Predictive Model Based on Homelessness

Dr. Byrne and Dr. Richard are planning to put together a peer-reviewed article that expands on an existing body of research that examines structural determinants of homelessness across roughly 400 communities that receive federal homeless assistance funding from the U.S. Department of Urban Development. These communities, which are geographical units known as “Continuums of Care” (coc) (see Glossary for more information) have to conduct counts of homelessness on an annual basis. Prior studies have used these data to examine the association between community-level factors such as rent levels, demographic and economic conditions and rates of homelessness. By analyzing and using 2007-2023 data, the goal of this project is to move beyond simply identifying associations and instead develop a predictive model of homelessness at the community level using these same factors. While some studies have been done to predict homelessness at the individual level based on factors like income and location, this project is unique in its focus on the community level. This project will aggregate data from the Department of Housing and Urban Development, along with publicly available data such as rent rates (see the Dataset section for more information), and group them by Continuum of Care (CoC) unit. Dr. Byrne and Dr. Richard have a strong understanding of the predictors needed to build the model. This should be discussed further during the initial client meetings to gain guidance on how to structure the dataset. Following this, regression models or other predictive machine learning models should be developed to predict the number or rate of homelessness by CoC. It is recommended that students have some knowledge of regression models (using the scikit-learn package or similar), familiarity with feature engineering, and experience with the pandas package in Python.