Mitiwanga, OH
Mitiwanga, OH's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Mitiwanga lands at the 36th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 276 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Mitiwanga's burn probability — fire likelihood with no building count factored in — sits at the 32nd percentile nationally.
What "at risk" means for the buildings here
85.1% of Mitiwanga's 276 buildings sit in USFS's Minimal exposure zone, with only 14.9% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
How Mitiwanga compares
Compare Mitiwanga's two percentiles: 79th within Ohio, only 36th nationally — a gap of 43 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Mitiwanga ranks 20,241 for wildfire risk (1 is highest) and 22,328 by building count (1 is largest). Within Ohio alone, it ranks 270 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
At the 36th national percentile, Mitiwanga rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Even with 85.1% of Mitiwanga 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 Mitiwanga's figures come from
Every one of the two percentiles behind Mitiwanga's 20,241-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Mitiwanga's dominant minimal exposure actually means, with real examples from across the dataset.