As you approach analyzing your HMDA data for Fair Lending compliance, there are a couple of key concepts you need to familiarize yourself with. Here are the four things you should consider before you start your HMDA analysis.
We shared the podium with Jeff Thompson from Great Lakes Banc Consulting at the MBA BEST (Bankers Education Summit and Trade Show) conference this week in Traverse City, Michigan. Jeff did a great job of explaining how to proactively manage a successful HMDA Quality Control Program. Having accurate HMDA data is the first step in your lending compliance management program. Then, we focused on the next step: how to analyze the HMDA LAR after it's been scrubbed and even submitted.
Now that your HMDA data is confirmed to be accurate and has been submitted, what should you do? Well, you need to analyze your data and explore where you may have disparities to ensure that you don't have any discrimination risk exposure. Here are four basic questions we reviewed with the bankers from Michigan, and that we'd like to share with you:
1. Do You Understand the Basics of Comparative Analysis for Your HMDA Data?
Are there disparities between Control Group vs. the Prohibited Basis Groups?
Control Group: In Fair Lending analysis, the Control group is the group that is least likely to be discriminated against. For many banks, this is a group consisting of white, non-Hispanic males. However, your financial institution’s control group will be dependent on your market area demographics, the bank ownership and the demographic of the customers that are commonly served.
Prohibited Basis Group: The prohibited basis group is any group defined by a prohibited basis characteristic, most commonly race, gender, and/or ethnicity.
For Fair Lending compliance, regulators will analyze your loan data and compare your control group to your prohibited basis group data.
For example, one data point they may review is application rate. If your application rate among control group individuals is much different than your application rate among prohibited basis group individuals, you have an application rate disparity. This disparity may indicate that discrimination is occuring, even unintentionally. After identifying this disparity, you should dig into potential causes and mitigating factors to determine your risk.
The right compliance partner will help you define your control and prohibited basis groups, and also provide clarity into what the results of your analysis might mean and how to mitigate your risk.
- Success Tip: Analyze each prohibited basis group independently for each of the Fair Lending risks (marketing, underwriting, pricing, steering, redlining and servicing).
2. What Levels of HMDA Data Should you Review?
For community banks operating in a single market with a single channel, you should be able to analyze the data on an aggregated basis. For larger banks with multiple channels (e.g., Call Center vs. Brick and Mortar) and multiple geographies (e.g., numerous MSAs), you will be well-served to consider the different slices of your HMDA data.
- Success Tip: As a general rule, your approach towards the level of analysis should start by recognizing the size and complexity of your business model. The more complex and diverse your business operations, the more likely you should look at those operations as unique data sets while also considering the big picture (in aggregate).
3. What Are the Right Benchmarks?
You should be prepared to analyze the disparities between prohibited basis groups and control groups within your institution. You should also be prepared to analyze your in comparison to outside benchmarks.
For example, if you have one branch bank on the fringe of a large MSA, should you compare your HMDA data to the entire MSA or can you create a better comparison by narrowing the comparison to a single county?
- Success Tip: The more precisely the benchmark reflects your organization, the better the comparative insights. A good partner will help you define institution- and market-specific benchmarks, so that you can more accurately understand the story your data tells!
4. What Is the Right Type of Analysis?
Should you conduct simple “simple comparison of means” or conduct more complex analysis like multivariate regression analysis? The answer really depends on the depth of your HMDA data.
- Success Tip: Smaller data sets typically will not afford you the opportunity to conduct the more complex data analysis. Larger data sets may require multivariate modeling (where variables like credit score, LTV and DTI are included).
Ncontracts Viewpoint: The process of collecting, reviewing (QC) and submitting your HMDA data is only half the battle. Analyzing your HMDA data is a prudent next step. Successful management of HMDA data includes both a consistent QC process and a thoughtful plan to analyze the fair lending risks.
Best practice organizations have processes in place that allow their institution to collect, scrub, submit AND analyze the data on a regular basis. Before analyzing the data, think about your strategic approach to the analysis by considering the four issues above.
Thanks again to Jeff Thompson, Stephanie Fisher and the Michigan Bankers Association for the opportunity to share and discuss various compliance concerns (including HMDA QC and Analysis) this week in the great state of Michigan.
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