Stoney Point, OK
Stoney Point, OK's wildfire risk, in USFS's own numbers
USFS scores Stoney Point at the 95th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 211 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Stoney Point at the 97th percentile, close to its 95th-percentile risk score.
Stoney Point's building exposure, zone by zone
211 buildings are counted in Stoney Point, and 100% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Where Stoney Point ranks
Stoney Point ranks lower within Oklahoma (78th percentile statewide) than its 95th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Stoney Point ranks 1,643 for wildfire risk (1 is highest) and 24,452 by building count (1 is largest). Within Oklahoma alone, it ranks 186 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Stoney Point and the insurance market
Stoney Point's very high rating (95th 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
Stoney Point's 100% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Stoney Point's figures come from
Every one of the two percentiles behind Stoney Point's 1,643-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Stoney Point's dominant direct exposure actually means, with real examples from across the dataset.