WildfireRiskFinder

Gobles, MI

How exposed is Gobles to wildfire?

Low
15thpercentile nationally

Gobles's 431 buildings earn a 15th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Gobles at the 15th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.

What "at risk" means for the buildings here

431Total buildings
27.8%Direct exposure
72.2%Indirect exposure
0%Minimal exposure

Most of Gobles's buildings (72.2%) 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.

Gobles against the rest of the country

Compare Gobles's two percentiles: 51st within Michigan, only 15th nationally — a gap of 36 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Gobles ranks 26,887 for wildfire risk (1 is highest) and 18,627 by building count (1 is largest). Within Michigan alone, it ranks 368 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.

Shopping for coverage in Gobles

Gobles's low rating (15th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Gobles

Because 72.2% of Gobles'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 Gobles's figures come from

The methodology guide shows exactly how USFS turned 431 counted buildings into the percentiles shown above for Gobles. The exposure-zones guide covers what Gobles's dominant indirect exposure actually means, with real examples from across the dataset.