How Federal Agencies Can Use Data to Promote Fair Housing
Historical and current housing data is a critical tool for advancing racial equity.
Housing inequity has made it more difficult for people of color, especially Black Americans, to own their own homes – and the problem is only getting worse. In 1960, White Americans had homeownership rates 26% higher than Black Americans. That gap has grown to 29.4%. Furthermore, redlining continues to affect financial opportunities and community services in Black and Brown communities with implications for financial, physical, and emotional health and well-being.
Housing adequacy and affordability are complex, vary by locality, and are often dependent on state and local policy decisions, economic conditions, availability of federal housing assistance, and historical and ongoing discriminatory housing practices. Steering, redlining, and mortgage lending discrimination are all discriminatory housing practices that impact people of color in the U.S. disproportionately.
While programs to fight housing discrimination have been in place for decades, federal agencies can do more. An issue brief from the nonprofit Center for Open Data Enterprise recently published by the IBM Center for the Business of Government, “Leveraging Data to Improve Racial Equity in Fair Housing,” shows that agencies can use data and analytics in new ways to promote fair housing and help close the racial homeownership gap. Historical and current housing data is a critical tool for advancing racial equity by mapping the status of housing disparities, providing use cases for data analysis, and identifying challenges to be addressed.
Data is now available thanks to the Fair Housing Act, administered by the Department of Housing and Urban Development, and the Home Mortgage Disclosure Act, administered by the Consumer Financial Protection Bureau, which requires financial institutions to maintain, report, and publicly disclose loan-level information on mortgages.
Federal agencies can build on these protections and collaborate with nonprofits like the Black Wealth Data Center, the private sector, and community-based partners to identify and apply new sources of data as well. The report from the IBM Center and CODE describes data opportunities in five areas.
1. Reducing mortgage lending discrimination. Mortgage lending discrimination is a direct result of redlining and related HOLC maps. It occurs when lenders base credit decisions on factors other than the applicant’s creditworthiness, such as race or gender. Local lending outcomes and practices are also critical factors when assessing fair housing and access to housing opportunities in a community. Demographic data and information on lending practices can help analyze homeownership and lending access disparities according to demographics.
2. Increasing homeownership and affordability. Consistent home ownership is a foundation for wealth building. However, Black homeownership rates have continued to lag substantially in comparison to all other groups in the United States. Local variations in affordability can be a significant cause of segregation, influencing some protected classes’ accessibility to certain neighborhoods.
Housing costs can be analyzed alongside income levels to measure housing affordability, relative to household income.
3. Addressing negative effects on well-being and health. Studies have shown statistically significant correlations between historical redlining and present-day adverse health and socioeconomic outcomes at the census tract levels (i.e. declines in intergenerational economic mobility, lower life expectancy). The Centers for Disease Control and Prevention has developed an initiative to improve public health through data-driven approaches, such as using public health surveillance data to improve community design decisions through Health Impact Assessments and other tools.
4. Improving socioeconomic outcomes. Poor economic standing is associated with limited health care access, low insurance rates, postponing needed care, and higher hospitalization rates — issues exacerbated by low-quality housing. Effective interventions, like the government’s success in radically curbing lead poisoning between 1976 and 2002, can reduce environmental health disparities related to housing. Interventions at the local, state, and federal level can help reduce these health and safety risks and promote health. Localized socioeconomic status data and tools like social vulnerability indices can help identify and target new interventions.
5. Acting on housing insecurity during COVID-19. A number of social and environmental factors — such as neighborhoods with high levels of pollution or located in food deserts — have contributed to the higher rates of COVID hospitalization and death among Black and Latino Americans. In addition, structural and institutional effects of residential housing segregation have made it more likely for BIPOC people to live in densely populated areas that can make it easier for COVID to spread. Government data on household density and stability is critical to developing local housing response plans to curb the pandemic’s negative impacts.
The report also includes specific recommendations for the federal government to use high-quality, detailed, accessible data in the interest of housing equity. These include:
Data-driven strategies: Developing actionable strategies to improve and apply Federal housing data sources
Data inventories: Creating inventories of high-priority, high-impact datasets and metrics through public-private collaboration
Data standards: Using Federal guidance and public-private collaboration to create standards and data-sharing protocols for housing data
Metrics and indicators: Proposing metrics to evaluate existing housing policies based on open data
Case studies: Collecting and sharing examples of data analyses that have helped uncover racial discrimination and identify poor housing stock, and the use of data for policy reform
Discriminatory housing practices and policies have systemically disenfranchised communities of color, resulting in lower homeownership rates, higher eviction rates, discriminative lending, and other poor outcomes. As federal agencies work to correct these historical inequities, high-quality, accessible Federal and local data will be needed to ensure their success.
Temilola Afolabi is the Center for Open Data Enterprise’s senior research associate and lead’s their Open Data for Racial Equity Program. She can be reached at firstname.lastname@example.org.