By Austin Cummings, research associate at the Fair Housing Center for Rights & Research
Access to credit is the “underpinning of economic inclusion and wealth-building in the United States” (Mitchell and Franco, 2018: 5). Credit inequality contributes to the wealth gap and undermines fair access to housing. Communities of color throughout the United States have been intentionally excluded from accessing credit, building home equity, and generating wealth, which has in turn structured differences in credit scores between African American and Latinx communities and white communities (Avery, Calem, and Canner 2004; Consumer Financial Protection Bureau, 2012; Fellowes 2006). Despite appearing to be a race-neutral and objective method, credit scoring perpetuates discriminatory outcomes in housing and access to lending. The history of discrimination in housing and lending, along with the increased reliance on credit history to evaluate potential tenants by housing providers, disproportionately disadvantages and disparately impacts communities of color throughout the United States and Northeast Ohio (Rice and Swesnik, 2012). This article provides an insight into the credit health of Cuyahoga County and the racial disparities in credit that persist throughout the COVID-19 pandemic in the region.
Credit Scores and Racial Inequity
Access to credit shapes an individual’s ability to purchase homes, buy cars, and access loans for starting a business or furthering their education. Credit scores and credit histories are used by landlords and property management groups to screen tenants, by insurers to determine home, life, and auto insurance rates, and sometimes by employers to evaluate job applicants. Credit also plays a key role in an individual or family’s ability to weather tough financial times.
Lenders and housing providers use credit scores and credit histories to assess the risk of lending to borrowers because they are supposedly an objective, race-neutral method for evaluating an individuals’ ability to pay back debts, make payments on time, and meet their fiduciary responsibilities. Despite not directly using a consumer’s race to calculate individual credit scores, the algorithms used to generate credit scores perpetuate long-standing racial inequalities in housing because the data used to evaluate individuals is heavily shaped by histories of discrimination in lending (Singletary 2020). For example, if an individual used a subprime loan to buy a home because they couldn’t access a higher quality loan due to redlining practices, that individual’s credit score would be dinged. Next, credit histories are negatively impacted by court-ordered judgements, debt-collections lawsuits, and other interactions with the criminal justice system. Debt-collection lawsuits have been reported to disproportionately go against African-Americans (Pew Charitable Trusts 2020). The criminal justice system treats African-Americans and Latinx individuals more harshly than whites (Alexander, 2020).
Credit scores may also be an unreliable tool because of inherent flaws in how they work and what they can predict. Researchers have found that credit reports are riddled with errors (Consumer Financial Kiel and Waldman, 2015), and have been shown to be an unreliable way of predicting an individual’s ability to make timely rental payments (Consumer Financial Protection Bureau, 2015). Reliance on credit scores and conventional credit histories in evaluating potential tenants and borrowers has a disproportionately adverse impact on Black and Latinx individuals, and individuals who use housing vouchers.
The expanded use of credit scoring systems to screen tenants, loan and mortgage applicants, and potential homebuyers disparately impacts communities of color (Rice, L. and Swesnik, D., 2012). Redlining and predatory lending created a dual-credit system, which our current credit scoring system is built upon and perpetuates. Due to the racial bias of the currently existing financial and lending institutions, blanket bans on tenants based on credit rating could potentially violate the Fair Housing Act.
Credit Scores and the COVID-19 Pandemic
Credit is usually built by paying bills on time. The COVID-19 pandemic has created a large amount of economic hardship, which could have long term impacts on credit scores. Discrimination in housing, lending, and employment, along with persistence of the racial wealth gap, make it far more difficult for communities of color to recover from financial setbacks, establish credit scores on par with their white peers, and build home equity (Kiel and Waldman, 2020). Actively excluded from accessing affordable, sustainable, and mainstream financial services, many borrowers of color are forced into participating in the secondary credit market, causing lower credit scores (Rice and Swesnik 2012). The lack of access to credit and large disparities in access to wealth shaped how various communities of color are weathering economic hardship associated with the COVID-19 pandemic.
