Gascoyne, ND
Gascoyne, ND's wildfire risk, in USFS's own numbers
Gascoyne sits at the 39th percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 42 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Gascoyne at the 37th percentile, close to its 39th-percentile risk score.
Where Gascoyne's buildings actually sit
Gascoyne rates 57.1% Minimal exposure against just 42.9% Direct and 0% Indirect — of 42 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Gascoyne against the rest of the country
Gascoyne's 39th national percentile looks worse in isolation than its 24th ranking inside North Dakota does — this place is on the milder end for its own state, by 15 points. Among the 31,521 US communities USFS scores, Gascoyne ranks 19,137 for wildfire risk (1 is highest) and 31,119 by building count (1 is largest). Within North Dakota alone, it ranks 305 of 402 places by risk. See the full county-by-county picture for North Dakota on its state page.
Shopping for coverage in Gascoyne
Gascoyne's moderate wildfire rating (39th 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
Gascoyne's 57.1% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Gascoyne's figures come from
Every one of the two percentiles behind Gascoyne's 19,137-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Gascoyne's dominant minimal exposure actually means, with real examples from across the dataset.