Dashboard
Homelessness rates by Continuum of Care, 2007–2024
Per-CoC and per-state homelessness rates over 18 years. US choropleth map, sortable tables, and rate-format toggles (per 10k, percent, 1 in N).
Open dashboard →Independent Research
Data analysis on housing and homelessness in the United States.
Gaither Research builds interactive dashboards and analysis from HUD's homelessness data. We use the Continuum of Care framework HUD reports on, so our numbers reconcile against the published federal totals.
U.S. homelessness rate, 2007–2024
Per 10,000 residents, from HUD Point-in-Time counts. The 2021 dip reflects HUD's COVID waiver (many CoCs skipped the unsheltered count). 2024 is the highest rate in the 18-year series.
Total Sheltered Unsheltered
Featured analyses
Analysis
What drives homelessness rates? Correlations across 17 covariates
Tests housing market conditions, eviction rates, federal funding, and labor market indicators against the CoC homelessness rate. Housing-market variables dominate; unemployment is essentially uncorrelated.
Open analysis →Methodology
Data quality audit: which CoC rates can we trust?
Per-CoC undercount risk score combining sheltered share, rural designation, year-over-year volatility, and federal funding. Flags 29 CoCs whose reported rates likely understate reality.
Open audit →Geographic decomposition
City effect on state-level homelessness rates
For each state, compares Major City CoCs vs. all other CoCs combined. Surfaces which state-level rates are dominated by their major cities, and which are not.
Open dashboard →Geographic decomposition
Rate by density: urban / suburban / rural breakdown
Per-state homelessness rates broken into Major City, Other Urban, Suburban, and Rural CoCs. National pattern: Major City CoCs run ~4× higher than rural, but state-level patterns vary.
Open dashboard →Coming soon
More analyses in development
Planned: climate-vs-homelessness rates, CoC funding-per-capita comparisons, and year-over-year volatility deep dives. Have a question or dataset to suggest?
How this research is different
We document data quality
Every CoC in our analyses carries a data-quality tier. Communities that count well can appear to have higher homelessness rates than communities that count poorly. We flag this explicitly so readers can tell real differences from measurement artifacts.
Reproducible from public sources
Our datasets are HUD's PIT counts, HUD's CoC funding awards, HUD's Housing Inventory Counts, and the UCSF BHHI CoC Data project. All are publicly available. Source files and processing scripts are tracked in git.
Rates expressed multiple ways
Rates can be shown per 10,000 residents, as a percentage, or as "1 in N." Counts can be sliced by sheltered, unsheltered, or total. Geographic decomposition shows whether a state's rate is driven by its major cities or distributed across the whole state.