Packwood, WA
Packwood, WA's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Packwood lands at the 69th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 418 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Packwood at the 71st national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 58.6% of Packwood's buildings as Direct exposure, higher than its 41.4% Indirect share and far above its 0% Minimal share — a profile where 245 structures sit close enough to vegetation that lot clearing matters most.
How Packwood compares
Packwood scores 69th nationally and 71st within Washington — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Packwood ranks 9,691 for wildfire risk (1 is highest) and 18,869 by building count (1 is largest). Within Washington alone, it ranks 182 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
Packwood's high rating (69th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
With 58.6% of Packwood in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Packwood's figures come from
The methodology guide shows exactly how USFS turned 418 counted buildings into the percentiles shown above for Packwood. The exposure-zones guide covers what Packwood's dominant direct exposure actually means, with real examples from across the dataset.