Oakwood (Montgomery), OH
Oakwood (Montgomery), OH's wildfire risk, in USFS's own numbers
Oakwood (Montgomery)'s 4,528 buildings earn a 10th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Oakwood (Montgomery)'s burn probability — fire likelihood with no building count factored in — sits at the 12th percentile nationally.
Where Oakwood (Montgomery)'s buildings actually sit
90.9% of Oakwood (Montgomery)'s 4,528 buildings sit in USFS's Minimal exposure zone, with only 9.1% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
Where Oakwood (Montgomery) ranks
Oakwood (Montgomery)'s 32nd-percentile standing inside Ohio outpaces its 10th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Oakwood (Montgomery) ranks 28,408 for wildfire risk (1 is highest) and 3,773 by building count (1 is largest). Within Ohio alone, it ranks 863 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 10th national percentile, Oakwood (Montgomery) 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 Oakwood (Montgomery)
Oakwood (Montgomery)'s 90.9% 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 Oakwood (Montgomery)'s figures come from
Oakwood (Montgomery)'s 10th-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 Oakwood (Montgomery)'s dominant minimal exposure actually means, with real examples from across the dataset.