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The article discusses developments in fair lending enforcement activities in the U.S. Topics include the largest redlining settlement announced by the Department of Justice (DOJ) in January 2023, federal regulatory developments, and litigation brought by city and county governments seeking to recover damages allegedly due to discrimination by mortgage lenders in their communities.

We document the existence of gender and racial gaps in dealer markups of auto financing interest rates. Most car buyers are unaware that dealers can mark up interest rates for auto loans, the third most prevalent U.S. consumer debt category, beyond the rate dictated by lenders. Dealers can set their markups without considering buyer risk profiles, potentially leading to discrimination. Using individual transaction-level data from a 20% random sample of U.S. car dealers between 2004 and 2015, this paper shows that women and minorities pay a statistically significant 0.6% and 2.6% greater interest rate markup than men and nonminorities, respectively. The racial and gender gaps in dealer markups largely attenuate over time. Additionally, the premia for minorities and women are larger for those buyers in census block groups with lower education levels, those not trading in a car, and those in markets with fewer financial institutions. Although we find no heterogeneity in the racial gap by age and political inclination, the gender gap increases for women who are relatively older and is significant only in majority Republican-voting counties. Our analyses do not provide a precise estimate of the interaction effect between race and gender. Based on the buyer characteristics considered, the most vulnerable group of women (minorities), accounting for 4.7% (8.5%) of car buyers, pay 7.6% (27.9%) more compared with men (nonminorities). The findings inform policymakers and consumers of the gender and racial gaps in auto financing, their underlying mechanisms, and situations most susceptible to unfair lending. History: Catherine Tucker served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: O.C. Ozturk thanks the Darla Moore School of Business research grant program for support. P. K. Chintagunta thanks the Kits Center for Marketing at the University of Chicago for financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mksc.2022.0295. [ABSTRACT FROM AUTHOR]

The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., "alternative data") stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel "meta" experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers' online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users' smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes. [ABSTRACT FROM AUTHOR]

The article examines developments related to fair lending laws and cases in the U.S. The Federal National Mortgage Association settled the complaint filed by the National Fair Housing Alliance for alleged discrimination in maintaining foreclosed properties. Changes to the Consumer Financial Protection Bureau's examination manual and supervisory operations to protect consumers from discrimination were announced in March 2022.

Focus on fair lending has become more intensified recently as bank and non-bank lenders apply artificial-intelligence (AI)-based credit determination approaches. The data analytics technique behind AI and machine learning (ML) has proven to be powerful in many application areas. However, ML can be less transparent and explainable than traditional regression models, which may raise unique questions about its compliance with fair lending laws. ML may also reduce potential for discrimination, by reducing discretionary and judgmental decisions. As financial institutions continue to explore ML applications in loan underwriting and pricing, the fair lending assessments typically led by compliance and legal functions will likely continue to evolve. In this paper, the author discusses unique considerations around ML in the existing fair lending risk assessment practice for underwriting and pricing models and proposes consideration of additional evaluations to be added in the present practice. [ABSTRACT FROM AUTHOR]

The article explores developments in fair lending litigations in the U.S. Topics discussed include a decision by the U.S. Ninth Circuit Court in the case City of Oakland v. Wells Fargo & Co. and its effect on fair lending claims by municipal governments and other plaintiffs, a complaint filed by the National Fair Housing Alliance against Redfin Corp. over its redlining practices, and a final disparate impact rule issued by the U.S. Department of Housing and Urban Renewal in 2020.

Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is independent of protected class status, even in a nonlinear sense. This procedure works for any machine learning method. The procedure was tested on subprime credit card data combined with demographic data by zip code from the US Census. The census data serves as an imperfect proxy for borrower demographics but serves to illustrate the procedure. [ABSTRACT FROM AUTHOR]

This article examines mortgage broker pricing in New York during the years leading up to the financial crisis. Broker compensation practices in 2005 through 2007, primarily the use of yield spread premiums (YSPs), led the Federal Reserve to promulgate new rules in 2011 that disallowed loan originators who receive compensation directly from the consumer from also receiving compensation from the lender or another party. This consumer testing rule passed because the board found that consumers were not aware of the payments lenders make to originators and how those payments can affect the consumer's total loan cost. Focusing on total costs paid by the borrower, we find that minority borrowers paid more in total fees as a percentage of the loan amount when including or excluding YSPs. Moreover, white borrowers were more successful in substituting YSPs for up-front cash fees, resulting in a reduction in total loan fees compared with minority borrowers. This may reflect information advantages for white borrowers that allow them to more accurately assess the total cost of loans. [ABSTRACT FROM AUTHOR]

