Nara Visa, NM
Nara Visa wildfire risk explained
USFS's Wildfire Risk to Communities model puts Nara Visa at the 87th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 163 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Nara Visa at the 87th percentile, close to its 87th-percentile risk score.
Nara Visa's building exposure, zone by zone
Direct exposure dominates in Nara Visa: 70.6% of its 163 buildings, versus 29.5% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Nara Visa against the rest of the country
Nara Visa ranks lower within New Mexico (66th percentile statewide) than its 87th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Nara Visa ranks 4,227 for wildfire risk (1 is highest) and 26,292 by building count (1 is largest). Within New Mexico alone, it ranks 167 of 495 places by risk. See the full county-by-county picture for New Mexico on its state page.
Shopping for coverage in Nara Visa
Nara Visa's very high rating (87th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Nara Visa
With 70.6% of Nara Visa in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Nara Visa's figures come from
Every one of the two percentiles behind Nara Visa's 4,227-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Nara Visa's dominant direct exposure actually means, with real examples from across the dataset.