During the COVID-19 pandemic, the Center on Budget and Policy Priorities reported that Black and Latino adults were more likely than white adults to struggle to cover expenses, experience higher rates of food hardship, and fall behind on rent payments (Nchako 2021). Despite receiving financial assistance from a variety of relief programs, millions of Americans are behind on critical payments, which could eventually impact their credit scores and future ability to access rental housing and affordable, high quality loans. The combination of economic hardship due to the COVID-19 pandemic could have serious implications for credit equality and fair access to housing.
Racial Disparities in Credit Health Persist Throughout Cuyahoga County During the COVID-19 Pandemic
Throughout the COVID-19 pandemic, racial disparities persisted in credit health between whites and communities of color. However, median credit scores increased, the share of individuals with subprime credit scores (scores below 600) decreased, and the share of individuals with any debts in collections decreased for communities of color in Cuyahoga County. Below is a breakdown of a handful of measures used by the Urban Institute to assess the credit health of a region.
Figure 1:
Throughout the COVID-19 pandemic, the overall percentage of folks with subprime credit scores in the state of Ohio fell, with 22% of Ohioans and 26% of all individuals residing in Cuyahoga County having subprime credit scores in July, 2021 (see Figure 1). Despite witnessing an overall decrease of individuals with subprime credit scores in communities of color throughout the COVID-19 pandemic, significant disparities persist between communities of color and majority white communities in Cuyahoga County. Only 12% of individuals residing in majority white communities had subprime credit scores, while roughly 46% of individuals residing in communities of color had subprime credit scores.
Figure 2:
Median credit scores increased across all communities in Cuyahoga County throughout the COVID-19 pandemic (see Figure 2), with the average credit score increasing from 676 to 688 in all of Cuyahoga County. Communities of Color in Cuyahoga County saw a greater increase in median credit score than other communities, raising to 613 in July, 2021. Significant disparities persist, with majority white communities having a median credit score of 744.
Figure 3:
The percentage of individuals with debt in collections in all communities in Cuyahoga County slightly reduced throughout the COVID-19 pandemic (see Figure 3). From February, 2020 to July, 2021 the percentage of individuals with any debt in collections through the entire county fell from 35.8% to 33.7%. The percentage of individuals from communities of color with any debt in collections in Cuyahoga County reduced in a similar fashion, dropping from 57.2% to 55.19%. Significant racial differences persist in the region however, with 19.6% of individuals from majority white communities in Cuyahoga County having any debt in collections.
Access to Credit
Credit scores and credit history, as noted above, shape access to credit for home loans. Previous research by the Fair Housing Center has found lenders in Northeast Ohio deny home-purchase mortgages to Black borrowers at more than twice the rate they do to white borrowers. In Cuyahoga County, some of the largest lenders have no branch presence and do very little of their business in census tracts where the majority of residents are people of color (Lepley and Mangiarelli, 2018).
Figure 4:
Recently released 2021 Housing Mortgage Disclosure Act Data illustrates that these disparities have persisted in the region (see Figure 4). Across all lenders in Cuyahoga County in 2021, Black borrowers were denied at more than twice the rate as white borrowers for conventional loans and one in half times more for FHA loans. Hispanic and Multi-Race borrowers were denied at almost two times the rate as white borrowers for conventional loans. Across both FHA and conventional loans, Black, Hispanic, and Multi-Race borrowers were denied loans at higher rate than the overall denial rate for the region, while white borrowers were denied below the overall denial rate for the region. One of the most cited regions for denying a potential borrower is credit history (See Table 1).
Table 1:
| Denial Reasons | Conventional | FHA |
| Debt-to-income ratio | 4.31% | 2.22% |
| Employment history | 0.20% | 0.47% |
| Credit history | 4.18% | 2.79% |
| Collateral | 2.31% | 2.19% |
| Insufficient cash | 0.35% | 0.78% |
| Unverifiable information | 0.82% | 0.51% |
| Credit application incomplete | 1.68% | 2.09% |
| Mortgage insurance denied | 0.01% | 0.01% |
| Other | 1.47% | 1.28% |
Conclusion
In Cuyahoga County, there are significant disparities in credit health across neighborhoods. Communities of color have the lowest median credit scores, the highest percentage of individuals with subprime credit scores, and the highest share of individuals having debt in collections. On the other hand, majority white communities have the highest median credit scores, lowest percentage of individuals with subprime credit scores, and lowest share of individuals having debt in collections. These disparities are reflected in access to mortgages, mortgage denial rates, and arguably shape access to rental housing. Relying on credit scores and credit histories to determine access to rental housing and mortgages disparately impacts communities of color and helps exacerbate existing inequalities in the region.
