Fair Lending and the Home Mortgage Disclosure Act
Effective January 1, 2018, the Consumer Financial Protection Bureau (CFPB) has added 84 data fields that must be reported on the Loan Application Register (LAR) as required by the Home Mortgage Disclosure Act (HMDA). Since January 1, 2018, there have been 27 Enforcement Documents related to LAR from OCC, CFPB, and FDIC. The newly revised rule increases the collected fields by 73%. The Home Mortgage Disclosure Act was approved in 1975 and requires mortgage lenders to keep records of certain key pieces of information regarding their lending practices. This information includes the types of loans approved and denied along with ethnic and racial information of loan applicants. The primary purpose of HMDA is to help authorities monitor discriminatory and predatory lending practices, as well as to ensure government resources are allocated properly.
The new rule expands on the data collected from covered mortgage transactions as mandated by the Dodd-Frank Act and CFPB discretion for additional fields. Original data fields included; loan type, purpose, occupancy, loan amount, action taken (approved, denied, etc.), ethnicity, race, sex, census tract location, income, and lien status. The amendment has added fields such as; property address, age, credit scores, closing costs, interest rate, term, debt-to-income ratio, loan-to-value ratio, property value and originator NMLS number. As banks scramble to comply with the massive data reporting changes soon to be in effect, there is the looming question yet to be addressed.
What is the government planning to do with all of this collected data?
The consensus from compliance professionals is the regulatory focus on Fair Lending. With the new data fields, regulators can easily slice and dice data to take a detailed look at a bank’s lending practices. Regulators can drill down to a single bank branch and even so far as an individual loan originator’s lending patterns and practices. Regulators in the last few years have aggressively applied a controversial legal theory of disparate impact to site banks with fair lending violations. Under disparate impact, regulators rely heavily (and sometimes solely) on statistical sketches to justify enforcement actions.
The theory of disparate impact was addressed briefly in the mid-1990s but fell short due to its lack of reliability to identify actual violations of law. The last administration restored using statistics, not just to identify possible areas of illegal discrimination, but as proof of discrimination in the absence of evidence of intent to violate the law. The Supreme Court found that the language of the Fair Housing Act does not allow consideration of disparate impact to establish an FHA violation, and noted the potential for abuse of the concept. The Court’s decision held that disparate impact could only be used if applied in keeping with “cautionary standards” established by the Court. However, this “cautionary standard” is being ignored by federal regulators.
The impact of redlining
In recent years there has been an increasing focus on redlining as a grounds for fair lending enforcement. The specific practice called “redlining” began with the National Housing Act of 1934. The implementation of this federal policy aggravated the decay of minority inner-city neighborhoods. In 1935, “residential security maps” were created to indicate the level of security for real-estate investments in each surveyed city. On the maps, the newest areas – those considered desirable for lending purposes – were outlined in green and known as “Type A.” These were typically affluent suburbs on the outskirts of cities. “Type B” neighborhoods, outlined in blue, were considered “Still Desirable,” whereas older “Type C” were labeled “Declining” and outlined in yellow. “Type D” neighborhoods were outlined in red and were considered the riskiest for mortgage support. These neighborhoods tended to be the older districts in the center of cities; often they were coincidentally the minority neighborhoods.
As the government is responsible for the creation of redlining, they are now responsible for reinventing redlining in the modern-day fair lending environment. Previously, data analysis focused on disparate treatment to determine if a bank was intentionally avoiding lending in minority neighborhoods. Recently, regulators have been reaching beyond a bank’s geographical area to examine marketing and outreach efforts.
Recent enforcement actions question the relationship between the Community Reinvestment Act (CRA) and redlining. CRA was passed in 1977 to encourage banks to meet local credit needs, and assist in eliminating redlining. Under CRA, banks would identify the physical location of their loans to demonstrate adequate credit distribution within their communities. The bank defined CRA Assessment Area was the basis for the analysis which includes the bank’s main office, its branches, ATM locations, as well as surrounding geographies in which the bank has originated a substantial portion of credit.
Agencies have cited redlining violations which disregard a bank’s CRA assessment area. Regulators have overlaid their own, “reasonably expected market area” (REMA) – an area Agencies assert that the bank should serve. Regulators are now imposing their own judgment about the market area a bank should serve based on the subjective use of statistical analysis. The majority of this data is obtained on the bank’s HMDA LAR. The REMA appears to justify the regulator’s conclusion of redlining.
What guidance is available?
Regulatory agencies, such as the CFPB, FDIC, and the OCC have not offered any guidance about how a bank can define its own REMA or the circumstance which would trigger regulators to disregard a bank’s CRA assessment area. This lack of clarification incites fear and discourages bank outreach and innovation, afraid of violating an unknown statistical measure. This also leads banks to comply with the regulatory judgment, even though it may be flawed, just to avoid reputational risks and the costs of opposing regulators.
These ever-changing and vague fair lending theories force banks to collect their own data and run analysis to identify those policies or practices that a regulator “might” assert would have a negative impact on a protected group of people. The impact leads to uncreative credits to local borrowers, which may have an adverse effect on the community. This unfounded focus on disparate impact demonstrates a menace of supervisory assertion without controls. It gives regulators leverage over a law, and perpetuates minority based considerations rather than removing them. This trend threatens to degrade the core of what Fair Lending is supposed to be, and is fueled by the big data now provided under the new HMDA rules.
Banks and Credit Unions can stay up-to-date on the latest agency news and regulatory updates involving HDMA, HRA and other lending and mortgage lending topics with Compliance.ai’s Pro Edition Regulatory Intelligence platform. Seamlessly track and monitor topic-based financial regulatory content in real-time. Compliance.ai leverages AI and machine learning to do the tracking and monitoring for you, with content from hundreds of sources including: state and federal-level agencies, white papers, and news publications. Try it for free for 30-days. Start your trial now!
- American Bankers Association, Fair Lending, Fighting Illegal Discrimination: Promoting Growth for the Whole Community, April 2017: https://www.aba.com/Compliance/Documents/FairLendingWhitePaper2017Apr.pdf
- FDIC Law, Regulations, Related Acts: 5000 – Policy Statement on Discrimination in Lending: https://www.fdic.gov/regulations/laws/rules/5000-3860.html
- Next City Article: “How Redlining Segregated Philadelphia” https://nextcity.org/features/view/redlining-race-philadelphia-segregation