Port William, OH
Port William, OH's wildfire risk, in USFS's own numbers
Port William's 179 buildings earn a 2nd-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Port William at the 2nd percentile, close to its 2nd-percentile risk score.
What "at risk" means for the buildings here
90.5% of Port William's 179 buildings sit in USFS's Minimal exposure zone, with only 9.5% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
Port William against the rest of the country
There's little gap between Port William's 2nd national percentile and its 11th percentile inside Ohio, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Port William ranks 30,998 for wildfire risk (1 is highest) and 25,684 by building count (1 is largest). Within Ohio alone, it ranks 1,127 of 1,264 places by risk. See the full county-by-county picture for Ohio on its state page.
Port William and the insurance market
At the 2nd national percentile, Port William rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Port William
Even with 90.5% of Port William outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Port William's figures come from
Every one of the two percentiles behind Port William's 30,998-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Port William's dominant minimal exposure actually means, with real examples from across the dataset.