FactHub

FactHub curated dataset

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

HUD Point-in-Time homeless counts for 67 large urban Continuums of Care joined to Census rent and population data, with tests of how much rent levels and rent growth explain, and where Seattle/King County falls.

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

Node: dataset:homelessness-rate-vs-rent:1

Send feedback on this dataset

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

Curated data tables

These related tables form this versioned dataset release. Each table has its own downloadable file and field definitions.

CoC homelessness and rent panel

HUD PIT counts for 67 large urban CoCs joined to ACS population, rent and vacancy for the same year, with per-year rent-only regression fits.

Unit of observation: CoC-year (PIT count year)

View this table

coc_homelessness_rent_panel.csv text/csv · 138.2 KB (141,479 bytes)
23 field definitions
FieldTypeMeaningUnitsValue type
coc_code string HUD Continuum of Care code — observed
coc_name string CoC name as HUD listed it that year — observed
short_name string Short display name — derived
hud_category_2025 string HUD 2025 CoC category, such as "Major City CoC" — observed
year integer PIT count year (counts are taken in January) — observed
count_type string HUD count type for that year — observed
sheltered_only_count integer 1 if that year was a sheltered-only count (HUD carries unsheltered forward, or records 0 in 2021); 0 otherwise — derived
total integer Total people experiencing homelessness on the count night people observed
sheltered integer People in emergency shelter, transitional housing or safe havens people observed
unsheltered integer People unsheltered on the count night (carried forward by HUD in sheltered-only years) people observed
acs_year integer ACS vintage used. 2025 rows use 2024; blank for 2020 (no standard ACS 1-year release) — derived
population integer Resident population of the CoC's counties/places (ACS B01003) people derived
rate_per_10k number Total homeless per 10,000 residents per 10,000 residents derived
sheltered_rate number Sheltered homeless per 10,000 residents per 10,000 residents derived
unsheltered_rate number Unsheltered homeless per 10,000 residents per 10,000 residents derived
median_rent number Median gross rent (ACS B25064). Multi-county CoCs: mean of county medians weighted by renter-occupied households US dollars per month, nominal derived
vacancy number Rental vacancy rate = B25004_002 / (B25003_003 + B25004_002 + B25004_003); blank where suppressed percent derived
predicted_rate number Total rate predicted by that year's OLS of rate on rent, fit on full-count CoCs including King County per 10,000 residents estimated
residual number rate_per_10k minus predicted_rate per 10,000 residents derived
predicted_rate_rent_vac number Total rate predicted by OLS on rent and vacancy per 10,000 residents estimated
residual_rent_vac number rate_per_10k minus predicted_rate_rent_vac per 10,000 residents derived
predicted_unsheltered_rate number Unsheltered rate predicted by OLS on rent per 10,000 residents estimated
unsheltered_residual number unsheltered_rate minus predicted_unsheltered_rate per 10,000 residents derived

CoC-to-Census geography crosswalk

The Census counties and places added or subtracted to rebuild each CoC boundary.

Unit of observation: CoC

View this table

coc_geography_map.csv text/csv · 3.6 KB (3,645 bytes)
6 field definitions
FieldTypeMeaningUnitsValue type
coc_code string HUD CoC code — observed
code_aliases string Other codes HUD used for the same CoC — observed
short_name string Short display name — derived
add_geos string Semicolon-separated Census geographies (county:FIPS or place:FIPS) summed into the CoC — derived
subtract_geos string Census places subtracted (e.g. cities with their own CoC) — derived
note string Notes on boundary approximations — derived

King County actual vs. rent-predicted homelessness rate

For each year, comparison sample and measure: King County's rate, the rate predicted from rent by a fit that leaves King out, and the 95% prediction interval for the difference.

Unit of observation: Year x comparison sample x measure

View this table

king_rent_residuals.csv text/csv · 2.8 KB (2,883 bytes)
13 field definitions
FieldTypeMeaningUnitsValue type
year integer PIT year (full-count years only) — observed
sample string all_full_count, major_city (HUD 2025 Major City CoCs) or excl_nyc — derived
measure string total or unsheltered rate — derived
n integer CoCs in the fit (King excluded) CoCs derived
king_median_rent number King County median gross rent that year US dollars per month, nominal observed
actual_rate number King County rate per 10,000 residents derived
predicted_rate number Rate predicted for King's rent per 10,000 residents estimated
residual number actual_rate minus predicted_rate per 10,000 residents estimated
pi95_low number Lower bound of the 95% prediction interval for the residual per 10,000 residents estimated
pi95_high number Upper bound of the 95% prediction interval per 10,000 residents estimated
slope_per_100 number Fitted change in rate per $100 of median rent per 10,000 residents per $100 estimated
slope_se number Standard error of the slope per 10,000 residents per $100 estimated
r2 number R-squared of the fit — estimated

Change in homelessness and rent by CoC, 2019 to latest full count

Baseline features, distance to King County, and changes in total and unsheltered rates and in median rent for all 67 CoCs.

