WildfireRiskFinder

Clio, MI

How exposed is Clio to wildfire?

Low
9thpercentile nationally

USFS's Wildfire Risk to Communities model puts Clio at the 9th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 1,176 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Clio's burn probability — fire likelihood with no building count factored in — sits at the 10th percentile nationally.

Where Clio's buildings actually sit

1,176Total buildings
11.8%Direct exposure
0%Indirect exposure
88.2%Minimal exposure

Only 11.8% of Clio's 1,176 buildings carry Direct exposure and 0% carry Indirect; the remaining 88.2% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.

Where Clio ranks

Compare Clio's two percentiles: 27th within Michigan, only 9th nationally — a gap of 19 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Clio ranks 28,744 for wildfire risk (1 is highest) and 10,788 by building count (1 is largest). Within Michigan alone, it ranks 541 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

Clio's low rating (9th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Lowering exposure, not just insuring around it

Even with 88.2% of Clio 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 Clio's figures come from

Clio's 9th-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 Clio's dominant minimal exposure actually means, with real examples from across the dataset.