Alston & Bird Consumer Finance Blog

Fair Lending

GSEs to Require Mortgage Servicers to Obtain and Maintain Fair Lending Data

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A&B Abstract:

On August 10, 2022, the Federal Housing Finance Agency (“FHFA”) announced that Fannie Mae and Freddie Mac (the “GSEs”) will require mortgage servicers to obtain and maintain fair lending data on their loans, beginning March 1, 2023. That same day, Fannie Mae and Freddie Mac (the “GSEs”) each issued guidance implementing the FHFA announcement.

FHFA and GSEs’ Announcements

In its announcement, the FHFA indicated that Fannie Mae and Freddie Mac will require mortgage servicers to obtain and maintain fair lending data, to include borrower age, race, ethnicity, gender, and preferred language (“Fair Lending Data”), and to ensure that this data transfers with servicing throughout the mortgage term. The announcement follows FHFA’s May 2022 announcement that Fannie Mae and Freddie Mac will require mortgage lenders to collect borrowers’ language preference data as part of the loan application process via a Supplemental Consumer Information Form (SCIF). Shortly after the FHFA announcement, Fannie Mae and Freddie Mac each announced that their respective guides had been updated to require servicers to maintain  Fair Lending Data in a “queryable” format for each mortgage loan, if obtained during the origination process, for loans originated on or after Mach 1, 2023. Additionally, in instances of post-delivery servicing transfers, the transferor servicer must deliver to the transferee servicer the Fair Lending Data in a queryable format for each mortgage loan, if obtained during the origination process, for mortgage loans originated on or after March 1, 2023.  In the event of a future transfer of ownership or assumption of the mortgage loan, servicers are authorized, but not required, to update the Fair Lending Data elements.

Of course, many mortgage servicers currently do not receive complete and accurate borrower demographic data from originating lenders in a readily accessible format for all loans in their servicing portfolio. And servicers may have different resources, capabilities, roles (master servicers vs. subservicer), and electronic systems, which may present additional limitations. For example, Home Mortgage Disclosure Act (“HMDA”) data currently may not transfer to a transferee servicer as part of a servicing transfer. The Fair Lending Data elements generally reflect data that is collected for HMDA-purposes. Therefore, mortgage lenders and servicers will need to ensure that the Fair Lending Data is transferred to the transferee servicer such that the data remains queryable post-transfer. Finally, even where a servicer has access to robust HMDA data, it is unlikely that all the fair lending data elements noted in the FHFA and GSE announcements would be available. For example, mortgage loan originators subject to the data collection requirements of HMDA are required to collect information regarding a consumer’s sex, but not their gender. In this case, it is unclear how much, if any, information a mortgage servicer will ultimately have regarding a consumer’s gender.

Takeaway

Ultimately, even if a servicer is able to obtain and maintain the required Fair Lending Data elements, it remains to be seen what servicers will be expected to do with that information. Depending on the quality and completeness of the data, the servicer may engage in statistical analysis in order to monitor for fairness in servicing outcomes, such as approval rates, foreclosure rates, and processing timelines for loss mitigation evaluations, as well as fee assessment/waiver rates for all serviced loans. Yet this monitoring can only be done if the various parties – originating lender, master servicer, and subservicer – work together to ensure that all necessary data is complete and travels with the servicing of the loan. Thus, mortgage lenders/servicers should begin evaluating their systems to ensure the required Fair Lending Data can be obtained and maintained in a queryable format. Moreover, mortgage lenders/servicers should reevaluate their servicing transfer protocols to ensure Fair Lending Data is transferred and onboarded seamlessly such that the data remains queryable. Finally, it will be interesting to see whether the federal agencies (i.e., HUD, VA, USDA) follow in the GSEs’ footsteps and impose similar fair lending data requirements.

CFPB and DOJ Announce Redlining Settlement Against Non-Bank Mortgage Lender

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A&B Abstract:

On July 27, 2022, the Consumer Financial Protection Bureau (“CFPB”) and the US Department of Justice (“DOJ”) entered into a settlement with Trident Mortgage Company (“Trident”), resolving allegations under the Equal Credit Opportunity Act (“ECOA”) and the Fair Housing Act that the non-bank mortgage lender intentionally discriminated against majority-minority neighborhoods in the greater Philadelphia area. This settlement is the first redlining enforcement action against a non-bank mortgage lender and evidences the government’s continued focus on “modern-day redlining.”