A fair lending analysis seeks to determine whether a lender's underwriting, pricing, and marketing decisions are free from a prohibited basis effect. That is, free from a significant effect based on the protected categories identified in the federal Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FH Act). This study examined the underwriting decisions of reverse mortgages to determine whether race/ethnicity, gender, or age were significant factors in those decisions. The analysis used loan-level data from the expanded 2019 Home Mortgage Disclosure Act (HMDA). A logistic regression model was used to estimate the denial/approval decisions. The results showed statistically significant lending disparities by race/ethnicity, gender, and age. Black and Hispanic denial odds for reverse mortgages were 107% and 48% higher, respectively, than for non-Hispanic white applicants. Denial odds for women were 12% lower than for men. Denial odds for applicants aged 74 years and older were 16% lower than for those aged 62 to 73 years old. Other statistically significant model predictors included loan purpose, non-amortizing features, closed-end loans, fixed-rate interest, indirect application channel (wholesale), income, loan amount, home equity, and interaction terms. The analysis identified serious deficiencies in the HMDA data that impact the ability to investigate fair lending disparities in reverse mortgages. This concern evolves from recent legislative exemptions for certain lenders reporting on critical data fields. The study illuminated reverse mortgage lending disparities by race/ethnicity, suggesting economic harm to black and Hispanic applicants. To more effectively monitor these disparities, the HMDA law should be amended to remove all data exemptions. [ABSTRACT FROM AUTHOR]

The article examines developments in small dollar lending in the U.S. in 2019, including federal regulations, enforcement actions by federal and state authorities, state legislation, and state court rulings. Also cited are the anticipated final rule from the Consumer Financial Protection Bureau (CFPB), and the agency's Payday Lending Rule covering loans like payday, vehicle title, and high-cost installment.

The article discusses the remand ruling by the U.S. Eleventh Circuit based on the 2017 decision by the Supreme Court in the case Bank of America Corp. v. City of Miami on municipal lending. Also cited are the enforcement actions filed by the Department of Housing and Urban Development (HUD) and Department of Justice (DOJ) due to discriminatory practices in lending, and the case City of Miami v. Wells Fargo & Co.

With limited financial sophistication, entrepreneurial consumers approach the financial marketplace more like retail financial consumers than like business customers. However, an assumption of both legislators and regulators is that business borrowers are more financially savvy than consumer borrowers and thus do not require protections that are as broad reaching. This gap between marketplace policy protections and the lived reality of the vast majority of small business entrepreneurs sets the stage for entrepreneurial consumers to fall through the regulatory cracks, creating the potential for exploitation and abuse. This situation may be exacerbated for minority entrepreneurs, who belong to protected classes that generally are more vulnerable to exploitation in marketplaces, including the small business lending marketplace. This article details the current status of the policy gap relative to minority entrepreneurial consumers and presents a matched-pair mystery shopping study to demonstrate the critical need for reliable, primary data to inform regulatory agencies as they work to implement appropriate protections to ensure equal access to credit across the small business lending marketplace. [ABSTRACT FROM AUTHOR]

The article presents information on developments in fair lending in the U.S. It discusses the impact of the U.S. Supreme Court rulings on the scope of municipal fair lending litigation, the decision of the U.S. Department of Housing and Urban Development to modify its 2013 Disparate-Impact Rule, and the U.S. Supreme Court's ruling on the case, Bank of America Corp. v. City of Miami.

The following discussion is intended to analyze the benefits and costs to society from the implementation of the Act. No criticism of the purpose of the Act is intended or implied. The analysis does not cover the changes to the Act embodied in the Equal Credit Opportunity Act Amendments of 1976 (Public Law 94-239), since the Board published final regulations implementing that Act on December 29, 1976, and they did not become effective until March 23, 1977 [5] and [17]. [ABSTRACT FROM AUTHOR]

In the decade leading up to the US housing crisis, black and Latino borrowers disproportionately received high-cost, highrisk mortgages--a lending disparity well documented by prior quantitative studies. We analyse qualitative data from actors in the lending industry to identify the social structure though which this mortgage discrimination took place. Our data consist of 220 depositions, declarations and related exhibits submitted by borrowers, loan originators, investment banks and others in fair lending cases. Our analyses reveal specific mechanisms through which loan originators identified and gained the trust of black and Latino borrowers in order to place them into higher cost, higher risk loans than similarly situated white borrowers. Loan originators sought out lists of individuals already borrowing money to buy consumer goods in predominantly black and Latino neighbourhoods to find potential borrowers, and exploited intermediaries within local social networks, such as community or religious leaders, to gain those borrowers' trust. [ABSTRACT FROM AUTHOR]

