Sanostee, NM
Sanostee, NM's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Sanostee lands at the 34th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 194 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 Sanostee at the 34th percentile, close to its 34th-percentile risk score.
Where Sanostee's buildings actually sit
USFS classifies 88.7% of Sanostee's buildings as Direct exposure, higher than its 11.3% Indirect share and far above its 0% Minimal share — a profile where 172 structures sit close enough to vegetation that lot clearing matters most.
Where Sanostee ranks
Sanostee's 34th national percentile looks worse in isolation than its 4th ranking inside New Mexico does — this place is on the milder end for its own state, by 30 points. Among the 31,521 US communities USFS scores, Sanostee ranks 20,860 for wildfire risk (1 is highest) and 25,090 by building count (1 is largest). Within New Mexico alone, it ranks 475 of 495 places by risk. See the full county-by-county picture for New Mexico on its state page.
Shopping for coverage in Sanostee
At the 34th national percentile, Sanostee rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Sanostee
With 88.7% of Sanostee in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Sanostee's figures come from
Every one of the two percentiles behind Sanostee's 20,860-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Sanostee's dominant direct exposure actually means, with real examples from across the dataset.