Nearly four years ago, the Consumer Financial Protection Bureau (CFPB) issued a rule requiring lenders to gather a greatly expanded range of data under the decades-old Home Mortgage Disclosure Act (HMDA). That was followed by another “clean-up” rule issued in 2017 that updated and clarified the prior rule.
Under the final CFPB rule, lenders were required to begin collecting the additional HMDA data in 2018 but were not required to begin reporting that data to the CFPB until this year. Although it may seem that the HMDA regulatory reform is a significant enough regulatory burden all on its own, let’s remember that the HMDA data is the fuel used for fair-lending and Community Reinvestment Act (CRA) analytics.
Clearly the regulators, community groups, and the HMDA-savvy public will have a greater level of detail relating to mortgage-lending applications and originations than ever before. History shows that changes to HMDA-reportable data are soon followed by increased regulatory and civil activity against lenders for possible fair-lending and CRA violations. For that reason alone, mortgage originators should be aware of the new HMDA-reporting requirements because it may affect lenders’ financing decisions and, consequently, the originators working with those institutions.
To some in the industry, fair-lending analytics might seem like the work of an illusionist. It’s especially intimidating if the mystery is left to the regulators to solve. Those familiar with working with data, numbers and statistics usually know all of the tricks behind the smoke and mirrors. Even the most seasoned statistician, however, will need to re-evaluate the fair-lending implications of all of the new HMDA data fields.
Set the table
Initially, the most important precursor to performing a fair-lending analysis is to be sure that the data is accurate. No amount of magic can turn bad data good.
Therefore, it is critical to ensure that all HMDA data is timely and accurately collected. Additionally, it would be a mistake to think the new analysis will be the same as the previous HMDA data analysis. There are a lot of nuances around the new HMDA reporting, even though the data is familiar.
So, what is the best way to approach the HMDA changes? Perhaps the biggest influence on the overall analysis is the influx of newly reportable records into the HMDA-reportable definition. For consumer transactions, reportable transactions are determined based on the securitization of a “dwelling,” and are no longer purpose-based.
This new standard has the potential to increase the HMDA-reportable population for many institutions. These changes are important because fair-lending analytics are based on numbers and ratios. Even small changes to this overall population of data can unmask potential problems.
These types of changes also will likely impact the overall risk profile, as well as how an institution will perform a trend analysis, as year-over-year will no longer involve an apples-to-apples type of comparison. Also, core business, compliance and marketing strategies that have been put into place based on data collected prior to the new HMDA rule being implemented may need to be revisited and updated based on the data profile that will be created from this new data. Following are some of the biggest changes that should be considered when re-examining your HMDA-analytics toolkit.
HELOC mandate
Previously optional to HMDA reporting, the now-required reporting of dwelling-secured, open-ended lines of credit (commonly known as a home equity line of credit, or HELOC) may be the single most impactful change of all of the new reporting requirements. This is for two reasons: the portfolio nature of the product, and the overall volume of reporting for these transactions.
For products such as HELOCs that are likely to be retained in portfolio, credit exceptions are believed to be more likely to occur, as an institution would not have to answer to an investor on these determinations. Additionally, underwriting and pricing criteria may not have been developed in as rigorous a process as traditional first-mortgage loans.
This added level of discretion without an appropriate level of controls could result in a higher degree of risk for potential discrimination on a prohibited basis now that this data is readily available for examination. Therefore, lenders should examine the credit and pricing policies and controls to ensure that the HELOC criteria are empirically derived. This would be in addition to performing fair-lending analysis, monitoring and testing to ensure that the policies are equally applied to all applicants.
Additionally, the sheer number of HELOCs could potentially shift ratios and statistics for an institution in dramatic fashion. The introduction of a large population of HELOCs within the HMDA data, for example, will undoubtedly impact redlining analysis, which has been a hot topic for regulatory scrutiny for the past several years. It may take a lot of time for institutions to adjust to these new volume measures, so it’s critical to start doing analytics as soon as possible to determine the impact of this new data on the lending institution.
