Homelessness Prediction Model

Dataset/Homelessness Prediction Model

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Homelessness is an acknowledged problem in many places, though its causes are myriad and may vary based on the characteristics of respective communities. This report investigates heterogeneity in community-level rates of sheltered and unsheltered homelessness, separately and combined, and provides insight into underlying community-level factors associated with homelessness across the United States. This study (1) identifies and describes market variables associated with sheltered and unsheltered homelessness, (2) constructs and evaluates empirical models of community-level homelessness, and (3) analyzes relationships within subgroup populations of local markets. Findings provide insights into predicting homelessness across different community types and market factors to consider as policy interventions are developed. The study finds that housing factors, such as rental costs, crowding, and evictions, are most consistently associated with higher rates of community-level homelessness. This demonstrates that housing market dynamics and the availability of affordable housing are closely tied to homelessness at the Continuum of Care (CoC) level even when controlling for a range of economic, demographic, safety net, and climate factors.

Users of the study data are advised to use the documentation provided below and to read the study at the link below to clarify the documentation file.