The Settlement Terms

The Trident settlement, which requires the lender to pay over $22 million, resolves allegations that Trident, through its marketing, sales, and hiring actions, “discouraged” prospective applicants in the greater Philadelphia area’s majority-minority neighborhoods from applying for mortgage and refinance loans. However, much like the CFPB’s lawsuit against Townstone Financial, Inc. (“Townstone”), the settlement does not indicate that Trident treated neighborhoods or applicants differently based on race or ethnicity. Instead, the crux of the settlement is that Trident did not take sufficient affirmative action to target majority-minority neighborhoods. This, coupled with Trident’s mortgage lending reporting under the Home Mortgage Disclosure Act (“HMDA”), ultimately subjected the lender to enforcement.

Specifically, the CFPB’s press release notes that: (1) only 12% of Trident’s mortgage loan applications came from majority-minority neighborhoods, even though “more than a quarter” of neighborhoods in the Philadelphia MSA are majority-minority; (2) 51 out of Trident’s 53 offices in the Philadelphia area were located in majority-white neighborhoods; and (3) all models displayed in Trident’s direct mail marketing campaigns “appeared to be white;” (4) Trident’s open house flyers were “overwhelmingly concentrated” in majority-white neighborhoods; and (5) Trident’s online advertisements appeared to be for home listings “overwhelmingly located” in majority-white neighborhoods.

Similar to the Townstone lawsuit, the settlement does not indicate that Trident explicitly excluded certain neighborhoods or prospective applicants or actually discouraged applicants from majority-minority neighborhoods, only that such applicants “would have been discouraged.” While both the Townstone lawsuit and the Trident settlement reference remarks made by employees in their internal communications, there is no indication that employees ever made offensive or discouraging statements to prospective applicants of any neighborhood. Nevertheless, the CFPB settlement requires Trident to pay $18.4 million into a loan subsidy program to increase the credit extended in majority-minority neighborhoods in the Philadelphia MSA; $4 million in civil money penalties; $875,000 toward advertising in majority-minority neighborhoods in the Philadelphia MSA; $750,000 toward partnerships with community-based organizations; and $375,000 toward consumer financial education. The settlement also requires Trident to open and maintain four (4) branch offices in majority-minority neighborhoods of the MSA.

Takeaways

The Trident settlement is noteworthy for various reasons. In addition to being the first government redlining settlement with a non-bank mortgage lender, the resolution involves a variety of parties, including the CFPB, DOJ, and the states of Pennsylvania, New Jersey, and Delaware. Further, the settlement once again highlights that insufficient marketing and outreach in minority neighborhoods may be considered sufficient  actionable under ECOA and the Fair Housing Act. Indeed, it appears that a lender’s failure to precisely align its lending patterns with the geography’s demographics may serve as the basis of a redlining claim, even absent specific allegations of intentional exclusion or other discrimination. Finally, the settlement demonstrates that even a lender no longer in operation (Trident stopped accepting loan applications in 2021) is still a worthy defendant in the government’s eyes.

CFPB Continues Scrutiny of Algorithmic Technology

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On May 26, 2022 the Consumer Financial Protection Bureau released a Consumer Financial Protection Circular stating that creditors utilizing algorithmic tools in credit making decisions must provide “statements of specific reasons to applicants against whom adverse action is taken” pursuant to ECOA and Regulation B. The CFPB previously stated that circulars are policy statements meant to “provide guidance to other agencies with consumer financial protection responsibilities on how the CFPB intends to enforce federal consumer financial law.” The circular at issue posits that some complex algorithms amount to an uninterpretable “black-box,” that makes it difficult—if not impossible—to accurately identify the specific reasons for denying credit or taking other adverse actions. The CFPB concluded that “[a] creditor cannot justify noncompliance with ECOA and Regulation B’s requirements based on the mere fact that the technology it employs to evaluate applications is too complicated or opaque to understand.”

