King County total homelessness rate vs. rent-predicted rate, all large CoCs

What this dataset measures

Large urban CoCs whose boundaries can be rebuilt from whole Census counties or places, so each homeless count can be matched to the population and rents of the same area. Built to test the common explanation that high housing costs account for Seattle/King County's homelessness.

Geography

United States, Washington, King County, WA, Seattle, WA

Unit of observation

Continuum of Care (CoC) by PIT count year

Update cadence

Annually, after HUD releases each year's PIT counts by CoC

Curator

FactHub

Supporting files

Source snapshots, manifests, quality reports, documentation, and other materials used to produce or review this release.

• build_and_analyze.js Transformation manifest · text/javascript · 11.8 KB (12,057 bytes)

  Node.js script that builds coc-panel from the HUD PIT sheets and ACS JSON and fits the per-year regressions.

• xlsb_to_csv.ps1 Transformation manifest · text/x-powershell · 977 bytes

  PowerShell script that exports each sheet of the HUD .xlsb workbook to CSV through Excel with macros disabled.

• build_rate_rent.js Transformation manifest · text/javascript · 8.2 KB (8,372 bytes)

  Node.js script that computes king-residuals and the saved analyses (leave-one-out fits, prediction intervals, rent-growth correlations).

• peers.js Transformation manifest · text/javascript · 10.2 KB (10,432 bytes)

  Node.js script that builds peer-changes (z-scored distances, 2019-to-latest changes, rent growth).

• findings.md Documentation · text/markdown · 14 KB (14,292 bytes)

  Method notes, results tables and caveats for the panel and regressions.

• findings-peers.md Documentation · text/markdown · 16.5 KB (16,944 bytes)

  Method notes, results and caveats for the peer comparison, including peers' count-method changes.

Saved analyses

Sources

1. HUD, 2007-2025 Point-in-Time Counts by CoC (AHAR Part 1 data) Homeless counts (total, sheltered, unsheltered) and count type for each CoC and year. Downloaded from the Internet Archive snapshot because huduser.gov blocks scripted requests; contentDigest is the SHA-256 of that downloaded workbook (8,859,426 bytes).

   Retrieved October 10, 2026. U.S. government work (public domain). Public domain; not rehosted here. Retrieve from HUD or the archived copy.

   Archived copy captured May 30, 2026

2. U.S. Census Bureau, American Community Survey 1-year estimates, tables B01003, B25064, B25004, B25003 (2015-2019, 2021-2024) Population, median gross rent and rental vacancy for the counties and places that make up each CoC.

   Retrieved October 10, 2026. U.S. government work (public domain). Public domain; values used are in coc-panel.

   Archived copy captured May 21, 2026

3. KCRHA, 2024 Point-in-Time Count FAQ v2 Documents King County's respondent-driven sampling (RDS) method from 2022 and the 2024 revision to 16,868.

   Retrieved October 10, 2026. Public agency document. Linked, not rehosted.

   Archived copy captured October 10, 2026

4. Almquist ZW et al., "Innovating a community-driven enumeration and needs assessment of people experiencing homelessness," American Journal of Epidemiology 194(6), 2024 Only quantitative comparison of King County's 2022 RDS estimate with a forecast of the old visual-count method; basis for the method-adjustment sensitivity range.

   Retrieved October 10, 2026. Open-access journal article. Linked, not rehosted.

   Archived copy captured October 10, 2026

Limitations and reproducibility

Reproducible from public sources: Download the HUD workbook and ACS tables listed in sources, export the workbook sheets with xlsb-export-script, then run build-panel-script, peers-script and residuals-script (Node.js). Outputs match the tables here.

• PIT counts are single January nights; methods vary by CoC and change over time.
• King County changed its unsheltered count method in 2022 (respondent-driven sampling). The estimated effect ranges from 0 to about 3,600 people in 2024.
• Rates per 10,000 are FactHub calculations. HUD does not publish CoC-level rates.
• CoC boundaries are rebuilt from whole Census geographies with small gaps (see coc-geography).
• Multi-county median rent is a renter-weighted average of county medians, not a true median.
• Rent alone explains 2-14% of the variation in total rates across all 67 CoCs (24-45% among Major City CoCs).
• All results are associations, not causal estimates.