Pray, MT
Pray wildfire risk explained
USFS's Wildfire Risk to Communities model puts Pray at the 95th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 1,032 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Pray at the 94th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 98.3% of Pray's buildings as Direct exposure, higher than its 1.7% Indirect share and far above its 0% Minimal share — a profile where 1,014 structures sit close enough to vegetation that lot clearing matters most.
Where Pray ranks
Pray's risk sits at a similar level relative to Montana (88th percentile statewide) as it does nationally (95th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Pray ranks 1,606 for wildfire risk (1 is highest) and 11,712 by building count (1 is largest). Within Montana alone, it ranks 57 of 475 places by risk. See the full county-by-county picture for Montana on its state page.
Shopping for coverage in Pray
Pray's 95th-percentile, very high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Hardening a home in Pray
Pray's 98.3% 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 Pray's figures come from
The methodology guide shows exactly how USFS turned 1,032 counted buildings into the percentiles shown above for Pray. The exposure-zones guide covers what Pray's dominant direct exposure actually means, with real examples from across the dataset.