This most recent circular follows a proposal from the CFPB related to review of AI used in automated valuation models (“AVMs”). As we noted in our previous post on that topic, the CFPB stated that certain algorithmic systems could potentially run afoul of ECOA and implementing regulations (“Regulation B”). In that prior outline of proposals with respect to data input, the CFPB acknowledged that certain machine learning algorithms may often be too “opaque” for auditing. The CFPB further theorized that algorithmic models “can replicate historical patterns of discrimination or introduce new forms of discrimination because of the way a model is designed, implemented, and used.”

Pursuant to Regulation B, a statement of reasons for adverse action taken “must be specific and indicate the principal reason(s) for the adverse action. Statements that the adverse action was based on the creditor’s internal standards or policies or that the applicant, joint applicant, or similar party failed to achieve a qualifying score on the creditor’s credit scoring system are insufficient.” In the circular, the CFPB reiterated that, in utilizing model disclosure forms, “if the reasons listed on the forms are not the factors actually used, a creditor will not satisfy the notice requirement by simply checking the closest identifiable factor listed.” In another related advisory opinion, the CFPB earlier this month also asserted that the provisions of ECOA and Reg B applies not just to applicants for credit, but also to those who have already received credit. This position echoes the Bureau’s previous amicus brief on the same topic filed in John Fralish v. Bank of Am., N.A., nos. 21-2846(L), 21-2999 (7th Cir.). As a result, the CFPB asserts that ECOA requires lenders to provide “adverse action notices” to borrowers with existing credit. For example, the CFPB asserts that ECOA prohibits lenders from lowering the credit limit of certain borrowers’ accounts or subjecting certain borrowers to more aggressive collections practices on a prohibited basis, such as race.

The CFPB’s most recent circular signals a less favorable view of AI technology as compared to previous statements from the Bureau. In a blog post from July of 2020, the CFPB highlighted the benefits to consumers of using AI or machine learning in credit underwriting, noting that it “has the potential to expand credit access by enabling lenders to evaluate the creditworthiness of some of the millions of consumers who are unscorable using traditional underwriting techniques.” The CFPB also acknowledged that uncertainty concerning the existing regulatory framework may slow the adoption of such technology. At the time, the CFPB indicated that ECOA maintained a level of “flexibility” and opined that “a creditor need not describe how or why a disclosed factor adversely affected an application … or, for credit scoring systems, how the factor relates to creditworthiness.” In that prior post, the CFPB concluded that “a creditor may disclose a reason for a denial even if the relationship of that disclosed factor to predicting creditworthiness may be unclear to the applicant. This flexibility may be useful to creditors when issuing adverse action notices based on AI models where the variables and key reasons are known, but which may rely upon non-intuitive relationships.” That post also highlighted the Bureau’s No-Action Letter Policy and Compliance Assistance Sandbox Policy as tools to help provide a safe-harbor for AI development. However, in a recent statement, the CFPB criticized those programs as ineffective and it appears those programs are no longer a priority for the Bureau. So too, that prior blog post now includes a disclaimer that it “conveys an incomplete description of the adverse action notice requirements of ECOA and Regulation B, which apply equally to all credit decisions, regardless of the technology used to make them. ECOA and Regulation B do not permit creditors to use technology for which they cannot provide accurate reasons for adverse actions.” The disclaimer directs readers to the CFPB’s recent circular as providing more information. This latest update makes clear that the CFPB will closely scrutinize the underpinnings of systems utilizing such technology and require detailed explanations for their conclusions.

