Camas, WA
Camas, WA's wildfire risk, in USFS's own numbers
USFS scores Camas at the 44th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 9,136 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Camas's burn probability — fire likelihood with no building count factored in — sits at the 47th percentile nationally.
Where Camas's buildings actually sit
Most of Camas's buildings (78.7% of 9,136) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure. At 9,136 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Camas against the rest of the country
Compare Camas's two percentiles: 60th within Washington, only 44th nationally — a gap of 16 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Camas ranks 17,665 for wildfire risk (1 is highest) and 1,809 by building count (1 is largest). Within Washington alone, it ranks 254 of 628 places by risk. See the full county-by-county picture for Washington on its state page.
Camas and the insurance market
At the 44th national percentile, Camas rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
With 78.7% of buildings rated Minimal exposure, Camas gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Camas's figures come from
Camas's 44th-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 Camas's dominant minimal exposure actually means, with real examples from across the dataset.