Housing Placement Rates: Impact of 2025 Policy Changes

DEMONSTRATION PROJECT - FICTIONAL DATA

SYNTHETIC DATA SET

Evaluation Question

Did the 2025 housing policy changes affect client housing success?

Results

Housing placement rates fell sharply after 2025, independent of program type and client population served.

Statistical modeling indicates an average 19.7% percentage point drop in placement rates from before the policy changes to after they took effect. This is consistent with structural, system-level change rather than a program or population specific effect.

  • Test performed: Multiple linear regression

    R² - 0.978
    Adjusted R² - 0.977
    Standard error - 0.0450
    F-statistic - 1551.4***
    Observations - 432

    The model accounts for 97.8% of the variance in placement rates, and the F-statistic (p < 0.001) indicates the model is highly statistically significant as a whole.

    Built on a synthetic dataset designed to illustrate methodology, not observed outcomes from a real Continuum of Care.

  • Transitional Housing - coefficient 0.240

    Rapid Re-Housing - coefficient 0.484

    Permanent Supportive Housing - coefficient 0.799

    Prevention - coefficient 0.563

    Capacity utilization - coefficient 0.155 (p = 0.001)

    Post-2025 policy period - coefficient −0.197

  • Item descriptionThis analysis uses a synthetic dataset to demonstrate the methodology rather than represent real-world outcomes. A live analysis would likely differ in several ways:

    • Model fit - An R² this high would be unusual in real world CoC data. Placement outcomes are influenced by factors such as staffing, client circumstances, and local referral pathways that aren't fully captured in the model.

    • Data quality - This analysis assumes clean and complete records. Real HMIS data often includes missing fields, inconsistent data entry, and differences in how agencies document services.

    • Confounding factors - The synthetic data treats the 2025 policy change as the primary driver of the outcome. In practice, changes in funding, staffing, economic conditions, and other factors may occur at the same time, making it harder to isolate the effect of a single policy change.

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2. Standard Operating Procedures