Harnessing data to uncover local inequities
December 9, 2024
Image credit: Getty Images
Overview
Data is integral for understanding housing disparities, developing interventions, and monitoring progress toward local goals. By integrating multiple data sources, cities can gain a more comprehensive view of existing inequities and adjust their equity strategies accordingly.
The Housing Needs Assessment Tool provides cities with an accessible and customized report across several housing indicators.
Created in partnership with PolicyMap, the Housing Needs Assessment Tool (HNAT) is the primary data resource offered by the Housing Solutions Lab. The tool leverages data from the U.S. Census to generate specialized reports by city and metropolitan area. Cities can use the tool to understand local housing challenges by assessing trends by race, income, and disability status. Key indicators, such as rent burden and poverty levels, provide a high-level overview of these differences.
Since HNAT creates custom reports for each city, it can be an accessible resource for cities seeking to quickly identify disparities in housing. To capture additional dimensions of these inequities, cities can combine HNAT with other relevant data sources or analytical tools.
The U.S. Census Bureau’s American Community Survey remains the premier data source for examining disparities in housing.
With annual releases of one-year and five-year estimates, the U.S. Census Bureau’s American Community Survey (ACS) provides a periodic and detailed overview of a range of metrics, including the race, disability status, income level, and housing tenure of householders. These highly granular variables permit a finer analysis of specific subgroups. The data are also available at very detailed levels, such as census block groups, making it especially valuable for municipalities aiming to understand local inequities and their spatial concentration. For example, cities can manipulate the data in the ACS to examine differences in homeownership rates by race and income level.
Despite these strengths, it is important to carefully interpret ACS estimates, as its margins of error may be large in some cases. The ACS also does not cover all forms of housing disparities. This is especially true for LGBTQ+ respondents, who are often underrepresented in such surveys due to privacy concerns or the fluidity of their identity, which may make it difficult to select a single label. As a result, the ACS must be paired with additional data sources when crafting a comprehensive equity strategy.
The U.S. Census Bureau’s American Housing Survey provides critical information on physical housing conditions.
Conducted biennially, the U.S. Census Bureau’s American Housing Survey (AHS) is unique in that it includes variables on the physical conditions of homes, including the incidence of roof, heating, and mold problems. The dataset also provides information on the share of homes that are adequate for living, as well as data on asthma and air quality. Each of these variables can be disaggregated by race, income, and disability status, allowing cities to understand disparities and trends between groups. Since Black households are more likely to reside in homes of poor quality, the dataset can help municipalities recognize the need for targeted interventions, like home repair programs, to support these communities. The dataset can also shed light on the accessibility of housing stock for people with disabilities, as well as potential areas for improvement.
Cities interested in leveraging the AHS should be aware that the data is limited in its geographic scope; only 25 metropolitan areas are available in the dataset, 15 of which are the largest by population. As a result, small cities may be unable to draw detailed insights from the dataset. The biennial nature of the survey may also pose challenges for identifying sudden and unequal shifts in housing quality, especially in the case of natural disasters.
The U.S. Census Bureau’s Household Pulse Survey offers a snapshot of recent housing trends.
Collected and disseminated several times a year, the U.S. Census Bureau’s Household Pulse Survey (HPS) fills some of these gaps by capturing emerging housing trends across the country. To do so, the survey measures housing insecurity, displacement pressures, and the likelihood of eviction, among other variables. These variables can be disaggregated by race, income level, and – notably – sexual orientation and gender identity. Cities may find this data especially useful in identifying disparities in housing outcomes for LGBTQ+ residents, who are more likely to experience housing insecurity or homelessness.
Like the AHS, the HPS is limited in scope: the data is restricted to 15 metropolitan areas, which may limit its applicability for small cities. The dataset is also based on a smaller sample size than other surveys, and therefore may be less precise in some cases.
Cities can also connect with federal and local housing agencies for specialized data resources.
Public housing authorities are critical partners in this regard, as many of them collect detailed data on race, income level, and disability status. Cities can use this data to identify disparities within their public housing stock and voucher programs. The U.S. Department of Housing and Urban Development (HUD) provides an aggregated version of this programmatic data via an online tool known as the Picture of Subsidized Households. However, accessing more detailed data by race, income, and disability status may require HUD approval via data use agreements.
Local housing agencies often collect the most detailed data on permitting and housing code enforcement. Cities can leverage this data in tandem with other tools to identify neighborhoods with diverging patterns of development or housing quality, thereby shedding light on potential disparities. Data-sharing agreements can formalize these relationships and encourage collaboration and information exchange across agencies committed to addressing housing disparities.
Cities can incorporate data from other sectors, including health and climate, to enrich their understanding of a complex housing landscape.
Housing disparities do not exist in isolation. Similarly, housing data in isolation may not provide cities with a comprehensive look at the factors driving disparities. Cross-sector data integration can help cities better understand how health outcomes, environmental risks, and access to transportation interact with housing.
The ACS and HPS include information about food assistance, commute times, and other topics of interest across different sectors. In addition, there are specialized resources that more directly connect these sectors. For example, HUD’s Housing and Health Dashboard aggregates several data sources that examine both health and housing outcomes, such as the National Health and Nutrition Examination Survey (NHANES). Additionally, the Environmental Protection Agency (EPA) provides a detailed Environmental Justice Screening and Mapping tool, which practitioners can use to directly compare the prevalence of environmental toxins with socioeconomic characteristics and health outcomes. By integrating these data sources into an analysis of housing disparities, cities can gain a more nuanced understanding of local issues and develop more targeted interventions.
Data in action: Oakland, CA
Oakland, CA, set a data-driven standard for how the city considers equity in policymaking by tracking differences in outcomes among social groups across 72 distinct indicators. Using the equity indicators, the city was able to identify high-impact strategies to grow and protect the local housing stock, including implementing a rental registry and developing permanently affordable housing on city-owned land.
Read more about Oakland’s work on Local Housing Solutions: How the City of Oakland Is Using a Data-Driven Approach to Address Racial Equity.
For more information on using data to inform housing policy, explore our Data Talks series.