Vass, NC
How exposed is Vass to wildfire?
Vass's 588 buildings earn a 82nd-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Vass at the 83rd national percentile — 1 points above 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 76.4% of Vass's buildings as Direct exposure, higher than its 23.6% Indirect share and far above its 0% Minimal share — a profile where 449 structures sit close enough to vegetation that lot clearing matters most.
Where Vass ranks
Vass's risk sits at a similar level relative to North Carolina (88th percentile statewide) as it does nationally (82nd) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Vass ranks 5,741 for wildfire risk (1 is highest) and 15,992 by building count (1 is largest). Within North Carolina alone, it ranks 92 of 772 places by risk. See the full county-by-county picture for North Carolina on its state page.
Shopping for coverage in Vass
Vass's 82nd-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.
Lowering exposure, not just insuring around it
With 76.4% of Vass 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 Vass's figures come from
Vass's 82nd-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 Vass's dominant direct exposure actually means, with real examples from across the dataset.