American Dream Deferred: The Effects of Credit Worthiness on Mortgage Access for Racialized Minorities in Los Angeles County
Miguel Miguel, graduate student, UCLA Urban Planning
This report is part of a larger research project led by UCLA Latino Policy and Politics Institute faculty affiliate, Dr. José Loya, on several topics related to stratification in homeownership, including ethno-racial, gender, and Latino disparities in mortgage access.
Homeownership is central to creating and growing wealth in the United States, but access to this wealth-generating vehicle is limited for households of color. In 2022, homeownership rates for Latinos and communities of color continue to lag behind whites. The mortgage industry is a central driver of this racial and ethnic stratification in homeownership. Ample research demonstrates that the unequal treatment of racialized minorities1 has created differentials in access to low-cost loan products that facilitate the American Dream of homeownership. Despite anti-discrimination laws and regulations, access to homeownership remains elusive for households of color.
In this brief we examine the role of race and ethnicity in the mortgage market by considering credit worthiness and its effects on access to mortgage credit in Los Angeles County. We draw on 2018 and 2019 pre-pandemic data from the Home Mortgage Disclosure Act (HMDA), a public dataset maintained by the Federal Financial Institutions Examination Council with individual-level completed loan applications. Our findings suggest that the credit worthiness of Blacks and Latinos in Los Angeles County is not valued equally in the housing market.
Our key findings are:
- Latino and Black applicants were less likely to be approved for a conventional loan and more likely to be approved for a high-cost loan or denied a mortgage.
- Black and Latino home seekers with excellent credit worthiness are just as likely to obtain a high-cost loan as are white applicants with poor credit worthiness.
- Black and Latinos with excellent creditworthiness were denied at twice the rate as white applicants with similar credit worthiness. The trends for Asians were similar to those for white
Based on the findings, we propose the following policy recommendations:
- Adjust the factors used by financial institutions to determine credit worthiness by considering an applicant’s rental history as part of the underwriting process.
- Local homeownership programs should expand and strengthen their focus to better assist home seekers of color, either by assisting residents’ efforts to improve their credit worthiness or by reevaluating the role of credit worthiness in down payment assistance programs.
- Improve data collection under the Home Mortgage Disclosure Act (HMDA) by expanding the current set of socioeconomic indicators collected for the and made available in public HMDA datasets.
Using homeownership as a tool to reduce the racial and ethnic wealth gap in Los Angeles County will continue to be constrained until racial and ethnic inequality in the mortgage market is resolved. Without such policy interventions, Black and Latino communities will continue to be excluded from the homeownership opportunities offered by low-cost fixed-rate loans.
Homeownership is central to creating and growing wealth in the United States, but access to this wealth-generating vehicle is limited by racial and ethnic inequality. In addition to financial benefits, homeownership is associated with an array of community amenities, including access to high-quality public schools, increased opportunities for social networking, and lower crime rates.2 Despite anti-discrimination laws and regulations, access to homeownership remains elusive for households of color. In 2022, the national homeownership rate hovered around 74 percent for non-Hispanic whites (hereafter “whites”), 61 percent for Asians, and only 45 percent and 49 percent for non-Hispanic Blacks (hereafter “Blacks”) and Latinos, respectively.3
While socioeconomic factors contribute to a large share of these differences, unequal access to mortgage financing remains a key structural source of racial and ethnic stratification in homeownership. Although Black and Latino applicants generally underperform in the mortgage market when compared to their white and Asian counterparts, it is unclear how racial and ethnic disparities in loan outcomes differ across debt-to-income levels.4 The debt-to-income ratio, defined as the percentage of gross monthly income that is used to pay the applicant’s minimum monthly debt obligations prior to obtaining a mortgage, is an important metric that helps lenders assess the economic risk and credit worthiness a borrower poses to a financial institution.5
The impact of credit on racial and ethnic stratification in the United States has a long, troubled history. For decades, explicit racial and ethnic discrimination in the housing market, such as redlining, has been an incredibly powerful tool, limiting access to credit and financial resources to minorities and communities of color, and spurring investment in predominantly white neighborhoods.6 To obtain a mortgage, an applicant must have access to credit products, a strong credit history, and funds for the down payment and closing costs. While credit access has increased for all racial and ethnic groups since the 1990s,7 racialized minorities have disproportionately absorbed higher-cost credit products that often lead to greater debt and constrain access to future credit lines.8 When applicants of color do obtain a loan, either secured or unsecured, the loans are often for smaller amounts, and borrowers pay higher fees and interest rates.9 The dynamic relationship between unequal access to credit products and higher credit fees and interest rates makes people of color particularly vulnerable in the credit market and less likely to achieve homeownership.
