Maple Grove, MI
Maple Grove, MI's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Maple Grove at the 19th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 118 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 Maple Grove at the 20th percentile, close to its 19th-percentile risk score.
What "at risk" means for the buildings here
Direct exposure dominates in Maple Grove: 97.5% of its 118 buildings, versus 0% Indirect and 2.5% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Maple Grove compares
Within Michigan, Maple Grove ranks higher (65th percentile) than it does nationally (19th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Maple Grove ranks 25,405 for wildfire risk (1 is highest) and 28,249 by building count (1 is largest). Within Michigan alone, it ranks 261 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Maple Grove and the insurance market
Maple Grove's low wildfire rating (19th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Lowering exposure, not just insuring around it
Maple Grove's 97.5% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Maple Grove's figures come from
Maple Grove's 19th-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 Maple Grove's dominant direct exposure actually means, with real examples from across the dataset.