Alameda County HCD Model Explorer
An interactive structural-regression simulator applying Colburn & Aldern (2022) to Alameda County — with 2024 HUD Point-in-Time (PIT) data, geographic comparables, and county-calibrated confidence intervals.
This interactive dashboard is produced by Alameda County Housing and Community Development (HCD) and is inspired by the structural regression framework developed by Gregg Colburn and Clayton Page Aldern in Homelessness Is a Housing Problem (University of California Press, 2022). The regressions, visualizations, and data have been recreated and updated independently by Alameda County HCD using 2024 HUD Point-in-Time (PIT) count data and current American Community Survey (ACS) housing estimates. This work is reproduced here for public education purposes with attribution and discussion, but without direct consultation or review by the original authors. Readers are encouraged to consult the book directly for the full methodology and findings.
The book's central finding, confirmed across decades of Continuum of Care (CoC)-level data, is that rent levels and rental vacancy rates explain the overwhelming majority of variation in homelessness rates between cities, while poverty, mental illness, drug use, unemployment, and climate explain virtually none of it once housing market conditions are controlled for.
The model is not a claim that social vulnerability does not matter to individuals. It is a claim that those vulnerabilities are expressed more severely where housing markets are tightest. A person with a substance use disorder is far more likely to lose their housing in San Francisco (vacancy 3%, median rent $2,700) than in Indianapolis (vacancy 8%, median rent $960), not because of any difference in their individual circumstances, but because the housing market provides no buffer. Alameda County HCD has used this framework to design and fund housing production programs, including the Scalable Housing Infill Funding Toolkit (SHIFT) missing middle initiative and a senior ADU program, oriented toward expanding supply elasticity and moderating rent growth at scale.
Core model
The range ±1.645 × SEmean answers "where would the average CoC with these housing conditions land?" — not where any single CoC falls. A full 90% prediction interval (±1.645 × RMSE) is 4–7× wider and encompasses individual CoC scatter. The CI is the right frame for a structural policy argument; the PI is the right frame for asking whether Alameda's count is structurally explained.
The city model's intercept (α=3.39) and rent coefficient (β₁=1.76) were estimated on 50 city-level CoCs. Cities concentrate services and visible street homelessness, producing structurally higher per-capita rates than county-level CoCs for the same rent level. Applying city coefficients to a county CoC introduces upward bias of roughly 20–25%.
County-level regression (α=3.33, β₁=1.45, β₂=−0.08) gives a point estimate of ~9,490 at Alameda's default inputs — within 40 people of the actual 9,450. The 90% CI spans ~7,860–11,450, containing the actual count. Toggle "County OLS" above to apply this calibration throughout.
Structural variables SIGNIFICANT
Model output — Alameda County
Rent vs. homelessness rate — PIT / ACS
Geographic distribution of homelessness rates