St. Helen, MI
St. Helen, MI's wildfire risk, in USFS's own numbers
St. Helen's 3,560 buildings earn a 76th-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 St. Helen at the 72nd percentile, close to its 76th-percentile risk score.
Where St. Helen's buildings actually sit
Indirect exposure is dominant in St. Helen (66.3% of 3,560 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 33.2% sit in the Direct zone.
St. Helen against the rest of the country
Within Michigan, St. Helen ranks higher (100th percentile) than it does nationally (76th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, St. Helen ranks 7,723 for wildfire risk (1 is highest) and 4,706 by building count (1 is largest). Within Michigan alone, it ranks 3 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
St. Helen's 76th-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.
Lowering exposure, not just insuring around it
St. Helen's 66.3% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where St. Helen's figures come from
The methodology guide shows exactly how USFS turned 3,560 counted buildings into the percentiles shown above for St. Helen. The exposure-zones guide covers what St. Helen's dominant indirect exposure actually means, with real examples from across the dataset.