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For his June cover story in National Mortgage Professional, staff writer Ryan Kingsley interviewed key members of the Gate House Compliance team, including partners Brian Montgomery and Michael Waldron, and top Gate House consultants Paul Hancock and Liza Warner.
Kingsley examined “the modern theory of redlining,” efforts by regulators and enforcement agencies to advance allegations of redlining “without demonstrating a lender’s intent to avoid or otherwise restrict access to mortgage credit in those communities.”
The modern theory, Kingsley writes, rests largely on whether lenders “originate a below-average number of mortgages in CRA-eligible census tracts,” which then “manufactures perceptions of discriminatory lending, while pushing lenders to manufacture their fair lending compliance.” The result is both enforcement and lender’s compliance becoming “a data exercise.”
Montgomery, who has served in the White House and at the highest levels of government across four different presidential administrations, argued that “even if there’s a change in administration, there will continue to be a focus on this topic, as there should be.” Moreover, the focus today has evolved to include not just to lenders but mortgage loan servicers, who have traditionally not kept data on the race of borrowers, putting the industry in new territory.
Hancock, a partner with K&L Gates who led the fair-housing and fair-lending enforcement program at the Department of Justice, offered a perspective from someone who has litigated fair lending compliance issues for four decades, maintaining that the approach the government is taking under the modern theory presents a real challenge:
“Our clients abhor [redlining],” but“[i]f the government is actually demanding a racial balance in loan originations, saying all lenders in the city of Chicago should make 20% of their loans in minority neighborhoods,” [for example,] “that’s a demand for a racial balance that is prohibited by the Constitution,” Hancock said. “It’s prohibited by civil rights laws. I think that, if tested, it would be rejected by the courts in this context.”
“Under the government’s theory, you can eliminate redlining by just making fewer loans in white areas. You’re not doing any more in minority areas,” Hancock says. “You’re making fewer loans in white areas and somehow that solves your legal problem. That just doesn’t make any sense.” Hancock says there hasn’t yet been a major case around fair servicing yet, though the government is looking for one. Hancock also notes: what is being demanded of lenders (and servicers) could change, putting them in a tough spot.
Waldron, who served as chief compliance officer for Bayview, says that the regime in place for many mortgage servicers (compared to fair lending) is “not as mature of a structure and mature from a thought-leadership perspective,” making it more difficult for servicer to stay ahead.
Warner, a partner with CrossCheck Compliance who leads its regulatory compliance, internal audit, and risk management team, and has 35 years examining the issues, concurred. Warner says the servicing side is “obviously is not as mature of a process of monitoring as it is on the origination side, and you don’t have a set of data like you have the [Home Mortgage Disclosure Act] data to compare results against.”
“It’s a little more challenging that way., Warner states, “you really have to understand what’s happening within the operation in order to conclude on anything with respect to the data.” Servicers “need to make sure as a company that the data is accurate, first of all, and that as an organization [they] understand what the data is telling [them], Warner says.
Hancock notes a large problem for the industry and with the government’s approach: much of the data available to lenders for analysis offers a rear-view perspective, making any ongoing benchmarking with peers implausible.
“What you don’t want to do,” Waldron explains, “is take action that inadvertently doesn’t mitigate the very issue that you’re trying to solve for.”
Kingsley concludes: Drawing bad conclusions from good data can lead lenders to make misguided investments or operational changes, inadvertently increasing long-term risks.