Berkley, MI
Berkley, MI's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Berkley at the 4th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 8,641 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 Berkley at the 5th percentile, close to its 4th-percentile risk score.
Berkley's building exposure, zone by zone
Berkley rates 99.2% Minimal exposure against just 0.8% Direct and 0% Indirect — of 8,641 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually. At 8,641 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
How Berkley compares
Berkley scores 4th nationally and 7th within Michigan — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Berkley ranks 30,357 for wildfire risk (1 is highest) and 1,927 by building count (1 is largest). Within Michigan alone, it ranks 689 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Berkley and the insurance market
Berkley's low wildfire rating (4th 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
Berkley's 99.2% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Berkley's figures come from
The methodology guide shows exactly how USFS turned 8,641 counted buildings into the percentiles shown above for Berkley. The exposure-zones guide covers what Berkley's dominant minimal exposure actually means, with real examples from across the dataset.