Fair lending analysis of nonmortgage credit products often involves proxying for race/ethnicity since such information is not required to be reported. Using mortgage data, this paper evaluates a series of proxy approaches (geo, surname, geo-surname, and Bayesian Improved Surname Geocoding (BISG)) as compared with the race/ethnicity reported under the Home Mortgage Disclosure Act (HMDA). The BISG proxy predicts the reported race/ethnicity the best as judged by prediction bias, correlation coefficient, and discriminatory power. In assessing fair lending risks where classification of race/ethnicity is called for, we propose the BISG maximum classification, which produces a more accurate estimation of mortgage pricing disparities than the current practices. The above conclusions withhold various robustness tests. Additional analysis is performed to assess the proxies on nonmortgage credits by leveraging consumer credit bureau data. [ABSTRACT FROM AUTHOR]

In the aftermath of the Great Recession, the federal government began to enforce fair lending laws with a vigor previously unseen. To hold lenders accountable for the racially discriminatory effects of their mortgage lending practices, federal prosecutors and financial regulators applied the theory of disparate impact to fair lending laws for the first time. Unclear, however, is what legal standards exist to evaluate allegations of discriminatory effects in this industry. No court has ever decided a fair lending case under a theory of disparate impact on its merits. That will likely change soon as major municipalities are pushing the boundaries of this theory to its outer limits. Last June, the U.S. Supreme Court granted certiorari in Bank of America v. City of Miami to address a pair of discrete issues in this area. This Comment attempts to answer some foundational questions about how the theory of disparate impact will be applied to mortgage lenders moving forward. It also makes a normative argument for judicial acceptance of this theory and continued governmental enforcement of fair lending laws. [ABSTRACT FROM AUTHOR]

The statistical analysis of observational data for fair lending purposes relies on the assumption that, at the firm level, racial discrimination (or the lack thereof) is stable across time. Using data from a mortgage lender during the period 1998-2006, we examine this crucial assumption for the case of pricing differentials for black applicants in household mortgage lending, effectively evaluating possible dynamics in aggregate discrimination patterns. We offer evidence that these estimated pricing differentials may vary substantially across time. [ABSTRACT FROM AUTHOR]

The cyclical movement of housing prices likely affects the supply of and demand for credit for home purchases, but little is known about how this process might influence differential access to credit between minority and non-minority borrowers. This paper uses data reported through the Home Mortgage Disclosure Act (HMDA) over the period 1990–2013 to estimate the relationship between annual metropolitan area-level house price inflation and the extent to which Black borrowers are denied relative to ‘comparable’ White borrowers on their loan applications. The results indicate that, on average, Black borrowers are denied more frequently than White borrowers, but this difference in denial rates decreases significantly as house prices rise more rapidly. Such results demonstrate the importance of considering local housing market conditions when using HMDA data to assess lender compliance with fair lending laws. [ABSTRACT FROM AUTHOR]

Even though Asians are now the largest minority group participating in United States mortgage markets, research on differences in underwriting and pricing outcomes in mortgages typically focuses on the outcomes of African American and Hispanic borrowers. One explanation for the lack of attention on Asian outcomes follows from the perceived relative economic prosperity of this minority group, which may lead to the belief that they are less in need of consumer protection or policy support. While simple group averages of economic characteristics support this belief, documented heterogeneity of Asian experiences suggests that the use of other measures may be needed to account for the varied outcomes of Asians in the U.S. housing markets. Using a unique proprietary source of lender data, we examine these Asian outcomes. We find that Asians face challenges in mortgage markets in ways that may be unique as compared to other minority groups. For example, while an examination of unadjusted average denial rates indicates favorable outcomes for Asians compared to other minority groups, we find that after accounting for loan and borrower characteristics, Asians have denial rates as high as other minority groups. [ABSTRACT FROM AUTHOR]

This paper engages John R. Commons’s arguments regarding the relationship between law and capitalism with the legal adjudication of the U.S. subprime mortgage crisis. As going concerns, subprime lenders have opened up ambiguities in the law regarding the application of earlier legal frameworks to new profit-making activities. As with Commons’s analysis of the nineteenth century legal reframing of property and value, this has promoted a range of legal projects that attempt to work through borrowers’ remedial claims against lenders. In his work, Commons expressed a high degree of optimism that new authoritative sources would expand public powers to balance the growing economic power of financial and industrial going concerns. I argue that the last decade has seen substantial narrowing in the legal remedies available to borrowers who claim to have been harmed through their exposure to the subprime market. Even as highprofile enforcement actions against large lenders seem to represent the expansion of public powers to balance corporate power, I argue that the legal doctrines employed in these actions narrowly structure borrowers’ remedial powers and employ certain legal techniques – most notably, a tendency to settle cases before trial and use of contract modifications – that transfer legal uncertainty back to borrowers and structure harm as legally unproblematic. [ABSTRACT FROM PUBLISHER]