Unit of observation: CoC

View this table

peers.csv text/csv · 11.5 KB (11,796 bytes)
28 field definitions
FieldTypeMeaningUnitsValue type
coc_code string HUD CoC code — observed
short_name string Short display name — derived
hud_category_2025 string HUD 2025 CoC category — observed
dist_rank integer Rank by distance to King County on 4 standardized 2015-19 features (0 = King) — derived
distance number Euclidean distance on z-scored features: mean total rate, mean unsheltered rate, relative rent, rent growth 2015-19 — derived
dist_rank_with_pop integer Rank when log 2019 population is added as a fifth feature — derived
distance_with_pop number Distance with population added — derived
simple_screen integer 1 if 2019 rent within ±15% of King's and 2019 rate within ±40% of King's — derived
n_full_2015_19 integer Full-count years in 2015-19 used for the baseline years derived
base_rate_per_10k number Mean total rate over full-count years 2015-19 per 10,000 residents derived
base_unsheltered_rate number Mean unsheltered rate over full-count years 2015-19 per 10,000 residents derived
base_rel_rent number Median rent divided by the sample median, averaged 2015-19 ratio derived
rent_growth_2015_19_pct number Nominal median rent growth 2015 to 2019 percent derived
pop_2019 integer 2019 population people observed
rent_2019 number 2019 median gross rent US dollars per month observed
rate_2019 number 2019 total rate per 10,000 residents derived
unsheltered_rate_2019 number 2019 unsheltered rate per 10,000 residents derived
end_year integer Latest full count: 2024, or 2025 where 2024 was sheltered-only — derived
end_rate number Total rate in end_year per 10,000 residents derived
end_unsheltered_rate number Unsheltered rate in end_year per 10,000 residents derived
chg_rate number end_rate minus rate_2019 per 10,000 residents derived
chg_rate_pct number Percent change in total rate percent derived
chg_unsheltered_rate number end_unsheltered_rate minus unsheltered_rate_2019 per 10,000 residents derived
chg_unsheltered_pct number Percent change in unsheltered rate percent derived
rent_2024 number 2024 median gross rent US dollars per month observed
rent_growth_2019_24_pct number Nominal median rent growth 2019 to 2024 percent derived
rel_rent_2019 number 2019 rent divided by the sample median ratio derived
rel_rent_2024 number 2024 rent divided by the sample median ratio derived

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

Question: How far is King County's total homelessness rate per 10,000 above or below the rate predicted from its median rent, across 59-66 large CoCs?

Result: 2015: +18.2 (95% PI -26.33 to 62.73); 2016: +20.41 (95% PI -26.65 to 67.48); 2017: +23.35 (95% PI -19.64 to 66.35); 2018: +23.06 (95% PI -18.35 to 64.47); 2019: +16.2 (95% PI -25.07 to 57.47); 2022: +25.87 (95% PI -13.35 to 65.09); 2024: +34.03 (95% PI -22.33 to 90.39)

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

Scope: All full-count CoCs in the 67-CoC sample, 2015-2024, King excluded from each fit

Estimates with uncertainty
EstimateValueUncertaintyMethodn
2015 King County total rate minus rent-predicted rate 18.2 ± 22.29 per 10,000 residents (95% CI -26.33 to 62.73) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 66
2016 King County total rate minus rent-predicted rate 20.41 ± 23.45 per 10,000 residents (95% CI -26.65 to 67.48) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 54
2017 King County total rate minus rent-predicted rate 23.35 ± 21.52 per 10,000 residents (95% CI -19.64 to 66.35) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 66
2018 King County total rate minus rent-predicted rate 23.06 ± 20.65 per 10,000 residents (95% CI -18.35 to 64.47) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 55
2019 King County total rate minus rent-predicted rate 16.2 ± 20.66 per 10,000 residents (95% CI -25.07 to 57.47) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 66
2022 King County total rate minus rent-predicted rate 25.87 ± 19.61 per 10,000 residents (95% CI -13.35 to 65.09) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 63
2024 King County total rate minus rent-predicted rate 34.03 ± 28.14 per 10,000 residents (95% CI -22.33 to 90.39) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 59

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 checks

King County total rate vs. rent-predicted rate, HUD Major City CoCs only

Question: Same as king-total-vs-rent, comparing only with HUD Major City CoCs.

