Cut Bank, MT
Cut Bank wildfire risk explained
Out of every US place USFS scores, Cut Bank lands at the 71st percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 1,908 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Cut Bank's burn probability — fire likelihood with no building count factored in — sits at the 73rd percentile nationally.
Cut Bank's building exposure, zone by zone
Most of Cut Bank's buildings (97.5%) 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.
How Cut Bank compares
Cut Bank's 71st national percentile looks worse in isolation than its 41st ranking inside Montana does — this place is on the milder end for its own state, by 30 points. Among the 31,521 US communities USFS scores, Cut Bank ranks 9,017 for wildfire risk (1 is highest) and 7,745 by building count (1 is largest). Within Montana alone, it ranks 280 of 475 places by risk. See the full county-by-county picture for Montana on its state page.
What this risk score means for insurance
Cut Bank's high rating (71st percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Cut Bank
Because 97.5% of Cut Bank'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 Cut Bank's figures come from
Every one of the two percentiles behind Cut Bank's 9,017-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Cut Bank's dominant indirect exposure actually means, with real examples from across the dataset.