The article discusses several developments regarding private fair lending litigation in America as of February 2014, focusing on a disparate impact legal theory and various U.S. Supreme Court cases such as Wal-Mart Stores Inc. v. Dukes which deals with continuing enforcement actions the U.S. Department of Justice's Civil Rights Division. Racial discrimination claims, the rules of the U.S. Department of Housing and Urban Development, and class action proceedings in America are examined.

This paper evaluates the impact of underwriting decision on fair lending risk assessments of loan pricing. Using data from one national bank that contain a rich set of decisioning variables, we compare the estimation results from the single equation model with those from the sample selection type of models. Then we conduct three simulation studies to evaluate the sample selection bias and omitted variable bias under various scenarios. We demonstrate that the single equation approach could potentially generate biased estimates of pricing disparities when it fails to consider the impact of the underwriting decision. [ABSTRACT FROM AUTHOR]

The article discusses the impact of the decision of the U.S. Supreme Court on the employment discrimination litigation on the case Wal-Mart Stores Inc. v. Dukes. It states that the decision has created a private enforcement on fair lending laws while uncovering the importance of class-action litigation. The efforts of the administration of President Barack Obama in enforcing civil rights laws in the consumer finance field are also emphasized.

The article focuses on the developments in fair lending law in the U.S. in 2012. Topics include the U.S. Department of Justice's (DOJ) prosecution of loan borrower discrimination, fair lending laws involving the U.S. Department of Housing and Urban Development (HUD), and the U.S. Fair Housing Act. Information is provided on the Equal Credit Opportunity Act (ECOA).

We investigate minority access to small-business loans using a probit model of loan application denial that recognizes two loan types (line-of-credit loans and non-line-of-credit loans) made by two lender types (commercial banks and nonbank financial institutions). We estimate our model on data from the 1998 Survey of Small Business Finances. We find evidence consistent with minority equal access to bank credit lines and nonbank non-line-of-credit loans in highly competitive loan markets; in less competitive markets we find evidence consistent with unequal access to these loans. We also find evidence consistent with unequal minority access to bank non-line-of-credit loans, regardless of loan market competitiveness. Our findings differ from previous research which treats small-business loans as a homogenous product and finds evidence consistent with unequal minority access to small-business loans generally. We argue that the existence of multiple small-business lending technologies and loan specialization by lenders account for our findings and demonstrate the need to treat small-business loans as a heterogeneous product when investigating equal access to small-business credit. [ABSTRACT FROM AUTHOR]

During mortgage transactions, there is a risk that some customers will be charged higher pricing based solely on group membership. Identifying such disadvantaged customers is a primary objective for banking regulatory agencies. Whatever strategy is used, there is an inherent tradeoff between resource allocation and reliability of conclusions. While the likelihood of identifying all disadvantaged customers should be high, the rate at which non-disadvantaged customers are identified should be low. This study uses Monte Carlo simulation to analyze how six different strategies compare on these two performance measures. The results show that the optimal strategy with respect to maximizing reliability and minimizing cost depends on the likelihood and severity of disadvantage. Further, none of the strategies are highly successful at identifying disadvantaged applicants or minimizing the number of non-disadvantaged applicants reviewed. [ABSTRACT FROM AUTHOR]

The article offers information on the development of the fair lending area and enforcement in the mortgage laws. It states that U.S. Department of Justice (DOJ) filed three enforcement actions in the past year, including one where some of the targets successfully defended the action, an unusual phenomenon. It mentions that private fair lending litigation has been actively pursued against the mortgage finance industry as well.

The article focuses on lending discrimination litigation in the Equal Credit Opportunity Act (ECOA). It states the implementation of Home Mortgage Disclosure Act (HMDA) and the regulations of the U.S. Federal Reserve Board (FRB). It further informs that under ECOA, lending discrimination can be proved in three ways including overt discrimination, disparate treatment and disparate impact.