Result: 2015: +1.93 (95% PI -45.25 to 49.1); 2016: -8.11 (95% PI -57.39 to 41.17); 2017: +6.23 (95% PI -38.39 to 50.86); 2018: -7.5 (95% PI -50.63 to 35.62); 2019: -2.63 (95% PI -43.38 to 38.12); 2022: +9.38 (95% PI -27.93 to 46.68); 2024: +14.24 (95% PI -47.74 to 76.21)

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

Scope: HUD 2025 Major City CoCs with full counts, King excluded

Estimates with uncertainty
EstimateValueUncertaintyMethodn
2015 King County total rate minus rent-predicted rate 1.93 ± 23.34 per 10,000 residents (95% CI -45.25 to 49.1) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 42
2016 King County total rate minus rent-predicted rate -8.11 ± 24.19 per 10,000 residents (95% CI -57.39 to 41.17) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 34
2017 King County total rate minus rent-predicted rate 6.23 ± 22.08 per 10,000 residents (95% CI -38.39 to 50.86) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 42
2018 King County total rate minus rent-predicted rate -7.5 ± 21.17 per 10,000 residents (95% CI -50.63 to 35.62) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 34
2019 King County total rate minus rent-predicted rate -2.63 ± 20.16 per 10,000 residents (95% CI -43.38 to 38.12) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 42
2022 King County total rate minus rent-predicted rate 9.38 ± 18.41 per 10,000 residents (95% CI -27.93 to 46.68) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 39
2024 King County total rate minus rent-predicted rate 14.24 ± 30.53 per 10,000 residents (95% CI -47.74 to 76.21) ± 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

King County total rate vs. rent-predicted rate, excluding New York City

Question: Same as king-total-vs-rent, without New York City (right-to-shelter and migrant sheltering).

Result: 2015: +19.67 (95% PI -22.64 to 61.98); 2016: +22.84 (95% PI -21.75 to 67.42); 2017: +24.82 (95% PI -15.6 to 65.24); 2018: +26.27 (95% PI -10.89 to 63.44); 2019: +17.8 (95% PI -20.33 to 55.94); 2022: +26.85 (95% PI -10.93 to 64.64); 2024: +37.02 (95% PI -7.03 to 81.07)

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

Scope: All full-count CoCs except NYC, King excluded

Estimates with uncertainty
EstimateValueUncertaintyMethodn
2015 King County total rate minus rent-predicted rate 19.67 ± 21.17 per 10,000 residents (95% CI -22.64 to 61.98) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 65
2016 King County total rate minus rent-predicted rate 22.84 ± 22.21 per 10,000 residents (95% CI -21.75 to 67.42) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 53
2017 King County total rate minus rent-predicted rate 24.82 ± 20.23 per 10,000 residents (95% CI -15.6 to 65.24) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 65
2018 King County total rate minus rent-predicted rate 26.27 ± 18.52 per 10,000 residents (95% CI -10.89 to 63.44) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 54
2019 King County total rate minus rent-predicted rate 17.8 ± 19.08 per 10,000 residents (95% CI -20.33 to 55.94) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 65
2022 King County total rate minus rent-predicted rate 26.85 ± 18.89 per 10,000 residents (95% CI -10.93 to 64.64) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 62
2024 King County total rate minus rent-predicted rate 37.02 ± 21.99 per 10,000 residents (95% CI -7.03 to 81.07) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 58

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

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

Question: How far is King County's unsheltered rate per 10,000 above or below the rate predicted from its median rent?

Result: 2015: +8.08 (95% PI -8.94 to 25.09); 2016: +13.85 (95% PI 1.18 to 26.51); 2017: +13.25 (95% PI -3.93 to 30.43); 2018: +19.18 (95% PI 5.91 to 32.44); 2019: +7.52 (95% PI -13.01 to 28.04); 2022: +17.48 (95% PI -7.38 to 42.33); 2024: +25.96 (95% PI 0.7 to 51.22)

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

Scope: All full-count CoCs, King excluded

Estimates with uncertainty
EstimateValueUncertaintyMethodn
2015 King County unsheltered rate minus rent-predicted rate 8.08 ± 8.52 per 10,000 residents (95% CI -8.94 to 25.09) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 66
2016 King County unsheltered rate minus rent-predicted rate 13.85 ± 6.31 per 10,000 residents (95% CI 1.18 to 26.51) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 54
2017 King County unsheltered rate minus rent-predicted rate 13.25 ± 8.6 per 10,000 residents (95% CI -3.93 to 30.43) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 66
2018 King County unsheltered rate minus rent-predicted rate 19.18 ± 6.61 per 10,000 residents (95% CI 5.91 to 32.44) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 55
2019 King County unsheltered rate minus rent-predicted rate 7.52 ± 10.27 per 10,000 residents (95% CI -13.01 to 28.04) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 66
2022 King County unsheltered rate minus rent-predicted rate 17.48 ± 12.43 per 10,000 residents (95% CI -7.38 to 42.33) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 63
2024 King County unsheltered rate minus rent-predicted rate 25.96 ± 12.62 per 10,000 residents (95% CI 0.7 to 51.22) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 59

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

King County unsheltered rate vs. rent-predicted rate, Major City CoCs

Question: Same as king-unsheltered-vs-rent, Major City CoCs only.

