Schwana, WA
How exposed is Schwana to wildfire?
USFS's Wildfire Risk to Communities model puts Schwana at the 92nd national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 97 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Schwana at the 92nd percentile, close to its 92nd-percentile risk score.
Schwana's building exposure, zone by zone
Indirect exposure is dominant in Schwana (77.3% of 97 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 21.7% sit in the Direct zone.
Schwana against the rest of the country
There's little gap between Schwana's 92nd national percentile and its 83rd percentile inside Washington, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Schwana ranks 2,441 for wildfire risk (1 is highest) and 29,207 by building count (1 is largest). Within Washington alone, it ranks 106 of 628 places by risk. See the full county-by-county picture for Washington on its state page.
Schwana and the insurance market
Schwana'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.
What would actually reduce this score
Because 77.3% of Schwana'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 Schwana's figures come from
The methodology guide shows exactly how USFS turned 97 counted buildings into the percentiles shown above for Schwana. The exposure-zones guide covers what Schwana's dominant indirect exposure actually means, with real examples from across the dataset.