Nanticoke, MD
Nanticoke, MD's wildfire risk, in USFS's own numbers
Nanticoke sits at the 98th percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 295 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Nanticoke's burn probability — fire likelihood with no building count factored in — sits at the 97th percentile nationally.
Where Nanticoke's buildings actually sit
Of Nanticoke's 295 counted buildings, 81.7% carry Direct exposure and only 6.1% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
How Nanticoke compares
There's little gap between Nanticoke's 98th national percentile and its 99th percentile inside Maryland, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Nanticoke ranks 758 for wildfire risk (1 is highest) and 21,757 by building count (1 is largest). Within Maryland alone, it ranks 4 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
What this risk score means for insurance
Nanticoke's very high rating (98th 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.
What would actually reduce this score
Because Direct exposure dominates in Nanticoke (81.7%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Nanticoke's figures come from
Every one of the two percentiles behind Nanticoke's 758-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Nanticoke's dominant direct exposure actually means, with real examples from across the dataset.