Washtucna, WA
How exposed is Washtucna to wildfire?
Washtucna's 242 buildings earn a 92nd-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Washtucna at the 93rd percentile, close to its 92nd-percentile risk score.
Washtucna's building exposure, zone by zone
77.3% of Washtucna's 242 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 22.7% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Washtucna against the rest of the country
Washtucna's risk sits at a similar level relative to Washington (83rd percentile statewide) as it does nationally (92nd) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Washtucna ranks 2,536 for wildfire risk (1 is highest) and 23,411 by building count (1 is largest). Within Washington alone, it ranks 110 of 628 places by risk. See the full county-by-county picture for Washington on its state page.
What this risk score means for insurance
Washtucna's 92nd-percentile, very high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Lowering exposure, not just insuring around it
Because 77.3% of Washtucna's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Washtucna's figures come from
The methodology guide shows exactly how USFS turned 242 counted buildings into the percentiles shown above for Washtucna. The exposure-zones guide covers what Washtucna's dominant indirect exposure actually means, with real examples from across the dataset.