A System Dynamics Approach on Modeling Homeless Prevention Strategy: A Case Study of LA County

Abstract

This article presents a system dynamic modeling approach to simulate the effect of a homeless prevention strategy on the homeless population in Los Angeles. Despite the implementation of rehousing strategy suggested by policy makers, the Los Angeles homeless population has increased over time. Traditional statistics analysis is widely used in researching this topic, but using aggregated data fails to provide sufficient explanations on the correlation between the permanent supportive housing and homeless population. Our system dynamics model overcomes this challenge in a unique way using stocks and flows. We model stocks as key factors that have significant impact on homelessness, including prevented homeless population, the population of the homeless who are in the temporary housing programs, and the population of those who are settled in the permanent supportive housing program. Flows provide details on how stocks are related to each other, allowing memories of the history and interconnection in the homeless system. Each stock may affect the other due to time delays and feedback loops through inflows and outflows. To assess the impact of homeless prevention programs, we perform simulation and scenario analysis by adjusting model inputs including ratios for prevented homelessness and the rapid re-housing. The system dynamics model helps unveil the unintended consequence introduced by the Housing-First policy and allows us to evaluate various policies to come up with data-driven recommendations. The simulation results suggest that prevention strategy could lead to a positive impact on reducing the homeless population. Indeed, the use of Housing-First policy along with a preventative program for homelessness could be considered as a more effective strategy for the mitigation of LA homelessness.