Ramah, NM
Ramah wildfire risk explained
Ramah sits at the 76th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 420 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Ramah at the 69th national percentile — 7 points below its risk-to-structures score, a gap driven by how much is actually built there.
Ramah's building exposure, zone by zone
USFS classifies 93.3% of Ramah's buildings as Direct exposure, higher than its 6.7% Indirect share and far above its 0% Minimal share — a profile where 392 structures sit close enough to vegetation that lot clearing matters most.
How Ramah compares
Ramah's 76th national percentile looks worse in isolation than its 48th ranking inside New Mexico does — this place is on the milder end for its own state, by 28 points. Among the 31,521 US communities USFS scores, Ramah ranks 7,571 for wildfire risk (1 is highest) and 18,821 by building count (1 is largest). Within New Mexico alone, it ranks 261 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
Ramah's 76th-percentile, 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 Ramah
Because Direct exposure dominates in Ramah (93.3%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Ramah's figures come from
Every one of the two percentiles behind Ramah's 7,571-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Ramah's dominant direct exposure actually means, with real examples from across the dataset.