Seaton, IL
How exposed is Seaton to wildfire?
Seaton sits at the 5th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 193 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Seaton at the 5th percentile, close to its 5th-percentile risk score.
Where Seaton's buildings actually sit
Most of Seaton's buildings (84.5% of 193) 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.
Seaton against the rest of the country
Seaton's 28th-percentile standing inside Illinois outpaces its 5th 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, Seaton ranks 29,966 for wildfire risk (1 is highest) and 25,117 by building count (1 is largest). Within Illinois alone, it ranks 1,038 of 1,445 places by risk. See the full county-by-county picture for Illinois on its state page.
Shopping for coverage in Seaton
Seaton's low wildfire rating (5th 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.
Lowering exposure, not just insuring around it
With 84.5% of buildings rated Minimal exposure, Seaton gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Seaton's figures come from
Every one of the two percentiles behind Seaton's 29,966-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Seaton's dominant minimal exposure actually means, with real examples from across the dataset.