Gwinn, MI
How exposed is Gwinn to wildfire?
Gwinn's 1,138 buildings earn a 72nd-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Gwinn at the 69th percentile, close to its 72nd-percentile risk score.
Gwinn's building exposure, zone by zone
Most of Gwinn's buildings (70.4%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Gwinn against the rest of the country
Within Michigan, Gwinn ranks higher (99th percentile) than it does nationally (72nd) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Gwinn ranks 8,883 for wildfire risk (1 is highest) and 11,027 by building count (1 is largest). Within Michigan alone, it ranks 6 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
Gwinn's 72nd-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
What would actually reduce this score
With ember exposure the dominant pattern in Gwinn (70.4% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Gwinn's figures come from
Every one of the two percentiles behind Gwinn's 8,883-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Gwinn's dominant indirect exposure actually means, with real examples from across the dataset.