Regulatory

Fair lending is all about the data

It's time to use analysis as a basis for liability

Laws and regulations considered to be “fair lending laws” include the Fair Housing Act (FHA) and the Equal Credit Opportunity Act (ECOA). The Fair Housing Act generally prohibits discrimination in all aspects of residential real estate-related transactions and the ECOA generally prohibits discrimination in any aspect of a credit transaction, namely actions attendant to an extension of credit, including extension of credit to small businesses, corporations, partnerships and trusts.

Both the FHA and ECOA prohibit discrimination on the basis of race, color, religion, national origin, sex, and marital status. The ECOA additionally prohibits discrimination on the basis of age and receipt of income derived from a public assistance program. The FHA additionally prohibits discrimination on the basis of handicap.

The Department of Housing and Urban Development’s Discriminatory Effects Rule became effective on March 13, formalizing disparate impact theory as a tool to establish liability under the FHA. It was widely anticipated that the Consumer Financial Protection Bureau would use the rule as guidance in enforcing the ECOA and, in July 2013, those suspicions proved prescient when the CFPB released its ECOA Examination Procedures and “Baseline Review Modules.” 

This article discusses how statistics are both the lender’s friend and adversary when considering the July 2013 CFPB fair lending examination procedures. It also discusses using your internal systems to cull data and promote compliance under both the FHA and ECOA.

The aforementioned rule provides as follows: “[l]iability may be established under the Fair Housing Act based on a practice’s discriminatory effect … even if the practice was not motivated by a discriminatory intent. The practice may still be lawful if supported by a legally sufficient justification.” 

“A practice has discriminatory effect where it actually or predictably results in a disparate impact on a group of persons or creates, increases, reinforces, or perpetuates segregated housing patterns because of race, color, religion, sex, handicap, familial status, or national origin.”

“A legally sufficient justification exists where the challenged practice: (i) is necessary to achieve one or more substantial, legitimate, nondiscriminatory interests of the respondent [lender] … and (ii) Those interests could not be served by another practice that has a less discriminatory effect.”

There are six Baseline Review Modules set forth in CFPB examination procedures, which largely track the theme of the rule. As a service to HousingWire readers, they are as follows:

Module I: Fair Lending Supervisory History, which seeks any history of fair lending violations, areas identified as past or future fair lending risks. Litigation initiated against the lender, enforcement action by regulators, complaints alleging discrimination from any source, and self-identified risks are all subject to discussion and examination in this module.

Module II: Fair Lending Compliance Management System, which tests management participation, policies and procedures, training and internal controls and monitoring.

Module III: Risks Related to Mortgage Lending Policies and Procedures, which looks at policies and procedures for mortgage underwriting, whether those systems are automated and in what circumstances those systems can be overridden.

Module IV: Risks Related to Mortgage Servicing, which looks at policies and procedures for mortgage servicing as they relate to fair lending. In particular, this module looks at loss-mitigation policies that could have disproportionately negative, unjustified impact on a credit decision on a prohibited basis.

Module V: Risks Related to Auto Lending, which looks at policies and procedures for pricing, underwriting, referrals, loan originator, dealer and other third-party compensation in both direct and indirect auto lending.

Module VI: Risks Related to Other Products, which looks at policies and procedures for pricing, underwriting, referrals, compensation, marketing and other lending operations as they relate to fair lending risks in secured or unsecured consumer lending, credit cards, add-on products, private student lending, payday lending or small business lending.

A lender’s liability (or not) under fair lending laws comes down to data analysis and what I describe as the Three L’s.

The first of these are the Loan Data Points, which include the core attributes of the loan. This is basically the loan amount, interest rate and fees charged, loan type and property address. 

The second L is called Lender Data Points. This involves matching closed loans with purchased loans (and type of purchaser), loan officer and/or broker compensation amounts as a percentage of charged origination fees, individual branch loan volume as compared to the whole of the organization, borrower complaints, Home Mortgage Disclosure Act data.

Finally, Location Data Points takes commercially available demographic data and superimposes that data upon the Loan and Lender Data Points to look for trending.

Liability may be demonstrated under the Rule (as applied to the FHA) or the Baseline Modules (under ECOA). This can be done either “overtly” (obvious discrimination) or by illustrating disparities among approval/denial rates between applicants of different races, national origins, or sex. This also includes risk-based pricing that is not objectively based or financial incentives to loan officers or brokers on loans made to protected classes of persons. Furthermore, the statistically high percentages of a protective class of persons receiving a particular loan type or product or statistically high percentage of complaints about that loan type or product could also fall under this umbrella. These three, each by itself as well as any combination of the above, could represent liability.

Fair lending is all about the data. A lender can have absolutely no intent to discriminate on any basis whatsoever, but statistics will be used to show how a lender’s practices created a discriminatory effect upon those protected classes of persons.

Being ready for an enforcement action under the FHA, or a CFPB examination under ECOA, necessarily means being able to peer into your own data long before your organization appears on someone’s radar screen. Proactive culling of data points within your loan document system and trending that data regularly enables you to either spot problems and take corrective action, or simply take comfort in knowing that your organization’s policies and procedures are correctly working.

Defending against offensive use of statistics by a regulator or prosecutor is never a pleasant proposition – it is time consuming, costly, and frequently involves unfavorable publicity. But an effective counter-offensive is possible by understanding your own organization’s statistics and serving up your own data driven response. 

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