Many studies rely on controlling for income and requested loan amount as proxies for credit worthiness.10 This is potentially problematic since credit worthiness varies tremendously across racial and ethnic groups, thus potentially distorting our understanding of racial and ethnic stratification in the mortgage market. Studies previous to 2018 were constrained because data collected in compliance with the federal Home Mortgage Disclosure Act (HMDA), whose purpose is to monitor minority access to the mortgage market, did not include information on the borrower’s credit worthiness, and the private datasets meant to fill this gap examined specific geographical areas.11 The latest additions of economic-risk indicators to the HMDA database allows for a comparison of loan outcomes by racial and ethnic difference and the debt-to-income ratio of applicants. In this brief, we use HMDA data to examine the role of race and ethnicity in the mortgage market by considering credit worthiness and its effects on access to mortgage credit. The findings offer a better understanding of access to homeownership in Los Angeles County across racial and ethnic groups and inform policies, programs, and practices to expand access.
Data and Methods
We drew on publicly available HMDA data published by the federal Consumer Financial Protection Bureau for applications received in 2018 and 2019. Under HMDA, banks collect information on loans that are originated, non-originated (declined, withdrawn, closed for incompleteness, or approved but not accepted), and purchases. The dataset contains a record for every application reported and includes the borrower’s sociodemographic characteristics, loan details (including an indicator for a high-cost loan), property type, census tract identifier, and the outcome of the application, including the reason for denial.
In the multivariant model, we restricted the HDMA dataset to noninstitutional applicants requesting credit for an owner-occupied single-family home (of one to four units) in the United States through a conventional or jumbo mortgage (Veteran’s Administration, refinance, and subordinate applications are not included). We employed listwise deletion for observations containing missing data. The final dataset contains roughly 3.3 million mortgage applications.
The dependent variable in the analysis is the outcome of a completed loan application. There are three possible outcomes for all applications: 1) borrowers can be granted a high-cost loan, which we define as a loan with an annual percentage rate (APR) of 1.5 points or greater than the average prime APR; 2) they can be granted a conventional loan (all loans other than those that are high cost); 3) their application can be denied. All loan denials were included in the study except for those that were rejected for an incomplete application. The result was a multinomial dependent variable that distinguishes between a conventional loan approval, a high-cost loan approval, and a denial.
The primary independent variables are the race-ethnicity of the primary applicant and their debt-to-income ratio (DTI). An applicant was defined as Latino if they identified as Hispanic; non-Latino applicants were defined as white, Black, or Asian. We distinguished between four levels of DTI ratio: less than 36 percent (excellent); between 36 percent and 40 percent (above average); between 40 percent and 45 percent (below average); and greater than 45 percent (poor).
We also controlled for the sociodemographic characteristics of the borrower, including their gender, age, whether there was a co-applicant, and the total income of the applicant. In addition, we accounted for loan characteristics, including the loan amount requested, the percentage required for down payment, loan terms (in years), and whether the loan was an interest-only loan and whether it included a balloon payment. We also considered neighborhood (as determined by census tract) characteristics, including the average age of housing in the community, the percentage of whites in the neighborhood, and the average income of households in the neighborhood. We then controlled for the year of the loan application and the U.S. region in which the property is located, as defined by census guidelines. Finally, the dataset was limited to mortgage loan applications that occurred in Los Angeles County.
Finding 1. Latino and Black applicants were less likely to be approved for a conventional loan and more likely to be approved for a high-cost loan or denied a mortgage.
