WildfireRiskFinder

Haring, MI

Haring wildfire risk explained

Moderate
28thpercentile nationally

Haring's 449 buildings earn a 28th-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Haring at the 28th percentile, close to its 28th-percentile risk score.

What "at risk" means for the buildings here

449Total buildings
24.9%Direct exposure
73.7%Indirect exposure
1.3%Minimal exposure

73.7% of Haring's 449 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 24.9% Direct and 1.3% Minimal. Vent screens and roofing material matter more here than lot clearing alone.

Where Haring ranks

Haring's 80th-percentile standing inside Michigan outpaces its 28th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Haring ranks 22,629 for wildfire risk (1 is highest) and 18,298 by building count (1 is largest). Within Michigan alone, it ranks 153 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

Haring's moderate rating (28th 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

Because 73.7% of Haring'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 Haring's figures come from

The methodology guide shows exactly how USFS turned 449 counted buildings into the percentiles shown above for Haring. The exposure-zones guide covers what Haring's dominant indirect exposure actually means, with real examples from across the dataset.