Sterling, MI
How exposed is Sterling to wildfire?
Out of every US place USFS scores, Sterling lands at the 30th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 346 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Sterling at the 31st national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Sterling's building exposure, zone by zone
Only 28.3% of Sterling's 346 buildings carry Direct exposure and 20.8% carry Indirect; the remaining 50.9% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
Where Sterling ranks
Compare Sterling's two percentiles: 83rd within Michigan, only 30th nationally — a gap of 53 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Sterling ranks 21,965 for wildfire risk (1 is highest) and 20,426 by building count (1 is largest). Within Michigan alone, it ranks 129 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Sterling and the insurance market
Sterling's moderate wildfire rating (30th 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.
Hardening a home in Sterling
Even with 50.9% of Sterling 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 Sterling's figures come from
Sterling's 30th-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 Sterling's dominant minimal exposure actually means, with real examples from across the dataset.