Figure 1 presents outcomes for completed loan applications in Los Angeles County by applicants’ race-ethnicity and DTI ratios. The chart shows large racial and ethnic disparities in application outcomes across DTI levels. Compared to white and Asian applicants, Latino and Black applicants were generally less likely to be approved for a conventional loan, more likely to be approved for a high-cost loan, and more likely to be denied a mortgage. Outcomes for Asian applicants were similar to those for whites. The figure also highlights substantial DTI ratio differences within racial and ethnic groups, with the proportion of adverse outcomes increasing as the DTI level increased. The intersection of racial and ethnic groups and DTI levels also shows tremendous variation. Latino and Black applicants with excellent DTI ratios received a conventional loan at a rate that was similar to that of whites and Asians with the poorest DTI ratios. In addition, the decline in the proportion of conventional loans across DTI levels was more dramatic for Black applicants than for any other racial and ethnic group.
Figure 1. Outcomes of Loan Applications by Race-Ethnicity and Debt-to-Income Ratio in Los Angeles County, 2018-2019
Debt-To-Income Levels by Race/Ethnicity
Source: HMDA 2018-2019.
Finding 2. Black and Latinos with a high debt-to-income ratio are more likely to be approved for a high cost loan rate versus whites with similar debt-to-income ratios.
Figures 2 shows the adjusted probability of obtaining a loan by applicants’ race-ethnicity and debt-to-income level for high-cost loans.12 The intersection of race-ethnicity and credit worthiness in mortgage lending is clearly illustrated. For example, among borrowers with excellent DTI ratios (less than 36 percent), the adjusted predicted probability of obtaining a high-cost loan was about 18 percent for Blacks and Latinos, while whites and Asians had a lower rate of about 11 percent. In addition, for Black and Latino borrowers with very poor DTI ratios (greater than 45 percent), the rate was about 27 percent, compared to about 20 percent for whites and Asians. Moreover, within each ethnic-racial group, Black and Latino applicants with excellent DTI perform similar to that of whites and Asians with DTI ratios that were below average (between 40 percent and 45 percent). In addition, the high-loan outcome disparity for Blacks and Latinos worsens as DTI ratios increase.
Figure 2. Adjusted Predicted Probability Model of High-Cost Loan Rates in Los Angeles County, 2018 – 2019
Adjusted Predicted Probabilities for Race-Ethnicity Groups by Debt-To-Income Level
Source: HMDA 2018-2019.
Note: 95 percent confidence intervals displayed using ends of whiskers.
Finding 3. Black and Latinos with excellent creditworthiness were denied at twice the rate as white applicants with similar credit worthiness.
Figure 3 shows the predicted probabilities from the same multivariate model of loan outcomes, this time for a mortgage denial. The racial and ethnic disparities differ when mortgage denials are compared to high-cost loans. Once again, white applicants were more successful than Black applicants. The success of Latinos generally fell between that of whites and Blacks, and Asians and whites generally performed similarly at each DTI level. The exception to this pattern was for applicants with poor DTI ratios; here, outcomes for Asians and Latinos fell between those for whites and Blacks. The adjusted mortgage denial rate for Blacks and Latinos with excellent DTI ratios was about 13 percent compared to about 5 percent for similar whites and Asians.
Figure 3. Adjusted Predicted Probability Model for Mortgage Denial Rates in Los Angeles County, 2018 – 2019
Adjusted Predicted Probabilities for Race-Ethnicity Groups by Debt-To-Income Level
Source: HMDA 2018-2019.
Note: 95 percent confidence intervals displayed using ends of whiskers.
Applicants with poor DTI ratios were significantly more likely to be denied than those with excellent DTI ratios. However, there appears to be no substantial difference in the predicted denial rates among those with DTI ratios of less than 45 percent. To illustrate, the adjusted denial rate among Asian applicants with poor DTI ratios (between 40 percent and 45 percent) was about 7 percent, whereas the rate among Asians with excellent to below average DTI ratios (between 37 percent and 40 percent) was only about 6 percent.
Finally, the patterns observed when examining the intersection of race-ethnicity and credit worthiness is slightly different for mortgage denials. The adjusted denial rate for Black and Latino applicants with excellent DTI ratios (less than 36 percent) was about 13 percent, compared to a rate of about 5 percent for whites and Asians with the same DTI ratios.
