Powers, MI
Powers, MI's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Powers at the 34th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 193 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Powers at the 34th percentile, close to its 34th-percentile risk score.
Powers's building exposure, zone by zone
64.8% of Powers's 193 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 35.2% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
How Powers compares
Powers's 85th-percentile standing inside Michigan 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, Powers ranks 20,924 for wildfire risk (1 is highest) and 25,123 by building count (1 is largest). Within Michigan alone, it ranks 111 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Powers and the insurance market
Powers'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.
Lowering exposure, not just insuring around it
Because 64.8% of Powers's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Powers's figures come from
Powers's 34th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Powers's dominant indirect exposure actually means, with real examples from across the dataset.