Nelagoney, OK
Nelagoney, OK's wildfire risk, in USFS's own numbers
Nelagoney sits at the 96th 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 71 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Nelagoney at the 98th percentile, close to its 96th-percentile risk score.
What "at risk" means for the buildings here
USFS classifies 100% of Nelagoney's buildings as Direct exposure, higher than its 0% Indirect share and far above its 0% Minimal share — a profile where 71 structures sit close enough to vegetation that lot clearing matters most.
How Nelagoney compares
Nelagoney's risk sits at a similar level relative to Oklahoma (88th percentile statewide) as it does nationally (96th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Nelagoney ranks 1,174 for wildfire risk (1 is highest) and 30,257 by building count (1 is largest). Within Oklahoma alone, it ranks 99 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Nelagoney and the insurance market
Nelagoney's very high rating (96th 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 Nelagoney (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Nelagoney's figures come from
Every one of the two percentiles behind Nelagoney's 1,174-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Nelagoney's dominant direct exposure actually means, with real examples from across the dataset.