How does BFZ address “race equity” in data?

Understanding and addressing race equity in data requires the intentional collection, analysis, and use of race and ethnicity information. It involves identifying disparities, addressing inequities, and using data to drive decisions that create equitable outcomes. 

• Race Equity: The condition where racial and ethnic identity no longer predicts outcomes, and outcomes for all groups improve.
• Self-Identification: Respecting how individuals see themselves and define their race and ethnicity. 
• Equity-Driven Data Practices: Processes that prioritize fairness and inclusivity at every stage of data collection, analysis, and application. 
• Disaggregating Data: Breaking down data by subcategories — such as race and ethnicity — to reveal patterns that may not be immediately visible as part of a large dataset.

By embedding race equity into data practices, communities will have the tools to investigate their systems and ensure they are designed to address historical inequities, provide equitable access to resources, and produce equitable outcomes.

Why does addressing race equity in your by-name dataset matter?

Addressing race equity is essential to creating a homelessness response system that works for everyone. Racial disparities are pervasive in homelessness rates, access to services, and outcomes, often reflecting broader societal inequities. By focusing on race equity in their data, communities can identify these disparities and implement strategies to eliminate them. By addressing barriers faced by marginalized groups, communities can improve overall system effectiveness. Transparent and respectful practices foster community trust, leading to more complete and accurate data.

Key data to include

A comprehensive by-name dataset should include, at minimum, the self-identified race and ethnicity information for every person in your system, which is often collected as part of the Homeless Management Information System (HMIS) or other intake documentation. It is critical that the race and ethnicity data recorded matches the individuals self-reported information and that the individuals collecting data have adequate training and guidance on how best to collect self-reported race and ethnicity data. HMIS data standard updates in 2024 were expanded to allow for self-identification; any alternative systems should be reviewed to ensure they allow for self-identified data collection.

Additional data to include

Incorporating feedback of people who are receiving or have received services from your homeless response system will help provide a deeper understanding of what aspects of your system may be levers for improvement. This could include gathering qualitative data and insights from people who are part of a racial or ethnic group that are known to be disproportionately experiencing homelessness in your system through surveys, focus groups, and involvement of any lived experience advisory groups.

Key system outcomes to disaggregate

When analyzing by-name datasets, communities may start by disaggregating the following system outcomes by race and ethnicity to identify and address disparities:

• Rates of homelessness inflow and outflow
• Length of time homeless for individuals
• Access to housing placements and service interventions
• Success rates in achieving housing stability
• Self-reported feedback on the experiences of people experiencing homelessness in the system

Disaggregating these outcomes allows your community to start pinpointing where racial disparities exist and implementing targeted solutions. 

Frameworks to evaluate 

As your community examines system outcomes like inflow and outflow rates and housing stability, you can consider a variety of race equity frameworks. For example, targeted universalism — an approach that sets universal goals for all groups (e.g., equal housing placement rates for all) and approaches them with targeted strategies to address and resolve inequities. This might look like:

• Setting universal goals with a common outcome that applies to everyone
• Developing targeted strategies that are tailored to a subpopulation’s’ needs 
• Monitoring outcomes regularly to ensure that interventions are reducing disparities.

Contextualizing and reviewing outcome data

Reviewing your outcome data requires a nuanced approach to understand the broader context:

• Historical context: What are the systemic inequities that contribute to the racial disparities in homelessness?
• Community-specific factors: How do local demographics (such as census data), service availability, and barriers to access impact racial disparities in homelessness locally? 
• Trends over time: Are there persistent disparities that show up consistently over time? Look at month-to-month and year-to-year comparisons. 

Using findings in your data

The insights you gain from your disaggregated and contextualized data should drive meaningful conversations, planning, and action in your community. Consider the following ways of interacting with your findings:

• Develop community-wide strategies to remove barriers and improve access and outcomes for marginalized groups.
• Use findings to inform equitable resource allocation through policy development or policy changes.
• Share outcomes with key stakeholders to build trust and collaboration.

See the tools and resources section for more examples of how to analyze your data with a race equity lens