Marne, OH
Marne wildfire risk explained
Marne sits at the 34th percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 512 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Marne at the 34th percentile, close to its 34th-percentile risk score.
Marne's building exposure, zone by zone
USFS classifies 71.5% of Marne's buildings as Direct exposure, higher than its 28.5% Indirect share and far above its 0% Minimal share — a profile where 366 structures sit close enough to vegetation that lot clearing matters most.
How Marne compares
Marne's 74th-percentile standing inside Ohio outpaces its 34th 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, Marne ranks 20,868 for wildfire risk (1 is highest) and 17,154 by building count (1 is largest). Within Ohio alone, it ranks 334 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
Marne's moderate wildfire rating (34th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
What would actually reduce this score
Marne's 71.5% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Marne's figures come from
Every one of the two percentiles behind Marne's 20,868-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Marne's dominant direct exposure actually means, with real examples from across the dataset.