Sequim, WA
Sequim wildfire risk explained
USFS scores Sequim at the 24th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 3,888 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Sequim at the 27th national percentile — 3 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Sequim's buildings actually sit
71.6% of Sequim's 3,888 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 8.6% Direct and 19.8% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Sequim against the rest of the country
Sequim's 39th-percentile standing inside Washington outpaces its 24th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Sequim ranks 23,936 for wildfire risk (1 is highest) and 4,352 by building count (1 is largest). Within Washington alone, it ranks 382 of 628 places by risk. See the full county-by-county picture for Washington on its state page.
Sequim and the insurance market
Sequim's moderate wildfire rating (24th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
What would actually reduce this score
With ember exposure the dominant pattern in Sequim (71.6% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Sequim's figures come from
Every one of the two percentiles behind Sequim's 23,936-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Sequim's dominant indirect exposure actually means, with real examples from across the dataset.