Truchas, NM
Truchas wildfire risk explained
Out of every US place USFS scores, Truchas lands at the 97th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 684 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Truchas's burn probability — fire likelihood with no building count factored in — sits at the 94th percentile nationally.
Where Truchas's buildings actually sit
USFS classifies 87.3% of Truchas's buildings as Direct exposure, higher than its 12.7% Indirect share and far above its 0% Minimal share — a profile where 597 structures sit close enough to vegetation that lot clearing matters most.
Where Truchas ranks
Truchas scores 97th nationally and 92nd within New Mexico — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Truchas ranks 922 for wildfire risk (1 is highest) and 14,827 by building count (1 is largest). Within New Mexico alone, it ranks 45 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
Truchas's 97th-percentile, very high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Hardening a home in Truchas
Truchas's 87.3% 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 Truchas's figures come from
Every one of the two percentiles behind Truchas's 922-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Truchas's dominant direct exposure actually means, with real examples from across the dataset.