Beltrami, MN
Beltrami wildfire risk explained
Out of every US place USFS scores, Beltrami lands at the 44th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 141 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Beltrami at the 42nd percentile, close to its 44th-percentile risk score.
What "at risk" means for the buildings here
Only 31.9% of Beltrami's 141 buildings carry Direct exposure and 0% carry Indirect; the remaining 68.1% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
Where Beltrami ranks
Beltrami's 61st-percentile standing inside Minnesota outpaces its 44th 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, Beltrami ranks 17,799 for wildfire risk (1 is highest) and 27,255 by building count (1 is largest). Within Minnesota alone, it ranks 352 of 914 places by risk. See the full county-by-county picture for Minnesota on its state page.
Beltrami and the insurance market
Beltrami's elevated wildfire rating (44th 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
With 68.1% of buildings rated Minimal exposure, Beltrami gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Beltrami's figures come from
The methodology guide shows exactly how USFS turned 141 counted buildings into the percentiles shown above for Beltrami. The exposure-zones guide covers what Beltrami's dominant minimal exposure actually means, with real examples from across the dataset.