Thorp, WA
How exposed is Thorp to wildfire?
USFS's Wildfire Risk to Communities model puts Thorp at the 99th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 224 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Thorp's burn probability — fire likelihood with no building count factored in — sits at the 100th percentile nationally.
Where Thorp's buildings actually sit
Direct exposure dominates in Thorp: 80.4% of its 224 buildings, versus 19.6% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Thorp compares
There's little gap between Thorp's 99th national percentile and its 98th percentile inside Washington, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Thorp ranks 237 for wildfire risk (1 is highest) and 23,997 by building count (1 is largest). Within Washington alone, it ranks 14 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
Thorp'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
With 80.4% of Thorp in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Thorp's figures come from
The methodology guide shows exactly how USFS turned 224 counted buildings into the percentiles shown above for Thorp. The exposure-zones guide covers what Thorp's dominant direct exposure actually means, with real examples from across the dataset.