One of the headline-leading compliance issues during 2025 has been the Trump Administration’s announcement in Executive Order 14281(“Restoring Equality of Opportunity and Meritocracy”) to “seek to eliminate the use of disparate impact liability in all contexts to the maximum degree possible.” The focus of media coverage has been the impact on legal theory. – and rightfully so, given the significant consequences for potential legal liability. Under this theory, lenders (and other actors) may be held liable for policies or practices that have discriminatory consequences in practice, even if there is no proof of an intent to discriminate. However, there’s another aspect of the changed policy that has been overlooked by the media. I am referring to the use of the statistical theory that is the basis for calculating “disparate impact” and its use as a screening tool to uncover potential unintended discrimination. Whatever the legal consequences of the Administration’s policy, it would seem that lenders, in a good faith effort to discover and eliminate unintended discrimination, should incorporate disparate impact analysis as part of its risk management system.
Briefly, the underlying basis for the concept of disparate impact is “statistical significance”. When reviewing the outcomes of lending practices based on race, ethnicity or sex statistical analysis seeks to uncover results so extreme as to be highly unlikely to have occurred by chance. Bank examiners apply the concept at the “5% level of significance”. This means that if a lender’s activity is not discriminatory there’s only a 5% chance that the results would be obtained by chance. Note, statistically significant results do not prove discrimination. But they are an indication that the results are so disparate or extreme they warrant further investigation.
This is basically the conclusion of the Supreme Court itself in Texas Department of Housing & Community Affairs v. The Inclusive Communities Project, Inc published on June 25, 2015, which upheld the application of disparate impact under the Fair Housing Act. However, the Court imposed important limitations on the application of the theory. Specifically, “that a racial imbalance, without more, cannot sustain a claim.” The Court said a plaintiff must establish a “robust” causal connection between the challenged practice and the alleged disparities.
During the Biden Administration the federal bank regulators and the DOJ continued to rely heavily on statistically significant results as the cornerstone of several dozen redlining cases and appeared to pay lip service to the “robust causal” limitations requirement in Inclusive Communities. This, in turn, may have motivated the Trump Administration to counter the practice with its policy under Executive Order 14281.
These political and legal battles notwithstanding, the use of disparate impact analysis as a practical analytical tool to identify potential discrimination should be a part of any mortgage lender’s regulatory compliance risk management program. When statistically significant results are identified further analysis should be employed to determine the causes, be they internal or external. This would be consistent with the Court’s Inclusive Communities decision. Whatever Administration rules in Washington and whatever their attitude toward disparate impact, prudent risk management suggests that disparate impact evaluation based on sound statistical analysis should be maintained by all mortgage lenders as a good faith compliance risk management tool.
