How Controlling for Local Competitive Environments May Reduce Fair Lending Pricing Risk

How Controlling for Local Competitive Environments May Reduce Fair Lending Pricing Risk

Lenders operating across multiple metro areas, in ADI’s experience, provide their branches with national rate sheets through which they price first-lien mortgages. Despite the single rate sheet, the lender’s many branch managers confront different competitive environments and are often tasked with refining rate sheet-based pricing to meet local conditions and maximize loan originations.

Branch Manager Pricing Decisions Can Create Fair Lending Risk

These branch manager decisions – often known as branch adjustments – represent a form of discretionary pricing. They pose a potential Fair Lending risk to lenders because they are difficult to incorporate into a Fair Lending regression analysis. So, when Fair Lending pricing models are built, inter-branch differences cannot be explained by either loan-level factors (e.g., LTV, credit score, occupancy status, etc.) or macroeconomic factors (i.e., some loans were priced when market rates were high, others when rates were low within a given year).

As a part of our regression-based Fair Lending pricing modeling solutions for several clients, we have developed a technique that helps explain pricing differences between Branches A and B – located in different metro areas –  by using the Herfindahl-Hirschman Index (HHI) – a measure of relative market concentration. This solution helps to:

  1. increase the proportion of pricing differences that can be explained in regression modeling; and therefore
  2. reduce the potential for Fair Lending pricing risk to be identified through pricing models.

Our solution involves incorporating a measure of market concentration[1] in each metro area, allowing us to characterize the competition a lender is facing at individual branches within our modeling. The index relies on data made public under the Home Mortgage Disclosure Act (HMDA).

Let’s consider a simple example to help illustrate:

  • Suppose Lender XYZ operates in two metro areas in Texas – Houston (Branch A) and Killeen (Branch B).
  • In Houston, the largest lender (Wells Fargo) originated 3 percent of 2017 first-lien purchase loans. In Killeen, on the other hand, the largest lender (Fairway) originated over one-quarter of 2017 first-lien purchase loans.
  • For lenders (other than Wells Fargo and Fairway) operating in both markets, we would expect branch managers to offer greater discretionary discounts in Killeen than in Houston. Conceptually, the Killeen branch manager would expect a substantial number of customers would check with the dominant local lender who, given their size, may offer relatively low pricing. On the other hand, the Houston branch manager would be under less competitive pressure. While its customers may shop for better pricing offers, it is less likely that these competitive offers would be priced as low as those offered by Killeen’s dominant lender.

Controlling for Competition Has Reduced Pricing Risk for Clients

Empirical evidence we have developed across multiple lenders has found evidence of the relationship between market concentration and borrower pricing or APR outcomes. Specifically – in Fair Lending pricing models we have incorporated HHI measures of market concentration for several clients – and found a negative association. That is, regression-based evidence has demonstrated that borrower pricing for small lenders in places like Killeen (where there were 200 originators in 2017) and Houston (where there were 400) differed. Pricing for Lender XYZ in relatively more competitive environments (e.g., Houston) has proven significantly lower than in less competitive metro areas like Killeen.

Incorporating HMDA-based measures of metro area market concentration has, in turn, resulted in significantly less Fair Lending pricing concerns for our clients, resulting in lower compliance costs and a lower need to complete Comparative File Reviews or other types of post-regression file reviews.

We would be pleased to discuss how we can build, validate or enhance Fair Lending regression-based pricing models for your company using these and other techniques, contributing to the efficacy of your company’s compliance risk management system.

[1] The HHI is used by the US Department of Justice and the Federal Trade Commission, for instance, to measure the level of competition in particular markets as it evaluates proposed corporate mergers.  LendingTree.com recently published an article “LendingTree Reveals Where Homebuyers See the Most Mortgage Lender Competition” that discusses the role of the HHI in measuring contemporary mortgage market concentration across metro areas.

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About the Author

Paul Strasberg

Paul is ADI’s Senior Economist and lead consultant in ADI’s Fair Lending analyses to accurately measure potential Fair Lending risk. You can contact Paul at pstrasberg@adiconsulting.com or 703.740.4907.

July 11th, 2018|