Earlington, KY
Earlington wildfire risk explained
USFS scores Earlington at the 44th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 838 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Earlington's burn probability — fire likelihood with no building count factored in — sits at the 46th percentile nationally.
Where Earlington's buildings actually sit
Indirect exposure is dominant in Earlington (60.4% of 838 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 39.1% sit in the Direct zone.
Earlington against the rest of the country
Within Kentucky, Earlington ranks higher (73rd percentile) than it does nationally (44th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Earlington ranks 17,644 for wildfire risk (1 is highest) and 13,270 by building count (1 is largest). Within Kentucky alone, it ranks 148 of 552 places by risk. See the full county-by-county picture for Kentucky on its state page.
Shopping for coverage in Earlington
Earlington's elevated wildfire rating (44th 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.
Hardening a home in Earlington
Earlington's 60.4% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Earlington's figures come from
Every one of the two percentiles behind Earlington's 17,644-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Earlington's dominant indirect exposure actually means, with real examples from across the dataset.