Fairacres, NM
Fairacres, NM's wildfire risk, in USFS's own numbers
Fairacres sits at the 43rd percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 600 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 Fairacres at the 41st percentile, close to its 43rd-percentile risk score.
Where Fairacres's buildings actually sit
Of Fairacres's 600 counted buildings, 66.3% carry Direct exposure and only 31.2% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Fairacres against the rest of the country
Inside New Mexico, Fairacres sits at just the 9th percentile even though it scores 43rd 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, Fairacres ranks 18,062 for wildfire risk (1 is highest) and 15,837 by building count (1 is largest). Within New Mexico alone, it ranks 450 of 495 places by risk. See the full county-by-county picture for New Mexico on its state page.
What this risk score means for insurance
Fairacres's elevated rating (43rd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Fairacres (66.3%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Fairacres's figures come from
Fairacres's 43rd-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Fairacres's dominant direct exposure actually means, with real examples from across the dataset.