Libby, MT
How exposed is Libby to wildfire?
USFS scores Libby at the 87th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 1,855 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Libby at the 87th percentile, close to its 87th-percentile risk score.
What "at risk" means for the buildings here
Most of Libby's buildings (92%) 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.
Where Libby ranks
Inside Montana, Libby sits at just the 68th percentile even though it scores 87th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Libby ranks 4,038 for wildfire risk (1 is highest) and 7,897 by building count (1 is largest). Within Montana alone, it ranks 154 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
Libby's very high rating (87th 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 Libby
Libby's 92% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Libby's figures come from
Every one of the two percentiles behind Libby's 4,038-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Libby's dominant indirect exposure actually means, with real examples from across the dataset.