New York state of mind
Specific to the state of New York only, transactions involving a loan consolidation, extension and modification agreement (CEMA) are now reportable under the new HMDA rules. For New York lenders, this could be a significant portion of the loan-application population.
Institutions should begin to perform analysis on this new HMDA-reportable population. They will need to determine if the addition of CEMA reporting results in any disparities.
On another front and affecting all lenders, unsecured home-improvement loans deemed consumer transactions (nonbusiness-purpose transactions) will no longer be reportable under HMDA. If previous analysis is included on this population of borrowers, that analysis should be updated in order to understand the impact and changes year over year.
In addition, previously optional to report, pre-approval requests are now required reporting under the new HMDA rules. The addition of pre-approval data will add more records to the reporting for those institutions that participate in a pre-approval program.
Expanded data fields
Certainly, the expanded data fields are the area of significant focus, as the data can now be evaluated in many ways, singularly and combined, and can thus yield much more information about each application. It is critical to be proactive, and consider the fields that have the highest potential impact. Following are of some of the fields that should be considered in that analysis.
Application channels. The application-channel reporting added into the mix of the HMDA data introduces an interesting wrinkle in the fair-lending analytics. If a lender participates in both direct and indirect lending, separate analysis by channel may yield disparities that were previously hidden within the analysis of the aggregate data.
Because only a small number of loans could mask problems, lenders who engage in both direct and in- direct lending should begin to understand how their data may be viewed separately — before the regulators have access to the data. If lenders are currently performing regression or other robust analysis, these institutions should also begin to bifurcate the approach in order to determine if a single channel may surface hidden issues.
Fees and interest rates. The expanded HMDA reporting includes several pricing fields, including the fees associated with a loan (origination charges, discount points and total loan costs); lender credits; and the interest rate. Because of the impact of these fields on the APR [annual percentage rate] calculation and pricing decisions, data-integrity controls and robust analysis needs to be put in place to ensure that these criteria are assessed individually, as well as in the aggregate APR-calculated field.
Lenders should know which of their borrowers are charged discount points, for example, and the degree to which these points benefit the borrower. There also might be some trends or customs by regions that may need to be accounted for to ensure that a lender can accurately tell their story when interpreting the data and discussing any apparent differences with the regulator.
Prepayment penalties and non-amortizing features. These loan features have had a negative perception associated with them over the past few years. Institutions need to understand the distribution of these elements to ensure that there is no disproportionate distribution of these components across prohibited-basis factors.
Automated underwriting-system results. The automated-underwriting results will provide the regulators a clearer view into exceptions to underwriting policies. In order to limit scrutiny around exceptions, lenders may want to consider tighter controls around exceptions and general underwriting procedures before the data is reported.
Other variables
HMDA data fields, such as the combined loan-to-value (CLTV) ratio, credit-score and debt-to-income (DTI) ratio, are important variables that may be used in credit decisions and loan pricing — and thereby are typically involved in focal-point reviews and regression analysis.
If the denial rate for a group of borrowers fails a benchmark test, or is tested to be statistically significant, that could become a focal point during the next regulator exam, and may implicate redlining or perhaps reverse-redlining concerns. Therefore, it is critical for these fields to be accurate.
Demographic information may be the most challenging and complicated area for data collection, data integrity and data analysis — and could impact marketing, redlining, pricing, underwriting and servicing fair-lending analytics. Lenders need to be confident that they understand the many nuances and complexities of the rules relating to the aggregate, disaggregate and free-form text categories.
Lenders should monitor the volume of applications received from applicants that designate their race or ethnicity within these new subcategories in order to begin the analysis on these populations as soon as possible. Similar to many of these other newly reported fields, lenders should know whether there are issues within the more granular data that may be masked in the aggregate-data reporting.
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Pulling a rabbit out of a hat may seem like a much easier feat to accomplish than analyzing the new HMDA data. For all lenders, however, it is time to start analyzing this new data to better understand how this information will be viewed — and originators also should be clued into these processes and results. The time to act is now to devise a strategy to ensure that fair-lending analytics will be something to celebrate.
