Real-Time Predictive Analytics for Early Homelessness Prevention: A Machine Learning Approach
Bibliographic record
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. This study presents a novel machine learning-based framework for early homelessness prediction, integrating key socioeconomic, housing, and public health indicators. Utilizing a real-world dataset, we compare the predictive performance of two machine learning models—Random Forest and XGBoost—to assess their effectiveness in identifying high-risk populations. The results demonstrate that the Random Forest model consistently outperforms XGBoost, 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, residuals 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".