CFPB’s SBREFA Outline on Automated Valuation Models Rekindles Debate over Disparate Impact Liability under the ECOA

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Section 1473(q) of the Dodd-Frank Act (now codified at 12 U.S.C. § 3354(q)) amended the Financial Institutions Reform, Recovery, and Enforcement Act of 1989 (“FIRREA”) to instruct the CFPB, Fed, OCC, FDIC, NCUA, and FHFA (collectively, the “agencies”) to jointly develop regulations for quality control standards for automated valuation models (“AVMs”), defined as “any computerized model used by mortgage originators and secondary market issuers to determine the collateral worth of a mortgage secured by a consumer’s principal dwelling.” As part of the rulemaking process, the Small Business Regulatory Enforcement Fairness Act of 1996 (“SBREFA”) requires the CFPB to convene a Small Business Review Panel to consider whether the rule could have a significant economic impact on a substantial number of small entities. Accordingly, on February 23, 2022, the CFPB released an outline of proposals and alternatives under consideration by the agencies to seek informed feedback and recommendations from small businesses likely to be subject to the rule.

As amended, subparts (1) – (4) of FIRREA Section 1125(a) mandate that the agencies establish four specific quality control standards for AVMs. FIRREA Section 1125(a)(5) also affords the agencies discretion to adopt standards designed to “account for any other such factor that the agencies…determine to be appropriate.” As such, the CFPB’s SBREFA outline proposes creating a fifth such discretionary quality control standard “designed to protect against unlawful discrimination.”

In support of its proposal, the CFPB asserts that algorithmic systems such as AVMs are subject to Federal nondiscrimination laws, including the Equal Credit Opportunity Act (“ECOA”), because a lender evaluating an applicant’s collateral could use an AVM “in a way that would treat an applicant differently on a prohibited basis or result in unlawful discrimination against an applicant on a prohibited basis.” The CFPB then notes that it recognizes three different methods of proving discrimination under the ECOA and its implementing regulation (“Regulation B”): (1) overt discrimination; (2) disparate treatment; and (3) disparate impact. It is worth mentioning that overt discrimination has been viewed by federal regulators such as DOJ and the FDIC as a blatant type of disparate treatment that does not require an inference or presumption based on circumstantial evidence. However, it appears that the CFPB considers these theories to be distinct from one another.

The third method of proving discrimination articulated by the CFPB, disparate impact, has been a controversial theory of liability because it imposes liability on a creditor even where the creditor had no intent to discriminate against an applicant. Rather, the theory presumes that the creditor has treated applicants fairly and consistently in accordance with some facially neutral policy or procedure of the creditor. Of course, the disparate impact theory gained traction in the subprime lending cases post-2008 and then loomed large in the CFPB’s enforcement actions against indirect auto lenders, the latter of which were scrutinized by Congress in its decision to rescind the CFPB’s indirect auto lending guidance using the Congressional Review Act. In fact, it remains a legal question whether disparate impact claims are cognizable under the ECOA since the United States Supreme Court (“Supreme Court”) has never considered the issue, though civil rights advocates point to the Supreme Court’s willingness in the 2015 Inclusive Communities case to recognize the theory for discrimination claims brought under the Fair Housing Act (“FHA”).

Thus, should the agencies adopt a final rule that relies upon disparate impact under the ECOA as a legal basis to justify imposing a quality control standard on AVMs (or muddies the waters by relying upon both the ECOA and the FHA without distinction), it is possible that the rule could be challenged under the Administrative Procedures Act as not in accordance with the law. Alternatively, if the CFPB were to bring an enforcement action against a creditor for allegedly violating either the final rule’s quality control standard or ECOA itself on the basis of disparate impact, the creditor could defend itself by arguing among other things that disparate impact claims are not cognizable under the ECOA. Indeed, the ECOA lacks any “results-oriented” language like the “otherwise make available” language of the FHA or the “otherwise adversely affect” language of the Age Discrimination in Employment Act, which the Supreme Court, in decisions issued a decade apart, relied on in recognizing disparate impact liability.

