Fairgrove, MI
Fairgrove wildfire risk explained
USFS scores Fairgrove at the 1st national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 372 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Fairgrove at the 1st national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Fairgrove's buildings actually sit
USFS classifies 78.8% of Fairgrove's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 21.2% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Fairgrove against the rest of the country
Fairgrove scores 1st nationally and 0th 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, Fairgrove ranks 31,308 for wildfire risk (1 is highest) and 19,821 by building count (1 is largest). Within Michigan alone, it ranks 741 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
What this risk score means for insurance
Fairgrove's low wildfire rating (1st 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
Even with 78.8% of Fairgrove 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 Fairgrove's figures come from
The methodology guide shows exactly how USFS turned 372 counted buildings into the percentiles shown above for Fairgrove. The exposure-zones guide covers what Fairgrove's dominant minimal exposure actually means, with real examples from across the dataset.