The Supreme Court And Disparate Impact

website-pdf-download

Jonathan-HermanIn an October 2013 article for Tomorrow’s Mortgage Executive, I posited the question, “Could it possibly be that, in a country where one is presumed innocent until proven guilty, a lender could be found to have discriminated on the basis of race or ethnicity, without any evidence that the lender actually intended to so discriminate?” On June 25, 2015, the United States Supreme Court answered my question by upholding the use of disparate impact in housing decisions. This article is intended to be a general discussion on why the U.S. Supreme Court reached the decision that it did and the practical effects of “disparate impact” on loan decisions.

Introduction and background of the Court’s decision

The Fair Housing Act (“FHA”) was adopted in the aftermath of Dr. Martin Luther King’s assassination, attempting to remedy racial segregation in the sale or rental of housing by prohibiting decisions on the basis of “race, color, religion, or national origin.” That is, disparate treatment of a protected class of persons, where one has a discriminatory intent or motive, was prohibited. The question posed to the U.S. Supreme Court was whether disparate impact claims were cognizable under the FHA. Those claims are ones in which the challenged practices have a disproportionate adverse effect on the same protected class, but where a discriminatory intent may not be readily apparent.

Featured Sponsors:

[huge_it_gallery id=”2″]

After first reviewing Title VII of the Civil Rights Act of 1964 (“Title VII”) and the Age Discrimination in Employment Act of 1967 (“ADEA”), the Court opined that “antidiscrimination laws must be construed to encompass disparate-impact claims when their text refers to the consequences of action and not just to the mindset of actors …” But the Court also observed that, “disparate-impact liability must be limited so employers and other regulated entities are able to make the practical business choices and profit-related decisions that sustain a vibrant and dynamic free-enterprise system.”

Applying its analysis of Title VII and the ADEA to the Fair Housing Act, the Court intoned that, “[r]ecognition of disparate-impact claims is consistent with the FHA’s central purpose.” The Court found that the Fair Housing Act, like Title VII and the ADEA, “was enacted to eradicate discriminatory practices within a sector of our Nation’s economy … [and s]uits targeting such practices reside at the heartland of disparate-impact liability.” The Court concluded by “acknowledg[ing] the Fair Housing Act’s continuing role in moving the Nation toward a more integrated society.”

But those who fear imposition of liability solely based on statistics can take some degree of comfort in cautionary language included in the Court’s opinion, “necessary to protect potential defendants against abusive disparate-impact claims.” The Court said, “a disparate-impact claim that relies on a statistical disparity must fail if the plaintiff cannot point to a defendant’s policy or policies causing that disparity.” “[r]acial imbalance … does not, without more, establish a prima facie case of disparate impact and thus protects defendants from being held liable for racial disparities they did not create.” (Ellipses and brackets in original, internal quotes omitted). “Courts must therefore examine with care whether a plaintiff has made out a prima facie case of disparate impact and prompt resolution of these cases is important.” “A plaintiff who fails to allege facts at the pleading stage or produce statistical evidence demonstrating a causal connection cannot make out a prima facie case of disparate impact.” “Courts should avoid interpreting disparate-impact liability to be so expansive as to inject racial considerations into every housing decision.”

The practical effect of the Court’s decision

The Supreme Court unquestionably recognizes disparate-impact claims. But the real question is what does the limiting language in the Court’s opinion mean in connection with real world lending decisions in our mortgage lending business model? That is, how does one attempt to comply with Fair Lending laws, in addition to all of our other compliance efforts?

Featured Sponsors:

[huge_it_gallery id=”3″]

For starters, intentional discrimination in lending decisions was, and remains, prohibited. But the Court’s language about what a plaintiff must allege to bring a Fair Housing claim, in my view, forces lenders to remain in a defensive posture. Both a private lawyer accepting a Fair Lending case, and a government lawyer acting on behalf of the Department of Justice, will use the Supreme Court’s opinion to carefully draft a Complaint, hoping to survive a defendant’s efforts to dismiss the claim. If the Complaint is not dismissed, the focus for lenders is unchanged: the use of data compiled during the mortgage origination process to demonstrate that the lending decision was racially neutral and that if a statistical disparity exists, there is no policy(ies) that caused an imbalance. In this regard, the suggested practical, proactive efforts to mitigate Fair Lending risk discussed in my prior, October 2013 article remain valid. In that article, I discussed the use of the “Three L’s,” namely Loan Data Points, Lender Data Points, and Location Data Points. Loan Data Points include core statistics concerning the loan (i.e. loan amount, interest rate and fees charged, loan type and property address, to name just a few) and data points from the loan application (i.e. national origin, sex, marital status, among others). This data may be “scraped” from your document preparation system and loan original system.

Lender Data Points may be gathered from your corporate records and will require 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, among others.

Location Data Points, from commercially available demographic data is superimposed upon the Loan Data Points and Lender Data Points, all to “trend” the data and look for variance that could evidence “discriminatory effect.”

Signs of discriminatory effect include disparities among approval/denial rates between applicants of different races, national origins, or sex (potential disparate treatment in underwriting), risk based pricing that is not objectively based or financial incentives to loan officers or brokers on loans made to protected classes of persons (potential disparate treatment in pricing). Outlier data that is suggestive of disparate treatment in steering includes 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. Potential discriminatory redlining or marketing include any or all of the above, but usually with a greater emphasis on demographic data in the relevant region (defined above as “Location Data Points”).

Always involve your attorneys in both compiling and interpreting the data, both to keep the conclusions confidential under the attorney client privilege and to evaluate the range of options in the event of a problem. Better still, allow your attorneys to associate with a vendor that can trend data in real time (i.e. collecting data contemporaneously with the loan application and closing documents), thereby enabling potential Fair Lending problems to surface prior to any expensive and time consuming enforcement action.

Conclusion

I continue to maintain that defending against a fair lending claim is never a pleasant proposition – it is time consuming, costly, and frequently involves a type of publicity that is not desired. There is nothing in the Supreme Court’s decision that changes that, even though the Supreme Court held that there must be a “robust causality requirement” to find disparate impact liability. Careful lawyers will continue to advocate that an apparent statistical disparity has a disparate effect on their client, and claims will be articulated using the Supreme Court’s opinion as a guidepost.

The plaintiffs’ claims and theories will be what they will be – the focus for defendants remains on using data as a shield, the same data that the plaintiff tries to use as a sword. Data itself does not lie on the witness stand and while a witness may slant his/her testimony to support his/her own interests, data that corroborates a documented, legitimate, non-discriminatory business interest will make a plaintiff’s burden rather difficult.

Documentation of non-discriminatory considerations in lending programs, along with developing preemptive strategies through real time loan data review, goes a long way towards spotting “trends” and “outliers” before your organization appears on a plaintiff’s radar screen and disrupts your sleeping habits.

About The Author

[author_bio]