In data analysis there is a common adage — garbage in, garbage out. If the data we put into a model (no matter how perfectly structured) is not cleaned properly, inaccurately collected, or improperly measured, the findings are going to be inaccurate. In many ways, credit scores are essentially working the same way. The data used to evaluate the creditworthiness of individuals is heavily structured by past and current inequalities that make accessing credit extremely difficult and discriminatorily impact access to housing. At best, racial disparities in credit reflect historical inequities (Urban Institute, 2022). In practice, the reliance upon credit scores in evaluating borrowers and tenants disproportionately disadvantages communities of color, perpetuating another cycle of economic and housing injustice.
Citations:
Alexander, M. (2020, January 7). The New Jim Crow: Mass Incarceration in the Age of Colorblindness (10th Anniversary ed.). The New Press.
Avery, R., Calem, P., and Canner ,G. (2004). Credit Report Accuracy and Access to Credit. Federal Reserve Bulletin. summer04_credit.pdf (federalreserve.gov)
Consumer Financial Protection Bureau (2012). Analysis of Differences Between Consumer-and Creditor- Purchased Credit Scores. 201209_Analysis_Differences_Consumer_Credit.pdf (consumerfinance.gov)
Fellowers, M. (2006). Credit Scores, Reports, and Getting Ahead in America. The Brookings Institute. BrkgsFellowes3.qxd (Page 1) (brookings.edu)
Lepley. M. and Mangiarelli, L. (2018). Cuyahoga County Mortgage Lending Patterns. Fair Housing Center for Rights & Research. https://www.thehousingcenter.org/wp-content/uploads/2018/07/Cuyahoga-County-Mortgage-Lending-Patterns-2018-BEST-FOR-SCREEN.pdf.
Mitchell, B. and Franco J. (2018). HOLC “Redlining” Maps: The Persistent Structure of Segregation and Economic Inequality. National Community Reinvestment Coalition. NCRC-Research-HOLC-10.pdf
Nchako, C. (2021, June 21). Latest hardship data show continuing racial disparities. Center on Budget and Policy Priorities. Retrieved August 24, 2022, from https://www.cbpp.org/blog/latest-hardship-data-show-continuing-racial-disparities.
Kiel, P. and Waldman A. (2020, February 29). The Color of Debt: How Collection Suits Squeeze Black Neighborhoods. ProPublica. Retrieved September 7, 2022, from https://www.propublica.org/article/debt-collection-lawsuits-squeeze-black-neighborhoods
Rice, L. and Swesnik, D. (2012). Discriminatory Effects of Credit Scoring on Communities of Color. National Fair Housing Alliance. https://nationalfairhousing.org/wpcontent/uploads/2017/04/NFHA-credit-scoring-paper-for-Suffolk-NCLC-symposiumsubmitted-to-Suffolk-Law.pdf.
Singletary, M. (2020, October 16). Credit scores are supposed to be race-neutral. That’s impossible. The Washington Post . Retrieved August 24, 2022, from https://www.washingtonpost.com/business/2020/10/16/how-race-affects-your-credit-score/
The Pew Charitable Trusts. (2020, May 6). How Debt Collectors Are Transforming the Business of State Courts. The Pew Charitable Trusts. Retrieved September 7, 2022, from https://www.pewtrusts.org/en/research-and-analysis/reports/2020/05/how-debt-collectors-are-transforming-the-business-of-state-courts
Urban Institute. (2022, March 8) Credit Health during the COVID-19 Pandemic: How is your community fairing on credit health measures, 2022, Urban Institute, Retrieved August 8, 2022, from https://apps.urban.org/features/credit-health-during-pandemic/.