Landis, NC
Landis wildfire risk explained
USFS's Wildfire Risk to Communities model puts Landis at the 51st national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 2,097 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 Landis at the 55th percentile, close to its 51st-percentile risk score.
What "at risk" means for the buildings here
Direct exposure dominates in Landis: 48.3% of its 2,097 buildings, versus 45.1% Indirect and 6.6% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Landis against the rest of the country
Landis's risk sits at a similar level relative to North Carolina (41st percentile statewide) as it does nationally (51st) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Landis ranks 15,450 for wildfire risk (1 is highest) and 7,201 by building count (1 is largest). Within North Carolina alone, it ranks 458 of 772 places by risk. See the full county-by-county picture for North Carolina on its state page.
What this risk score means for insurance
Landis's elevated wildfire rating (51st percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
What would actually reduce this score
With 48.3% of Landis 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 Landis's figures come from
Every one of the two percentiles behind Landis's 15,450-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Landis's dominant direct exposure actually means, with real examples from across the dataset.