Kilbourne, OH
Kilbourne wildfire risk explained
USFS's Wildfire Risk to Communities model puts Kilbourne at the 10th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 119 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Kilbourne at the 10th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Kilbourne's buildings actually sit
USFS classifies 69.8% of Kilbourne's buildings as Direct exposure, higher than its 0% Indirect share and far above its 30.3% Minimal share — a profile where 83 structures sit close enough to vegetation that lot clearing matters most.
How Kilbourne compares
Within Ohio, Kilbourne ranks higher (31st percentile) than it does nationally (10th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Kilbourne ranks 28,442 for wildfire risk (1 is highest) and 28,209 by building count (1 is largest). Within Ohio alone, it ranks 867 of 1,264 places by risk. See the full county-by-county picture for Ohio on its state page.
What this risk score means for insurance
Kilbourne's low rating (10th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
With 69.8% of Kilbourne 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 Kilbourne's figures come from
Every one of the two percentiles behind Kilbourne's 28,442-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Kilbourne's dominant direct exposure actually means, with real examples from across the dataset.