The large racial and ethnic disparity in access to mortgage credit in Los Angeles County, even after considering varying levels of credit worthiness, is extremely troubling. The lack of access to mortgage loans prevents minorities from homeownership opportunities and wealth accumulation more broadly.
Policy intervention in the mortgage market is necessary to give minorities greater access to homeownership opportunities. Based on the findings presented in this brief, we propose the following policy recommendations:
1. Adjust the factors that are used by financial institutions to weigh the borrower’s economic risk in the mortgage underwriting process.
For instance, an applicant’s rental history is not captured in the credit score or any other part of the mortgage application. A history of consistent rental payments shows that the applicant has prioritized housing costs over time and indicates how they may handle mortgage obligations. Considering the borrower’s rental history is especially important for minorities in Los Angeles County, since many of these households tend to rent for a considerable period as they save money to purchase a home.
2. Expand homeownership programs by local government and nonprofit agencies and to align these programs toward minoritized and communities of color.
Based on the analysis reported here, minorities need additional assistance to overcome the barriers they continue to face in the mortgage market. Programs designed to increase credit scores and credit worthiness will only partially help home seekers of color in Los Angeles. Additional programs and services, such as down payment assistance or shared home equity programs, may have a larger impact in increasing homeownership opportunities for minorities. Lowering costs through this type of intervention will reduce the amount of the loan request and decrease the perceived economic risk of the applicant, thus improving their chances to obtain a low-cost fixed-rate mortgage.
3. Improve data collection under the Home Mortgage Disclosure Act (HMDA) by expanding the current set of socioeconomic indicators collected for and made available in public HMDA datasets.
Currently, public datasets do not include the borrower’s credit score, marital status, or legal status. As a result, housing studies are confined to examining racial and ethnic stratification in the mortgage market rather than investigating the discriminatory practices that account for such disparities.
1 Minority is defined as non-white racial and ethnic groups.
2 Camille Zubrinksy Charles, Won’t You Be My Neighbor? Race, Class, and Residence in Los Angeles (New York: Russell Sage Foundation, 2009); and John Yinger, Closed Doors, Opportunities Lost: The Continuing Costs of Housing Discrimination (New York: Russell Sage Foundation, 1995).
3 United States Census Bureau, “Quarterly Residential Vacancies and Homeownership, Fourth Quarter 2022,” release no. CB23-08, January 26, 2023. CB23-08 reports 2022 data.
4 Jacob W. Faber, “Racial Dynamics of Subprime Mortgage Lending at the Peak,” Housing Policy Debate 23, no. 2 (2013): 328–49, available online; and José Loya and Chenoa Flippen, “The Great Recession and Ethno-Racial Disparities in Access to Mortgage Credit,” Social Problems 68, no. 5 (2020), available online.
5 Gene Amromin and Leslie McGranahan, “The Great Recession and Credit Trends across Income Groups,” American Economic Review 105, no. 5 (2015): 147–53, available online; Jaya Dey and Lariece M. Brown, “The Role of Credit Attributes in Explaining the Homeownership Gap between Whites and Minorities since the Financial Crisis, 2012–2018,” Housing Policy Debate 32, no. 1 (2020): 1–62, available online; and Jacob W. Faber, “Cashing in on Distress: The Expansion of Fringe Financial Institutions During the Great Recession,” Urban Affairs Review 54, no. 4 (2018): 663–96, available online.
6 Marsha J. Courchane and Peter M. Zorn, “Differential Access to and Pricing of Home Mortgages: 2004 through 2009: Pricing of Home Mortgages,” Real Estate Economics 40, no. s1 (2012): S115–58, available online; Douglas S. Massey and Nancy A. Denton, American Apartheid: Segregation and the Making of the Underclass (Cambridge, MA: Harvard University Press, 2001); and Keeanga-Yamahtta Taylor, Race for Profit: How Banks and the Real Estate Industry Undermined Black Homeownership (Chapel Hill: University of North Carolina Press, 2019).
