San Acacia, NM
San Acacia wildfire risk explained
USFS's Wildfire Risk to Communities model puts San Acacia at the 59th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 45 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks San Acacia at the 57th national percentile — 2 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 93.3% of San Acacia's buildings as Direct exposure, higher than its 6.7% Indirect share and far above its 0% Minimal share — a profile where 42 structures sit close enough to vegetation that lot clearing matters most.
How San Acacia compares
San Acacia's 59th national percentile looks worse in isolation than its 29th 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, San Acacia ranks 12,809 for wildfire risk (1 is highest) and 31,054 by building count (1 is largest). Within New Mexico alone, it ranks 351 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
At the 59th national percentile, San Acacia rates elevated 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 San Acacia
San Acacia's 93.3% 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 San Acacia's figures come from
San Acacia's 59th-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 San Acacia's dominant direct exposure actually means, with real examples from across the dataset.