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This study examines racial inequities and homelessness in the United States through mixed methods research in eight communities. We compare the race and ethnicity of those experiencing homelessness to the general population and to people in poverty, and we also explore how race and ethnicity are associated with housing outcomes. Interviews with 195 individuals of color explore pathways into homelessness and drivers of outcomes. We find that Black/African Americans and Native Americans were the most overrepresented among those experiencing homelessness in each community, and interview data suggest that factors associated with homelessness for people of color include barriers to housing and economic mobility, racism and discrimination within homeless services, and involvement in multiple systems, including criminal justice. How race and ethnicity were associated with outcomes varied for youth, single adults, and families. We argue that researchers and policy-makers need to address homelessness with attention to racial justice.

Homelessness is poorly captured in most administrative data sets making it difficult to understand how, when, and where this population can be better served. This study sought to develop and validate a classification model of homelessness. Our sample included 5,050,639 individuals aged 11 years and older who were included in a linked dataset of administrative records from multiple state-maintained databases in Massachusetts for the period from 2011–2015. We used logistic regression to develop a classification model with 94 predictors and subsequently tested its performance. The model had high specificity (95.4%), moderate sensitivity (77.8%) for predicting known cases of homelessness, and excellent classification properties (area under the receiver operating curve 0.94; balanced accuracy 86.4%). To demonstrate the potential opportunity that exists for using such a modeling approach to target interventions to mitigate the risk of an adverse health outcome, we also estimated the association between model predicted homeless status and fatal opioid overdoses, finding that model predicted homeless status was associated with a nearly 23-fold increase in the risk of fatal opioid overdose. This study provides a novel approach for identifying homelessness using integrated administrative data. The strong performance of our model underscores the potential value of linking data from multiple service systems to improve the identification of housing instability and to assist government in developing programs that seek to improve health and other outcomes for homeless individuals.

People of color are dramatically more likely to become homeless in the USA than are their white counterparts. In particular, black and Native Americans experience disproportionately higher rates of homelessness. While this disproportionality has been well documented for decades, national and local responses to homelessness have been slow to approach the problem with attention to racial equity. Additionally, health disparities, particularly in mental health and substance use disorder treatment, complicate potential exits from homelessness for racial and ethnic minorities. This chapter describes the racial dimensions of homelessness with attention to the historical policy decisions that laid the groundwork for contemporary homelessness among people of color. The authors conclude with recommendations for research, practice, and policy.