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Ethno-racial and Down Payment Disparities in Mortgage Credit Access

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Abstract

Access to homeownership is central to both wealth and ethno-racial stratification. Previous research demonstrates ethno-racial inequality in homeownership such as unequal treatment, steering, and the type of mortgage products offered to both minorities and communities of color alike. However, it is unclear how differences in down payment levels shape ethno-racial disparities in mortgage credit access. This paper draws on annual data from the Home Mortgage Disclosure Act (HMDA) from 2018 to 2019 to assess ethno-racial disparities in mortgage outcomes across varying down payment levels. I demonstrate that black and Latino borrowers are more likely to obtain a high-cost loan and be denied a mortgage across varying down payment levels compared to white applicants. The results for Asians are mixed. These trends are particularly true when examining mortgage denials as black and Latino applicants with a down payment greater than 20% of the home value are just as likely to be denied a mortgage as white applicants with a down payment that is less than 5% of the home value. Asians with the highest down payment level perform similarly as whites with a below average down payment level. Implications for ethno-racial stratification are discussed.

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Notes

  1. In addition to FHA and VA home loans, there are additional loan programs, such as the Home Possible and Home Ready Program, that are designed to assist low-income households access a mortgage by lowering typical down payment and credit requirements. However, it not possible in HMDA to differentiate among borrowers who may have participated in one of these specialized programs.

  2. Approximately, 16% of the original HMDA dataset (2018–2019) contains missing data. The missing data include household income (1% missing), gender (7% missing), race and ethnicity (6% missing), minority population in the neighborhood (1% missing), and region of the property (1% missing).

  3. Missing data of HMDA appear to be random and there does not appear to be a relationship between the missing data and neighborhoods or other spatial characteristics. Because of the large percentage of complete observations and that the randomness of the missing data, there was no added benefit of using multiple imputation to conduct the analysis.

  4. The 2020 HMDA dataset was recently released to the public, but the CFPB continues to update and edit the dataset weekly. Thus, the 2020 HMDA dataset is not complete.

  5. The HMDA dataset contains the reason for denial. The reasons for denial include high debt-to-income ratio, poor unemployment history, poor credit history, insufficient collateral, insufficient cash, and other reasons. I separated the reasons for denial and found no substantive differences in the multivariate analysis as compared to the results shown.

  6. I separated Latinos by race and found no substantive differences in the results produced in the multivariate analysis.

  7. The down payment percent is calculated as the difference between the value of the home and loan amount, divided by the home value.

  8. About 1% of counties contained missing unemployment information.

  9. Less than 2% of MSA’s contained missing house price index information.

  10. The top 100 MSA’s are defined by the population size from the 2010 Census.

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  1. Department of Urban Planning, University of California at Los Angeles, Mail Box: 5261 Public Affairs, Los Angeles, USA

    José Loya

  2. Luskin School of Public Affairs, University of California at Los Angeles, Mail Box: 5261 Public Affairs, Los Angeles, USA

    José Loya

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Appendices

Appendix 1

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Table 2 Mortgage loan outcomes by race and ethnicity and down payment percent; 2018–2019
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Appendix 2

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Table 3 Odds ratios from multinomial logistic regression of loan outcomes from 2018 to 2019: (a) high-cost origination (Ref = Conventional Origination), (b) mortgage denial (Ref = Conventional Origination)
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Loya, J. Ethno-racial and Down Payment Disparities in Mortgage Credit Access. Race Soc Probl 15, 376–394 (2023). https://doi.org/10.1007/s12552-022-09378-z

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