Even if the plain language of the ECOA could not support a disparate impact claim, the CFPB might argue that the statute’s anti-discrimination provision is ambiguous (by asserting, for instance, that the word “discriminate” could be interpreted to encompass both intent-based and effects-based actions), in which case the CFPB may expect the reviewing court to grant its interpretation Chevron deference. See Chevron, U.S.A. v. Natural Resources Defense Council, Inc. 476 U.S. 837 (1984). But this argument also might prove difficult because Chevron deference is appropriate only when it appears that Congress has “delegated authority to the agency generally to make rules carrying the force of law, and … the agency interpretation claiming deference was promulgated in the exercise of such authority.” See Public Citizen, Inc. v. U.S. Dept. of Health and Human Services, 332 F.3d 654, 659 (D.C. Cir. 2003) (quoting U.S. v. Mead Corp., 533 U.S. 218 (2001)). In examining Regulation B, which was originally issued by the Fed and subsequently readopted by the CFPB, the only references to the concept of disparate impact appear in 12 C.F.R. § 1002.6(a) and Official Interpretation 6(a)-2. However, these provisions merely summarize the ECOA’s legislative history and Supreme Court precedent under Title VII of the Civil Rights Act, and even then, acknowledge only that the ECOA “may” prohibit acts that are discriminatory in effect. The CFPB has articulated its belief that disparate impact is cognizable under the ECOA elsewhere, including in a compliance bulletin and its examination manual, but those materials carry no force of law under the CFPB’s own recently-adopted rule. Thus, a reviewing court could conclude that the mere recitation of legislative history and of a judicial doctrine developed under an unrelated statute was not an actual exercise of rulemaking authority under the ECOA, and therefore that the agencies’ interpretation of the ECOA as expressed in Regulation B is not entitled to Chevron deference. In that circumstance, the reviewing court would be free to resolve any purported ambiguity in the ECOA according to its own construction, affording respect to the agencies’ position only to the extent it is persuasive. And should either of these issues – disparate impact under the ECOA or the availability of Chevron deference – ultimately be appealed to the Supreme Court, there may well be four justices willing to grant certiorari to consider them.

The uncertain outcome of any challenge to the CFPB’s use of disparate impact in a rulemaking or in enforcing the ECOA, given the stakes involved, suggests that the CFPB may seek to resolve matters via settlement rather than risking litigation in federal court. However, only time will tell whether the CFPB is spoiling for a fight.

Modern-Day Redlining Enforcement: A New Baseline

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On October 22, 2021, the U.S. Department of Justice (DOJ) announced an aggressive new initiative, in collaboration with U.S. Attorneys’ Offices throughout the country, to combat the practice of redlining. Three days prior, the Consumer Financial Protection Bureau (CFPB) was said to be hiring up to 30 new enforcement attorneys to focus on redlining and other fair lending enforcement. While these developments are not surprising for an Administration that has emphasized the importance of promoting racial equity, particularly in homeownership, this swift and purposeful action by federal regulators signals that these agencies mean business. Indeed, as evidence of this new priority, federal regulatory agencies have issued two multimillion–dollar redlining settlements against financial institutions just in the past two months.

Since the early 1990s, federal regulatory agencies have recognized redlining as a type of illegal “disparate treatment” (i.e., intentional) discrimination that violates federal fair lending laws such as the Fair Housing Act and the Equal Credit Opportunity Act (ECOA). For example, in 2009, the agencies defined the term “redlining” as a form of disparate treatment discrimination where a lender provides unequal access to credit, or unequal terms of credit, because of the race, color, national origin, or other protected characteristic of the residents of the area where the credit seeker resides or will reside or where the residential property to be mortgaged is located. As recently as 2019, the DOJ continued to use the term “redlining” to refer to a practice whereby “lenders intentionally avoid providing services to individuals living in predominantly minority neighborhoods because of the race of the residents in those neighborhoods.”

To that end, the earliest redlining enforcement actions were brought against banks whose alleged intent to discriminate could be the only explanation for the bank’s geographic distribution of loans around, but not in, minority communities. As proof of a bank’s intent to discriminate, the DOJ produced brightly colored maps to support its position that a bank had unnaturally drawn its service area boundaries to circumvent minority neighborhoods from its mortgage lending and then painstakingly adhered to this “red line” to avoid serving these neighborhoods. In Atlanta, one bank allegedly drew a red line down the railroad tracks that divided the city’s white and black neighborhoods, while in the District of Columbia, another bank allegedly drew its own line down the 16th Street corridor. Years later, in Detroit and Minneapolis-St. Paul, still other banks were alleged to have served a virtual “horseshoe” encompassing white neighborhoods while carving out minority neighborhoods. And again, in Indianapolis, a bank allegedly drew an “Indy Donut” that encircled and excluded the minority areas in the center of the city. In these cases, given that the banks were required by the Community Reinvestment Act (CRA) to define the areas they intended to serve, the DOJ pointed to the banks’ use of different, and in some cases, oddly shaped, service area boundaries (as opposed to existing legal borders or contiguous political subdivisions) as evidence of intent to discriminate.

