WildfireRiskFinder

Ovid, MI

How exposed is Ovid to wildfire?

Low
6thpercentile nationally

USFS scores Ovid at the 6th national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 773 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Ovid at the 7th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.

Where Ovid's buildings actually sit

773Total buildings
12.6%Direct exposure
0%Indirect exposure
87.5%Minimal exposure

Most of Ovid's buildings (87.5% of 773) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.

Ovid against the rest of the country

Ovid scores 6th nationally and 17th within Michigan — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Ovid ranks 29,537 for wildfire risk (1 is highest) and 13,891 by building count (1 is largest). Within Michigan alone, it ranks 619 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.

Ovid and the insurance market

Ovid's low wildfire rating (6th 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

Ovid's 87.5% 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 Ovid's figures come from

The methodology guide shows exactly how USFS turned 773 counted buildings into the percentiles shown above for Ovid. The exposure-zones guide covers what Ovid's dominant minimal exposure actually means, with real examples from across the dataset.