Jay, OK
Jay, OK's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Jay lands at the 89th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 1,466 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Jay's burn probability — fire likelihood with no building count factored in — sits at the 91st percentile nationally.
What "at risk" means for the buildings here
Indirect exposure is dominant in Jay (52.5% of 1,466 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 47.5% sit in the Direct zone.
Where Jay ranks
Inside Oklahoma, Jay sits at just the 45th percentile even though it scores 89th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Jay ranks 3,346 for wildfire risk (1 is highest) and 9,330 by building count (1 is largest). Within Oklahoma alone, it ranks 460 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
What this risk score means for insurance
Jay's 89th-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
Because 52.5% of Jay's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Jay's figures come from
Jay's 89th-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 Jay's dominant indirect exposure actually means, with real examples from across the dataset.