White Pigeon, MI
White Pigeon, MI's wildfire risk, in USFS's own numbers
White Pigeon sits at the 2nd percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 922 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts White Pigeon at the 2nd percentile, close to its 2nd-percentile risk score.
Where White Pigeon's buildings actually sit
Most of White Pigeon's buildings (92.3% of 922) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.
White Pigeon against the rest of the country
There's little gap between White Pigeon's 2nd national percentile and its 3rd percentile inside Michigan, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, White Pigeon ranks 30,923 for wildfire risk (1 is highest) and 12,537 by building count (1 is largest). Within Michigan alone, it ranks 725 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
White Pigeon's low rating (2nd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
White Pigeon's 92.3% 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 White Pigeon's figures come from
The methodology guide shows exactly how USFS turned 922 counted buildings into the percentiles shown above for White Pigeon. The exposure-zones guide covers what White Pigeon's dominant minimal exposure actually means, with real examples from across the dataset.