Nenahnezad, NM
How exposed is Nenahnezad to wildfire?
USFS scores Nenahnezad at the 57th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 318 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Nenahnezad at the 58th national percentile — 1 points above 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 75.8% of Nenahnezad's buildings as Direct exposure, higher than its 24.2% Indirect share and far above its 0% Minimal share — a profile where 241 structures sit close enough to vegetation that lot clearing matters most.
Where Nenahnezad ranks
Nenahnezad's 57th national percentile looks worse in isolation than its 25th ranking inside New Mexico does — this place is on the milder end for its own state, by 32 points. Among the 31,521 US communities USFS scores, Nenahnezad ranks 13,555 for wildfire risk (1 is highest) and 21,157 by building count (1 is largest). Within New Mexico alone, it ranks 372 of 495 places by risk. See the full county-by-county picture for New Mexico on its state page.
Shopping for coverage in Nenahnezad
Nenahnezad's elevated rating (57th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Nenahnezad's 75.8% 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 Nenahnezad's figures come from
Every one of the two percentiles behind Nenahnezad's 13,555-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Nenahnezad's dominant direct exposure actually means, with real examples from across the dataset.