Real-Time Predictive Analytics for Early Homelessness Prevention: A Machine Learning Approach

Abstract

Homelessness is a complex and persistent societal issue, often exacerbated by economic instability, housing shortages, and systemic inequities. Existing strategies primarily rely on reactive interventions, which, while essential, fail to provide proactive solutions for prevention. 25 Oct 2025 (Published Online) This study presents a novel machine learning-based framework for early homelessness Homelessness, machine learning, world dataset, we compare the predictive performance of two machine learning models, Random XGBoost, Random Forest. Forest and XG Boost, to assess their effectiveness in identifying high-risk populations. The results demonstrate that the Random Forest model consistently outperforms XG Boost, achieving a lower Mean Absolute Error (MAE) of 12.46, a lower Mean Squared Error (MSE) of 44,534.73, and a higher R² score of 0.996, indicating a superior fit. Feature importance analysis reveals that total homeless counts (pit_tot_hless_pit_hud) and individual homelessness rates are the most critical predictive factors, while economic conditions and housing market pressures also play significant roles. Furthermore, residual analysis and error distribution comparisons illustrate that the Random Forest model maintains a more stable and consistent predictive capability across different demographic and geographic groups. Our research stands apart by integrating a high- dimensional, multi-source dataset to enhance predictive accuracy while addressing ethical considerations such as bias mitigation and fairness in algorithmic decision-making. The findings suggest that machine learning-driven approaches can be pivotal in resource allocation and policy- making, enabling governments and social organizations to proactively intervene before individuals and families fall into homelessness. This study contributes to the growing body of literature advocating for data-driven, predictive solutions in social welfare, demonstrating the tangible impact of machine learning in tackling one of society’s most pressing issues.