7 Amalia Estenssoro and Micelle Cissi, “Overview of Bank Credit Expansion since the Financial Crisis,” Federal Reserve Bank of St. Louis website, February 2, 2015, available online; Angela C. Lyons, “How Credit Access Has Changed over Time for U.S. Households,” Journal of Consumer Affairs 37, no. 2 (2003): 231–55, available online; and Michael Nau, Rachel E. Dwyer, and Randy Hodson, “Can’t Afford a Baby? Debt and Young Americans,” Research in Social Stratification and Mobility 42 (December 2015): 114–22, available online.
8 Randall Campbell, Brandon Roberts, and Kevin Rogers, “An Evaluation of Lender Redlining in the Allocation of Unsecured Consumer Credit in the US,” Urban Studies 45, nos. 5–6 (2008): 1243–54, available online; Dey and Brown, “Role of Credit Attributes”; Annie Harper, Tommaso Bardelli, and Stacey Barrenger, “‘Let Me Be Bill-Free’: Consumer Debt in the Shadow of Incarceration,” Sociological Perspectives 63, no. 6 (2020): 978–1001, available online; and Douglas S. Massey Jacob S. Rugh, Justin P. Steil, and Len Albright, “Riding the Stagecoach to Hell: A Qualitative Analysis of Racial Discrimination in Mortgage Lending,” City and Community 15, no. 2 (2016): 118–36, available online.
9 Brent W. Ambrose, James N. Conklin, and Luis A. Lopez, “Does Borrower and Broker Race Affect the Cost of Mortgage Credit?,” Review of Financial Studies 34, no. 2 (2021): 790–826, available online; M. Cary Collins, Keith D. Harvey, and Peter J. Nigro, “Mortgage Broker Loan Pricing Leading Up to the Financial Crisis: Were Yield Spread Premiums the Only Problem?” Housing Policy Debate 32, no. 2 (2021): 1–16, available online; Courchane and Zorn, “Differential Access”; and Jacob W. Faber, “Segregation and the Geography of Creditworthiness: Racial Inequality in a Recovered Mortgage Market,” Housing Policy Debate 28, no. 2 (2018): 215–47, available online.
10 Faber, “Racial Dynamics”; Ryan Gabriel, Jacob Rugh, Hannah Spencer, and Aïsha Lehmann, “The Neighborhood Attainment of Mixed-Race Couples across the Black/White Spectrum,” Sociological Perspectives 65, no. 2 (2021): 350–73, available online; Loya and Flippen, “Great Recession”; Alicia Munnell, Geoffrey Tootell, Lynn Browne, and James McEneaney, “Mortgage Lending in Boston: Interpreting HMDA Data,” American Economic Review 86, no. 1 (1996): 25–53, available online; Jacob S. Rugh, “Why Black and Latino Home Ownership Matter to the Color Line and Multiracial Democracy,” Race and Social Problems 12, no. 1 (2020): 57–76, available online; and Jacob S. Rugh, Len Albright, and Douglas S. Massey, “Race, Space, and Cumulative Disadvantage: A Case Study of the Subprime Lending Collapse,” Social Problems 62, no. 2 (2015): 186–218, available online.
11 Munnell et al., “Mortgage Lending.”
12 The figures chart the predicted probabilities and the 95 percent confidence intervals produced from the multivariate model. I estimate a multinomial logistic regression model with robust standard errors predicting loan outcomes (obtaining a conventional loan [reference], obtaining a high-cost loan, and a mortgage denial). The patterns related to income, down payment, loan type, and loan amount all align with previous analyses which identifies lower denials and high-cost lending relative to conventional originations among applicants that have a higher income, higher down payment, and lower loan amount, and among those applying for conventional payment loan products.
We would like to thank Fariba Siddiq, doctoral student at the UCLA Department of Urban Planning, for her assistance with data management. We are also thankful for thoughtful edits from Rebecca Frazier at the UCLA Chicano Studies Research Center and layout and design by Andrea Cannon.
We acknowledge the Gabrielino/Tongva peoples as the traditional land caretakers of Tovaangar (the Los Angeles basin and So. Channel Islands). As a land grant institution, we pay our respects to the Honuukvetam (Ancestors), ‘Ahiihirom (Elders) and ‘eyoohiinkem (our relatives/relations) past, present and emerging.