Paw Paw, MI
How exposed is Paw Paw to wildfire?
Out of every US place USFS scores, Paw Paw lands at the 11th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 1,528 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Paw Paw's burn probability — fire likelihood with no building count factored in — sits at the 12th percentile nationally.
What "at risk" means for the buildings here
Most of Paw Paw's buildings (78.9%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Paw Paw compares
Paw Paw's 39th-percentile standing inside Michigan outpaces its 11th 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, Paw Paw ranks 27,957 for wildfire risk (1 is highest) and 9,046 by building count (1 is largest). Within Michigan alone, it ranks 456 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
Paw Paw's low wildfire rating (11th 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 78.9% of Paw Paw'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 Paw Paw's figures come from
Paw Paw's 11th-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 Paw Paw's dominant indirect exposure actually means, with real examples from across the dataset.