WildfireRiskFinder

Climax, MI

How exposed is Climax to wildfire?

Low
6thpercentile nationally

USFS's Wildfire Risk to Communities model puts Climax at the 6th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 413 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 Climax at the 6th percentile, close to its 6th-percentile risk score.

Where Climax's buildings actually sit

413Total buildings
39.7%Direct exposure
0%Indirect exposure
60.3%Minimal exposure

Most of Climax's buildings (60.3% of 413) 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.

Where Climax ranks

Climax scores 6th nationally and 15th 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, Climax ranks 29,660 for wildfire risk (1 is highest) and 18,969 by building count (1 is largest). Within Michigan alone, it ranks 633 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

At the 6th national percentile, Climax rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

Hardening a home in Climax

Even with 60.3% of Climax 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 Climax's figures come from

Climax's 6th-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 Climax's dominant minimal exposure actually means, with real examples from across the dataset.