Using quantitative panel data from HUD and the American Community Survey, as well as other public sources, this symposium uses predictive machine learning and regression methods to explore causal relationships in homelessness. Matching between spatial areas of CoCs and other census areas is made possible through previous GIS analysis from Byrne and others.
The three papers call out distinct regional issues for social work to act on when addressing structural mechanisms to prevent and end homelessness. The first paper (Byrne & Richard) uses predictive modeling techniques to forecast rates of homelessness at the community level based on housing and economic factors. Building on this, the second paper (Richard & Byrne) identifies racial inequities in homelessness, drafting a typology of communities with respect to their disproportionate black-white racial impacts and proposing mechanisms to address this health equity issue. The final paper (Leary) repositions the Low-Income Housing Tax Credit as a modern tool to address homelessness through permanent supportive housing, suggesting the need to streamline funding to the community level. This body of work establishes a need to strengthen and clarify our regional approach in addressing homelessness.
These regional trends in homelessness and modern policy interventions have important implications for practice. Social workers are better suited to engage with vulnerable populations if they understand the regional determinants of homelessness and the funding mechanisms available for solutions. As advocates across macro-mezzo-micro levels of organization, social work has a calling to be more than a bandaid to homelessness, and to identify determinants for prevention and policy tools for solutions. These three papers capture how the regional level of homelessness, the Continuum of Care, can be best situated for macro social work intervention.
![[ Visit Client Website ]](images/banner.gif)