Ages, KY
How exposed is Ages to wildfire?
Out of every US place USFS scores, Ages lands at the 91st percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 241 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Ages at the 93rd percentile, close to its 91st-percentile risk score.
What "at risk" means for the buildings here
Most of Ages's buildings (51.9%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Ages compares
Ages's risk sits at a similar level relative to Kentucky (93rd percentile statewide) as it does nationally (91st) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Ages ranks 2,803 for wildfire risk (1 is highest) and 23,429 by building count (1 is largest). Within Kentucky alone, it ranks 37 of 552 places by risk. See the full county-by-county picture for Kentucky on its state page.
Shopping for coverage in Ages
At the 91st percentile nationally, Ages carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Hardening a home in Ages
Ages's 51.9% 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 Ages's figures come from
Every one of the two percentiles behind Ages's 2,803-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Ages's dominant indirect exposure actually means, with real examples from across the dataset.