K. I. Sawyer, MI
How exposed is K. I. Sawyer to wildfire?
USFS scores K. I. Sawyer at the 76th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 885 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts K. I. Sawyer at the 72nd percentile, close to its 76th-percentile risk score.
Where K. I. Sawyer's buildings actually sit
61.5% of K. I. Sawyer's 885 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 38.5% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
How K. I. Sawyer compares
K. I. Sawyer's 100th-percentile standing inside Michigan outpaces its 76th 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, K. I. Sawyer ranks 7,563 for wildfire risk (1 is highest) and 12,861 by building count (1 is largest). Within Michigan alone, it ranks 2 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Shopping for coverage in K. I. Sawyer
K. I. Sawyer's high rating (76th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in K. I. Sawyer
With ember exposure the dominant pattern in K. I. Sawyer (61.5% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where K. I. Sawyer's figures come from
The methodology guide shows exactly how USFS turned 885 counted buildings into the percentiles shown above for K. I. Sawyer. The exposure-zones guide covers what K. I. Sawyer's dominant indirect exposure actually means, with real examples from across the dataset.