Testing for discrimination in mortgage lending requires classifying consumers into treatment and control groups. Classification is quite complicated, because Home Mortgage Disclosure Act (HMDA) data, the primary source of data for these analyses, contain information on the ethnicity, race, and gender for both primary and coapplicants. In addition, applicants have the option of reporting multiple races. Using these detailed data to construct standard groups, such as "Black," "Hispanic," and "White," requires subjective decisions on how to aggregate applications. This study uses a data-driven approach to classify applications, minimizing subjectivity. Using HMDA data, as well as data from a recent examination conducted by the Office of the Comptroller of the Currency, we disaggregated applications into the most basic subsets possible. Our objectives are to better understand the characteristics of applicants, analyze variation in denial rates across underlying subsets of applications, and develop a data-driven classification strategy feasible for fair lending analyses. [ABSTRACT FROM AUTHOR]

Amendments to the Fair Lending Act have exempted an age restriction on ownership from fair housing prohibitions. This paper studies the economic impact of such ownership restriction on housing values. Using American Housing Survey data, we find that there is a significant premium attached to the restrictive covenant when other factors are controlled. In particular, we find that imposing age restriction on ownership increases the housing values by anywhere from 10.5% to 12.7%, At the average house value, this is equivalent to a dollar amount between $14,642 and $17,399. The estimates are robust to different specifications in hedonic equations. [ABSTRACT FROM AUTHOR]

The article discusses the fair lending litigations in the mortgage finance in the U.S. It is noted that the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA) allow the courts to deal with cases filed against mortgage lenders. It further states that court decisions are rendered on pleadings issues in cases against mortgage lenders. Moreover, it mentions that the Department of Justice and the Federal trade Commission has filed the federal enforcement actions.

The article discusses the application of fair lending laws to subprime mortage pricing in the U.S. The author analyzes research on the disparities in loan approval and mortgage pricing and whether such gap represent discrimination. A pricing study by Doctor Marsha Courchane revealed that one reason why minorities pay higher mortgage rates is the greater likelihood of securing a mortgage through a broker. Under the Fair Housing Act, and the Equal Opportunity Act, racial discrimination in mortgage terms and prices is regarded as illegal.

Since the early 1990s, credit expanded relative to income, especially after 2001. It is hypothesized that traditionally uneven credit access and gaps in the costs of credit by demographic characteristics shrank during this period. Relying on data from the Federal Reserve’s Survey of Consumer Finance, this study looks at financial constraints, the costs of credit and a number of contributions to the costs of credit, including sources and types of loans. The results indicate that taste-based discrimination and structural discrimination may have persisted and possibly increased over time. Gaps in credit access and costs of credit have widened by race, remained high by income, but shrank by ethnicity. Part of the overall differences in credit access was a varying reliance on professional information when making decisions on debt. [ABSTRACT FROM AUTHOR]

The article presents information on four Annual Surveys that have reported on trends in fair lending litigation. These surveys were conducted on class actions under the Equal Credit Opportunity Act (ECOA), in which the plaintiffs alleged that sales of credit contracts to various auto finance companies had a discriminatory impact on protected racial minorities. One litigation on racial discrimination in housing finance involved BNC Mortgage Inc., and another involved Freedom Mortgage Team lnc.

In this article, the authors empirically test whether Small Business Administration (SBA) guaranteed lending has a greater impact on economic performance in markets with a high percentage of potential minority small businesses. This hypothesis is based on the assumptions that (1) credit rationing is more likely to occur in markets with a higher percentage of minority small businesses; and (2) SBA guaranteed lending is likely to reduce these credit rationing problems, thus improving economic performance in the local market. Using local market employment rates as the measure of economic performance, the authors find evidence consistent with this proposition. Specifically, the authors find a positive and significant impact of SBA guaranteed lending on the average employment rate in a local market. And, this impact is 200 percent larger in markets with a high percentage of potential minority small businesses. This result has important implications for public policy in general and SBA guaranteed lending in particular. [ABSTRACT FROM AUTHOR]

Objective. This article examines the role of relationship lending in the automobile loan market at a community development credit union (CDCU) and at a traditional community bank. Method. Data collected from actual car loan applications are used in a probit analysis to estimate the importance of selected demographic, financial, and loan-specific variables on the probability of loan approval at the two types of financial institutions. Results. We first show that the community bank relies on credit scoring, not relationship lending. Relationship lending is, however, a critical factor in the loan decision at the CDCU. Low-income households with strong ties to the CDCU are likely to receive loans, despite poor credit histories. Conclusions. If consolidation, deregulation, and technology move mainstream financial institutions away from relationship lending and toward credit scoring, CDCUs will occupy an increasingly critical niche for low-income households. [ABSTRACT FROM AUTHOR]