Garrett, WA
Garrett, WA's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Garrett lands at the 63rd percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 1,301 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Garrett at the 66th national percentile — 3 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Garrett's buildings actually sit
USFS classifies 51.8% of Garrett's buildings as Direct exposure, higher than its 14.7% Indirect share and far above its 33.5% Minimal share — a profile where 674 structures sit close enough to vegetation that lot clearing matters most.
Garrett against the rest of the country
There's little gap between Garrett's 63rd national percentile and its 69th percentile inside Washington, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Garrett ranks 11,647 for wildfire risk (1 is highest) and 10,073 by building count (1 is largest). Within Washington alone, it ranks 196 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
Garrett's high rating (63rd 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.
Lowering exposure, not just insuring around it
Garrett's 51.8% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Garrett's figures come from
The methodology guide shows exactly how USFS turned 1,301 counted buildings into the percentiles shown above for Garrett. The exposure-zones guide covers what Garrett's dominant direct exposure actually means, with real examples from across the dataset.