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FactHub saved analysis

King County 2024 total rate vs. rent, with count-method adjustment

How does King County's 2024 residual change if the estimated effect of its 2022 switch to respondent-driven sampling is removed?

Coverage: 2015-2025 (PIT count years; no ACS 1-year data for 2020)

Node: dataset:homelessness-rate-vs-rent:1:analysis:king-2024-method-adjusted

Contained in Homelessness rate vs. rent in 67 large U.S. Continuums of Care, 2015-2025.

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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

King County 2024 total rate vs. rent, with count-method adjustment

Question: How does King County's 2024 residual change if the estimated effect of its 2022 switch to respondent-driven sampling is removed?

Result: 2024 total, central method adjustment (1,100 people removed): 29.33 (95% PI -27.03 to 85.69); 2024 total, high method adjustment (3,600 people removed): 18.65 (95% PI -37.71 to 75); 2024 total, Major City sample, central adjustment: 9.54 (95% PI -52.44 to 71.51); 2024 total, Major City sample, high adjustment: -1.15 (95% PI -63.12 to 60.83)

Operation: Leave-one-out OLS of homelessness rate on median gross rent; King County residual with 95% prediction interval

Scope: 2024 fits, full sample and Major City sample

Estimates with uncertainty
EstimateValueUncertaintyMethodn
2024 total, central method adjustment (1,100 people removed) 29.33 ± 28.14 per 10,000 residents (95% CI -27.03 to 85.69) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 59
2024 total, high method adjustment (3,600 people removed) 18.65 ± 28.14 per 10,000 residents (95% CI -37.71 to 75) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 59
2024 total, Major City sample, central adjustment 9.54 ± 30.53 per 10,000 residents (95% CI -52.44 to 71.51) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 37
2024 total, Major City sample, high adjustment -1.15 ± 30.53 per 10,000 residents (95% CI -63.12 to 60.83) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 37

View this analysis

  • Rent alone explains little of the variation across large CoCs, so the prediction intervals are wide.
  • King County switched to respondent-driven sampling in 2022; its 2022 and 2024 rates are not directly comparable with visual counts.
  • Cross-sectional association within one year; not a causal estimate.

Robustness check for: King County total homelessness rate vs. rent-predicted rate, all large CoCs

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.

Publications using this dataset

Version details

The exact citing versions, and the version of this dataset each cites.

  • Article seattle-homelessness-spending-results — version 5 (published) cites version 1
  • Article seattle-homelessness-spending-results — version 4 (published) cites version 1
  • Article seattle-homelessness-spending-results — version 3 (published) cites version 1
  • Article seattle-homelessness-spending-results — version 2 (published) cites version 1
  • Dataset homeless-system-performance — version 1 (published) cites version 1
  • Dataset king-county-homeless-count-beds — version 2 (published) cites version 1