Manteo, NC
Manteo wildfire risk explained
USFS scores Manteo at the 96th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 1,120 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Manteo's burn probability — fire likelihood with no building count factored in — sits at the 92nd percentile nationally.
Where Manteo's buildings actually sit
USFS classifies 57.6% of Manteo's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 19.8% Direct and 22.6% Indirect — a landscape-level risk rather than a building-by-building one.
How Manteo compares
There's little gap between Manteo's 96th national percentile and its 100th percentile inside North Carolina, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Manteo ranks 1,201 for wildfire risk (1 is highest) and 11,154 by building count (1 is largest). Within North Carolina alone, it ranks 5 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
Manteo's 96th-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 Manteo
Manteo's 57.6% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Manteo's figures come from
Manteo's 96th-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 Manteo's dominant minimal exposure actually means, with real examples from across the dataset.