Avon, OH
Avon wildfire risk explained
Out of every US place USFS scores, Avon lands at the 26th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 9,726 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 Avon at the 27th percentile, close to its 26th-percentile risk score.
Avon's building exposure, zone by zone
65.6% of Avon's 9,726 buildings sit in USFS's Minimal exposure zone, with only 34.4% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself. At 9,726 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Where Avon ranks
Within Ohio, Avon ranks higher (60th percentile) than it does nationally (26th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Avon ranks 23,207 for wildfire risk (1 is highest) and 1,681 by building count (1 is largest). Within Ohio alone, it ranks 511 of 1,264 places by risk. See the full county-by-county picture for Ohio on its state page.
Shopping for coverage in Avon
At the 26th national percentile, Avon rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
Avon's 65.6% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Avon's figures come from
Avon's 26th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Avon's dominant minimal exposure actually means, with real examples from across the dataset.