Today, the majority of mortgage loans in the United States are made by nonbank mortgage lenders that, while not subject to the CRA’s requirements, remain bound by the antidiscrimination provisions of the Fair Housing Act and ECOA. In lieu of maps and service area boundaries, federal regulators now look to the loan application and origination data reported by the lender under the Home Mortgage Disclosure Act (HMDA) as the starting point for a redlining investigation. If the HMDA data suggests that a mortgage lender’s generation of mortgage loan applications or originations in majority-minority census tracts might not be as strong as that of its “peers” (e.g., similarly sized competitors), a federal regulator may initiate an investigation to determine whether the lender has violated fair lending laws. Of course, because data about “racial imbalance” has been deemed by the U.S. Supreme Court to be insufficient for establishing a prima facie case of discrimination, a federal regulator must supplement the data with evidence that the lender’s arguably weaker performance in minority neighborhoods may have resulted from an intent to discriminate by excluding or otherwise treating those areas differently.

Recently, however, the evidence cited by federal regulators to establish redlining has evolved and expanded significantly. Specifically, regulators appear to be relying on a “discouragement” theory of redlining that looks at the totality of the circumstances to determine whether a reasonable person would have been discouraged from applying for a loan product or service – perhaps regardless of whether the lender intended to discriminate. It is worth noting that this theory derives from ECOA’s implementing regulation, Regulation B, which extends the statute’s protections to “potential” applicants, and is not found in the language of ECOA itself.[1] While a lender is prohibited by Regulation B from making discouraging oral or written statements to an applicant on the basis of race or other protected characteristic, long-standing federal agency guidance indicates that a finding of discouragement necessarily requires some evidence of differential treatment on a prohibited basis. Traditional examples of discouragement have included the use of phrases such as “no children” or “no wheelchairs” or “Hispanic residence,” or a statement that an applicant “should not bother to apply.” In contrast, recent redlining enforcement suggests that federal regulators may be interested in the multitude of factors that could have contributed to a lender’s observed failure to reach minority neighborhoods, which, when taken together, may prove the lender’s intent to discriminate.

For example, federal regulators appear to be scrutinizing a lender’s marketing efforts and strategies to determine whether the lender has sufficiently prioritized minority areas. Prior to 2020, redlining cases highlighted the lender’s alleged failure to market in minority areas by intentionally treating these areas differently, either by allegedly excluding such areas from any marketing campaigns or using different marketing materials, such as solicitations or offers, for white versus minority areas.[2] The most recent redlining cases, however, suggest that lenders’ marketing strategies might need to go beyond treating white and minority neighborhoods consistently. Specifically, in its summer 2021 Supervisory Highlights, the CFPB called out a lender that had engaged in redlining by marketing via “direct mail marketing campaigns that featured models, all of whom appeared to be non-Hispanic white” and using only “headshots of its mortgage professionals in its open house marketing materials … who appeared to be non-Hispanic white.” Notably, the CFPB did not indicate that the lender had marketed to, and conducted open houses in, white neighborhoods while excluding minority neighborhoods, nor that the lender had used different marketing materials for white versus minority neighborhoods. Rather, the CFPB’s claim effectively acknowledges that residents of minority neighborhoods would have received the same marketing materials as any other neighborhood. Yet the CFPB’s position appears to be that the use of white models and white employees in these otherwise neutral marketing materials would have discouraged a prospective applicant in a minority area, regardless of whether the lender intended to discourage anyone or not.

