View my CV: 2025 CV LaKeta Kemp
Examining the correlation between rent increases, population density, and metropolitan status in selected US cities to predict the rates of unhoused people and families.
In the last ten years, housing costs in the United States have climbed upward while salaries have remained the same or increased slowly (Kemeny and Storker, 2024). The result has been voluntarily pricing communities out of their home or through eviction. Our research aims to uncover if there is a statistical relationship between the rate of increase in rents compared to the rates of unhoused people and families in those communities. A brief review of the data science process is described in relation to the Brookings Institute. Homelessness trends, types, and service delivery ecosystems vary considerably across US cities (Hadden Loh and Love, 2024). The U.S. Department of Housing and Urban Development (HUD) annually estimates Fair Market Rents (FMRs), providing the maximum allowable rental prices for defined metropolitan areas, some HUD-defined subdivisions, and each non-metropolitan county. The dataset guides the determination of payment standard amounts for the Housing Choice Voucher. This federal program assists low-income families to obtain safe, affordable housing in the private market. These values are analyzed through the data science process to establish the minimum affordable rental rates by selected cities in the United States. (HUD Housing Analysis)