is sometimes helpful to also compare your application rates by race/ethnicity and gender to national averages, or benchmarks.  A good Fair Lending analysis software or tool can make this much easier.  

3. Are We Approving or Denying Minority Applicants at the Same Rates as Similarly Situated Control Group Applicants? Are We Providing Everyone the Same Quality of Service During the Process?

Underwriting Risk can come in many forms, but calculating and understanding the drivers of any origination disparity, denial disparity, fall-out disparity, and time (to process) disparity is the place to start.  It is difficult to determine if disparities exist without analyzing your data statistically. Comparing the approval and denial rates of control group and minority or prohibited basis group applicants, and reviewing those by product, market and channel will provide the answers you need.  

Some institutions omit a review of what the regulators call "Quality of Service" measures. Make sure you can explain Quality of Service measures like processing times of originated loans, withdrawn and incomplete applications. Review exception frequency and corresponding reason codes, as well as any disparities between prohibited basis and control group applicants.  All of the information needed to determine your underwriting risk is available in the HMDA data. For non-HMDA reporters, this analysis can be done, but gathering all the right data may be a little more difficult.

4. Are We Pricing Our Products Equally to All Borrower Types?

Pricing Risk tends to draw a lot of attention and historically has been the area of risk that most fair lending settlements are based upon.  In our experience, rarely have we seen an institution knowingly provide different pricing for borrowers with similar credit characteristics. However, we consistently see statistical differences in pricing between control group and prohibited basis group or minority applicants. 

Pricing differences and rate-spread frequency exist, and will draw the attention of the regulators.

That said, often these disparities and rate spreads are easily explained. Those institutions that analyze their data statistically and are able to quickly investigate the loans that are driving disparity can dramatically reduce this Fair Lending risk.

[Free Resource: Fair Lending - HMDA Analytics Sample Reports!]

5. Are We Directing Certain Types of Applicants into Specific Products?

Determining if Steering Risk exists is a challenge for most institutions. We help over 500 financial institutions conduct statistical analysis of their loan data for Fair Lending using our Nfairlending software system, and we often see what could be characterized as steering. Different borrower types typically do purchase different loan products, but that does not mean steering is present.

Review product distribution by market, and compare control group and minority applicants, Don't be alarmed if you see measurable disparities - most institutions do.  Drilling down into the loan data by product and comparing the credit characteristics of borrowers often explains the disparity and diminishes this fair lending risk.

6. Are We Avoiding Certain Low-Income or High-Minority Areas, Either Through Policy or Practice, Within Our Market?

This is referred to as Redlining risk and it occurs when an institution appears to limit or avoid lending to high-minority areas. Addressing this key area of regulator focus requires reviewing your lending activity geographically and comparing it to the underlying Census demographics.  

Reviewing loan distribution by markets served is the only way to fully understand redlining risk. By geocoding loan data, and then comparing control group borrowers to borrowers in LMI (low- to moderate- income) and high-minority census tracts, you can quickly determine if your institution has elevated redlining risk.  

7. Do We Have the Expertise to Accurately Understand the Results of Our Analysis, and Manage the Risk Effectively?

Like the first question, this one isn't related to a specific Fair Lending risk, but is a more general indicator of the health of your compliance program. We've worked with hundreds of compliance officers, senior management teams and risk managers, and know that managing compliance is an enormous job.

It can be challenging for a compliance professional who is charged with so much responsibilty to also act as a statistician, demographer and data analyst when it comes to Fair Lending loan analysis. 

Conducting the data analysis is one thing - interpreting it accurately is another. Drawing conclusions based on loan analysis and comparing it to relevant benchmarks require skills that and experience that only come with time and exposure.  It is our experience, that those institutions that have a credible resource that can explain the results of their statistical analysis and help them prioritize the potential risks are best prepared to manage Fair Lending effectively. They are able to understand the focal points quickly and focus 90 percent of their time addressing them.  

Ncontracts Viewpoint: There is power in the numbers, but the real power is in understanding what they mean. At Ncontracts, our team of compliance professionals, programmers and data analysts see thousands of data sets a year. Your team may only review one or two. Whether you choose to partner with Ncontracts or another provider, make sure they offer robust data analysis, clear interpretation of the findings and actionable suggestions for resolving any issues found.  

Nfairlending is the next generation of our Fair Lending analytics software. It can conduct in-depth loan analysis in seconds and provides easy to read dashboards that your board can understand.

We understand that great technology is important, but what a compliance department needs is clear guidance, interpretive support and clear action steps for eliminating risk. That's exactly what Ncontracts provides, as every analysis is accompanied by a guided review by our team of Fair Lending Data Analysts.  

Are you ready to learn more and get pricing for Fair Lending, HMDA, CRA and/or Redlining solutions? Then just click below!



Related: How to Build a Strong Fair Lending & Redlining Compliance Management System


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