Indeed, recent redlining enforcement suggests that not only will regulators allege it insufficient to treat all applicants and neighborhoods the same, but a lender must undertake affirmative action to specifically target minority neighborhoods. This approach attempts to impose unprecedented, CRA-like obligations on nonbank mortgage lenders to proactively meet the needs of specific neighborhoods or communities and ensure a strong HMDA data showing – or else be subject to redlining enforcement. For example, the July 2020 complaint filed by the CFPB against Townstone Financial Inc. claimed that the lender had “not specifically targeted any marketing toward African-Americans.” Along the same vein, the August 2021 settlement between the DOJ, Office of the Comptroller of the Currency (OCC), and a bank in the Southeast resolved allegations that the lender had failed to “direct” or “train” its loan officers “to increase their sources of referrals from majority-Black and Hispanic neighborhoods.” Of course, lenders understand that “specifically targeting” prospective customers or neighborhoods on the basis of race or other protected characteristic is not required by, and may present its own risk under, fair lending laws. Indeed, the CFPB has suggested that the industry might benefit from “clarity” of how to use “affirmative advertising” in a compliant manner. Similarly, the CFPB’s allegation that Townstone had “not employ[ed] an African-American loan officer during the relevant period, even though it was aware that hiring a loan officer from a particular racial or ethnic group could increase the number of applications from members of that racial or ethnic group” was not only irrelevant since the lender’s main source of marketing was mass market radio advertisements but also presumptive and problematic from an employment-law perspective.

Setting aside the legal questions raised by this expanded approach to redlining, mortgage lenders will also face practical considerations when assessing potential fair lending risk. Given the mortgage industry’s extensive use of social media, lead generation, artificial intelligence, and other technologies to carry out marketing strategies and disseminate marketing material, an inquiry by a federal regulator into potential discouragement of certain applicant groups or areas could be endless. Could every statement or omission made by an employee on any form of media be relevant to a redlining investigation? How many statements or omissions would it take for a federal regulator to conclude that a lender has engaged in intentional, differential treatment based on race or other protected characteristic? To that end, could personal communications between employees, which are not seen by the public, and thus could not have the effect of discouraging anyone from applying for a loan, nevertheless be sought by a federal regulator to further a case of intentional discrimination? The language of recent redlining cases suggests that a regulator may find these communications relevant to a redlining investigation even if they do not concern prospective applicants.

Ultimately, both federal regulators and mortgage industry participants must work together to promote homeownership opportunities in minority areas. But along the way, a likely point of contention will be whether enforcement should be imposed on a lender’s alleged failure to develop and implement targeted marketing strategies to increase business from minority areas, such as expanding the lender’s physical presence to minority areas not within reasonable proximity to the lender’s existing offices, conducting marketing campaigns directed exclusively at minority areas, and recruiting minority loan officers for the specific purpose of increasing business in minority areas. Such an approach might overstate the meaningfulness of physical presence and face-to-face interaction in the digital age, when lenders rely heavily on their online presence.

Of course, there may be legitimate, nondiscriminatory business reasons for a lender’s chosen approach to its operations and expansion. It remains to be seen whether those reasons will be sufficient to assure a federal regulator that the lender’s arguably weak performance in a minority area was not the result of redlining. However, given that nearly all precedent regarding redlining has been set by consent orders and has yet to be tested in the courts, the outcome of any particular investigation will greatly depend on the lender’s willingness to delve into these issues.

[1] See 12 CFR § 1002.4(b), Comment 4(b)-1: “the regulation’s protections apply only to persons who have requested or received an extension of credit,” but extending these protections to prospective applicants is “in keeping with the purpose of the Act – to promote the availability of credit on a nondiscriminatory basis.”

[2] For example, the Interagency Fair Lending Examination Procedures identify the following as “indicators of potential disparate treatment”: advertising only in media serving nonminority areas, using marketing programs or procedures for residential loan products that exclude one or more regions or geographies that have significantly higher percentages of minority group residents than does the remainder of the assessment or marketing area, and using mailing or other distribution lists or other marketing techniques for prescreened or other offerings of residential loan products that explicitly exclude groups of prospective borrowers or exclude geographies that have significantly higher percentages of minority group residents than does the remainder of the marketing area.