Result: 2015: +2.51 (95% PI -15.76 to 20.79); 2016: +10.9 (95% PI -3.28 to 25.08); 2017: +6.26 (95% PI -11.04 to 23.55); 2018: +15.3 (95% PI -0.08 to 30.67); 2019: -0.83 (95% PI -21.56 to 19.9); 2022: +9.24 (95% PI -17.59 to 36.08); 2024: +19.65 (95% PI -7.35 to 46.66)

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

Scope: HUD 2025 Major City CoCs with full counts, King excluded

Estimates with uncertainty
EstimateValueUncertaintyMethodn
2015 King County unsheltered rate minus rent-predicted rate 2.51 ± 9.04 per 10,000 residents (95% CI -15.76 to 20.79) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 42
2016 King County unsheltered rate minus rent-predicted rate 10.9 ± 6.96 per 10,000 residents (95% CI -3.28 to 25.08) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 34
2017 King County unsheltered rate minus rent-predicted rate 6.26 ± 8.56 per 10,000 residents (95% CI -11.04 to 23.55) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 42
2018 King County unsheltered rate minus rent-predicted rate 15.3 ± 7.55 per 10,000 residents (95% CI -0.08 to 30.67) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 34
2019 King County unsheltered rate minus rent-predicted rate -0.83 ± 10.26 per 10,000 residents (95% CI -21.56 to 19.9) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 42
2022 King County unsheltered rate minus rent-predicted rate 9.24 ± 13.24 per 10,000 residents (95% CI -17.59 to 36.08) ± one standard error; 95% confidence interval Leave-one-out OLS on median rent; 95% prediction interval 39
2024 King County unsheltered rate minus rent-predicted rate 19.65 ± 13.3 per 10,000 residents (95% CI -7.35 to 46.66) ± 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.

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

Rent growth 2019-2024 vs. change in homelessness, large CoCs

Question: Did CoCs whose rents rose faster from 2019 to 2024 see larger increases in homelessness?

Result: Rent growth 2019-24 vs % change in total homelessness rate: r = -0.043 (95% CI -0.282 to 0.201); Rent growth 2019-24 vs % change in unsheltered rate: r = -0.007 (95% CI -0.249 to 0.235); Rent growth 2019-24 vs change in total rate per 10,000: r = -0.135 (95% CI -0.366 to 0.11)

Operation: Pearson correlation across CoCs, King County excluded

Scope: 66 large CoCs other than King County

Estimates with uncertainty
EstimateValueUncertaintyMethodn
Rent growth 2019-24 vs % change in total homelessness rate -0.043 Pearson correlation coefficient (95% CI -0.282 to 0.201) 95% confidence interval Pearson r across large CoCs excluding King County; 95% CI by Fisher z-transform 66
Rent growth 2019-24 vs % change in unsheltered rate -0.007 Pearson correlation coefficient (95% CI -0.249 to 0.235) 95% confidence interval Pearson r across large CoCs excluding King County; 95% CI by Fisher z-transform 66
Rent growth 2019-24 vs change in total rate per 10,000 -0.135 Pearson correlation coefficient (95% CI -0.366 to 0.11) 95% confidence interval Pearson r across large CoCs excluding King County; 95% CI by Fisher z-transform 66

View this analysis

  • ACS median gross rent includes long-tenure leases and lags asking rents.
  • Tests only whether changes in rent explain changes in homelessness; rent level remains the strongest cross-sectional correlate.
  • Several CoCs changed count methods between 2019 and their end year.

King County rank on rent growth and on change in homelessness, 2019 to latest full count

Question: Where does King County rank among 67 large CoCs on rent growth and on the increase in homelessness since 2019?

Result: Rent growth 2019-24: +21.5%, 60th of 67. Increase in unsheltered rate: +18.7 per 10,000, 4th of 67. Increase in total rate: +22.4 per 10,000, 7th of 67.

Operation: Rank of King County within the 67 CoCs

Scope: 67 large CoCs

View this analysis

  • As counted; the 2022 count-method change may account for 0 to about 3,600 of King County's 2024 count (central estimate about 1,100).
  • Descriptive ranks, not a causal estimate.

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