Malott, WA
How exposed is Malott to wildfire?
USFS scores Malott at the 99th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 378 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Malott at the 99th 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
75.9% of Malott's 378 buildings sit in USFS's Direct exposure zone, roughly 287 structures close enough to burnable vegetation for flame contact, not just embers — 24.1% fall in the Indirect, ember-only zone and 0% are Minimal.
How Malott compares
Malott scores 99th nationally and 97th 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, Malott ranks 395 for wildfire risk (1 is highest) and 19,707 by building count (1 is largest). Within Washington alone, it ranks 21 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
Malott's 99th-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 Direct exposure dominates in Malott (75.9%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Malott's figures come from
Malott's 99th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Malott's dominant direct exposure actually means, with real examples from across the dataset.