Mio, MI
Mio wildfire risk explained
USFS's Wildfire Risk to Communities model puts Mio at the 62nd national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 1,604 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Mio at the 61st national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 63.8% of Mio's buildings as Direct exposure, higher than its 36.2% Indirect share and far above its 0% Minimal share — a profile where 1,023 structures sit close enough to vegetation that lot clearing matters most.
Mio against the rest of the country
Within Michigan, Mio ranks higher (98th percentile) than it does nationally (62nd) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Mio ranks 11,915 for wildfire risk (1 is highest) and 8,755 by building count (1 is largest). Within Michigan alone, it ranks 15 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 62nd percentile nationally, Mio carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
What would actually reduce this score
With 63.8% of Mio in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Mio's figures come from
The methodology guide shows exactly how USFS turned 1,604 counted buildings into the percentiles shown above for Mio. The exposure-zones guide covers what Mio's dominant direct exposure actually means, with real examples from across the dataset.