Homeownership Gap
About
The Homeownership Gap measures the ratio between the homeownership rate of Black and White individuals in a county. Individuals from both Hispanic and non-Hispanic ethnicities are included. In 2020, the homeownership rate for Black households was 43.4% while the homeownership rate for White households was 72.1%, a staggering difference of 31 points 1. The homeownership gap contributes significantly to the larger Black and White wealth gap, as home equity is a major source of wealth.
Why is the Homeownership Gap important to the Structural Racism and Discrimination (SRD) Index?
The Black–White homeownership gap in the US remains persistently large—typically 20–30 percentage points—and is only partially explained by differences in income, education, and credit, with a substantial share attributable to structural and institutional racism such as historic redlining, mortgage discrimination, and residential segregation 2-7. Causal evidence shows that redlining policies from the 1930s–1940s led to enduring declines in home values, homeownership rates, and neighborhood advantages for Black households, effects that are still measurable over half a century later 7,8. Moreover, Audit, the Home Mortgage Disclosure Act (HMDA), and credit-panel analyses show that Black applicants face higher denial rates and are disproportionately steered into costlier or subprime loan products, reducing both access to ownership and the intergenerational wealth benefit of owning 2,9,10. Segregation remains a strong contextual driver of the homeownership gap, operating via differential access to credit, schools, and housing appreciation 3,5. Addressing this inequity is essential to reduce racial disparities in housing access and ensure fair financial opportunities for all residents.
What is the expected relation to Structural Racism and Discrimination?
A higher value of the Homeownership Gap between Black and White applicants contribute to the higher value or score of the SRD Index.
How is the Homeownership Gap calculated?
Data Source
We obtained data from the IPUMS National Historical Geographic Information System (NHGIS) 11. The data is publicly available.
Data
We used the following four variables at the county level.
| Variables* | Year | Unit |
|---|---|---|
| Total occupied housing units with householder who is Black or African American alone | 1990 | 2000 | 2010 | 2020 | Number |
| Total occupied housing units with householder who is White alone | 1990 | 2000 | 2010 | 2020 | Number |
| Owner occupied housing units with householder who is Black or African American alone | 1990 | 2000 | 2010 | 2020 | Number |
| Owner occupied housing units with householder who is White alone | 1990 | 2000 | 2010 | 2020 | Number |
* Individuals from both Hispanic and non-Hispanic ethnicities are included.
Methodology
We calculated the Homeownership Gap using a ratio formula:
$$
RPrBlWhHO = \frac{PrBlHO}{PrWhHO}
$$
Where:
RPrBlWhHO: Ratio of proportion of Black and White owner-occupied housing units
PrBlHO = BlHO/BlOHU: Proportion of Black owner-occupied housing units to total occupied housing units by Black householder
PWhHO = WhHO/WhOHU: Proportion of White owner-occupied housing units to total occupied housing units by White householder
Missing Data
We replaced missing values (where applicable) by using functions that were based on Tobler’s First Law of Geography, i.e., “everything is related to everything else, but near things are more related than distant things” 12. This law also informs the concept of Spatial Autocorrelation (SA). The variables were highly spatially autocorrelated and the missing value in a county was imputed using the median value of the eight nearest neighbors or counties. The eight nearest neighbors were identified using the KDTree algorithm in Python’s scipy.spatial module 13. After imputation of missing data, we have 13 counties with no data in 2020, and none for the 2010, 2000, and 1990 years.
References
1. Brandi Snowden and Nadia Evangelou, “Racial Disparities in Homeownership Rates,” National Association of Realtors, March 3, 2022, https://www.nar.realtor/blogs/economists-outlook/ racial-disparities-in-homeownership-rates.
2. Ross, S. L., & Yinger, J. (2002). The color of credit: Mortgage discrimination, research methodology, and fair-lending enforcement. MIT press.
3. Freeman, L. (2005). Black homeownership: The role of temporal changes and residential segregation at the end of the 20th century. Social Science Quarterly, 86(2), 403-426.
4. Wachter, S. M., & Megbolugbe, I. F. (1992). Impacts of housing and mortgage market discrimination racial and ethnic disparities in homeownership. Housing Policy Debate, 3(2), 332-370.
5. Flippen, C. A. (2001). Residential segregation and minority home ownership. Social science research, 30(3), 337-362.
6. Myers Jr, S. L., & Chung, C. (1996). Racial differences in home ownership and home equity among preretirement-aged households. The Gerontologist, 36(3), 350-360.
7. Appel, I., & Nickerson, J. (2016). Pockets of poverty: The long-term effects of redlining. Available at SSRN 2852856.
8. Gordon, C., & Bruch, S. K. (2020). Home inequity: race, wealth, and housing in St. Louis since 1940. Housing Studies, 35(7), 1285-1308.
9. Ross, S. L., & Yinger, J. (2002). Looking the Other Way: A Critique of the Fair-Lending Enforcement System and a Plan to Fix It.
10. Dey, J., & Brown, L. M. (2022). The role of credit attributes in explaining the homeownership gap between whites and minorities since the financial crisis, 2012–2018. Housing Policy Debate, 32(2), 275-336.
11. Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2024). IPUMS National Historical Geographic Information System: Version 19.0 [Dataset]. IPUMS.
12. Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(sup1), 234–240.
13. SciPy Developers. (n.d.). scipy.spatial.KDTree. SciPy v1.10.1 Manual. Retrieved November 19, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html