January 17, 2018 | Posted by Trey Sullivan
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9 Minute Read

HMDA Plus, or 2017 Final HMDA Rule, changes will have a distinct impact on Fair Lending exams in the future. In this post, we will discuss how updates to the Home Mortgage Disclosure Act are likely to change Fair Lending exams in 2018 and beyond, and how you can prepare.

We're mid-way through the first month of the year, and the intensity of focus on HMDA has not waned. If anything, compliance professionals nationwide are even more engaged with the 2017 Final HMDA Rule now that they are actively collecting the new data and preparing to submit in March.

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In fact, we're continuing to get questions about HMDA every day. (To help answer them, we're hosting a free HMDA webinar, "Answers to 5 FAQs about the New HMDA Rule." We'll answer some of the basic HMDA questions in just 30 minutes.)

As we move further into 2018, the potential implications of these regulatory updates crystallize and become more tangible. In particular, the TRUPOINT team is thinking about how the HMDA rule will change Fair Lending examinations.

In this blog, we'll share a few qualitative and quantitative tips for how to prepare for Fair Lending compliance examinations in a post-HMDA Plus world. 

In particular, we believe that revised requirements will highlight additional risk factors, and will need for more in-depth Fair Lending analysis. Here's how:

Additional HMDA Data Fields Mean More Data-Driven Insights for Lenders - and Regulators

The new HMDA Final Rule expands the data fields that financial institutions are required to collect. There are 48 new or expanded HMDA data fields, and a total of 110 data points required to be collected. In addition, the regulators also released the so-called "key data fields," i.e. the ones that they think will be most important for compliance.

Some of those key data fields include:

  • Ethnicity of Applicant or Borrower, and Co-Applicant or Co-Borrower
  • Race of Applicant or Borrower, and Co-Applicant or Co-Borrower
  • Sex of Applicant or Borrower, and Co-Applicant or Co-Borrower
  • Age of Applicant or Borrower, and Co-Applicant or Co-Borrower
  • Credit Score of Applicant or Borrower, and Co-Applicant or Co-Borrower
  • Debt-to-Income Ratio
  • Combined Loan-to-Value Ratio
  • Origination Charges
  • Discount Points
  • Lender Credits

The inclusion of those additional data fields means that financial institutions, consumers,competitors, and regulators will have access to more information about your lending activity.

If you have disparities in your data, it may become easier to explain them - or there may be fewer places to hide. In particular, we are expecting there to be more focus on rate spread, fees, and race-, ethnicity-, sex-, and age-based discrimination.

In addition, HMDA data analysis will now need to include these additional data fields, making it even harder to conduct in-house. Having a HMDA analysis software solution will become even more important.

If you'd like to learn more about the HMDA data fields, here are a few resources that outline these important HMDA data fields:

Beware Broader Compliance Analysis from the Regulators

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So with these new areas of emphasis, how do you adequately prepare for your Fair Lending exam? The regulatory agencies will be employing specialized techniques, including statistical analysis, regression analysis to evaluate Fair Lending risk.

With the availability of expanded data sets, examiners can now conduct statistical analysis of your Fair Lending and HMDA data before they arrive onsite. 

They will probably be able to build a clearer picture of what they think is happening in your institution based on a quantitative understanding of your performance. In addition, they may already have a narrative of what they think is happening, and it will be up to your compliance team to help them understand the qualitative nuances they can't see in the data.

We envision that when you have a Fair Lending exam, it will be even more important that you have an in-depth understanding of the story your data tells, and be prepared to quickly and comprehensively answer the regulators' questions. If you have disparities in your data, you'll need to understand and be able to explain them. 

Despite Changes, the Key Aspects of Fair Lending Compliance Remain the Same

The HMDA changes will dramatically impact the data that is analyzed for Fair Lending and HMDA compliance, but the bones of your Fair Lending analysis will remain the same.

We will still be looking at evidence of potential discrimination at every stage of the lending process:

  • compliance-analysis-team-trupoint.jpegRedlining
  • Marketing
  • Steering
  • Denials
  • Pricing
  • Originations
  • Servicing

As you move through Fair Lending analysis in the future, there is some comfort in knowing that you'll be analyzing the same basic categories of information, even if there are more data-points used.

However, the Final HMDA Rule will change certain important aspects of Fair Lending analysis and risk exposure. For example:

  • Redlining Risk: Over the past two years, Redlining has been a major area of regulatory scrutiny. We don't expect that the additional data fields will dampen regulators' desire or ability to find potential evidence of Redlining or Reverse Redlining.
    • In particular, you'll be able to more easily identify disparity in the number of originations of higher-priced loans, or loans with potentially negative consequences, in areas with relatively high concentrations of minority residents.
  • Underwriting and Pricing RiskExaminers will be looking for financial incentives, such as overages, underages and yield spread premiums, where pricing discretion was used. Since financial institutions will also have to report rates and calculate rate spread (the FFIEC has released a rate spread calculator here), it will be easier for the regulators to identify disparities and potential discrimination in pricing.
    • Much of your pricing and rate data will be available in the public HMDA LAR, for use by community groups, consumers, and even your competitors. This additional transparency will likely have both positive and negative consequences; it remains to be seen how it will impact the competitive landscape.
  • Data Integrity: Having clean data will be more important - and more difficult. Your data will have to pass some basic data quality checks before being submitted in March. In addition, having good data quality will help set the stage for a good exam, and it will be essential that your data is reliable in order to conduct accurate Fair Lending and HMDA analysis.

Reduce Risk with Proactive Steps

Know the story your data tells. Analyze your Fair Lending and HMDA data, and understand any disparities that arise - inside and out. Disparities do not always mean discrimination, but it will be your responsibility to explain that to the regulators. If you do find evidence of potentially disproportionate treatment or impact, be positive and proactive about addressing it.

As soon as your 2017 data has been submitted, we recommend analyzing it internally. With added oversight of lending (including foreclosures, modifications and workouts), it is more important than ever that you understand the differences between prohibited and protected class individuals.

[Read More: A Step-By-Step Guide to Implementing the New HMDA Rule]

TRUPOINT Viewpoint: What we know is regulators will have new methodologies they use to scrutinize your Fair Lending and HMDA data. You should look at your data for evidence of disparate treatment, disparate impact and discrimination. The agencies will likely be more empowered, and more certain of the conclusions they're drawing about your lending activity. Within this environment, lenders and servicers must continue to mitigate risk for their institutions, meet consumer needs fairly and be prepared for the enhanced Fair Lending exams.

With broadened government oversight and increasingly complex regulations, good financial institutions will take extra precautions. The best will become even more proactive in their Fair Lending compliance and regulatory risk management. 

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Trey Sullivan

Trey Sullivan