Jennings, OK
Jennings, OK's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Jennings at the 95th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 282 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Jennings's burn probability — fire likelihood with no building count factored in — sits at the 97th percentile nationally.
Where Jennings's buildings actually sit
282 buildings are counted in Jennings, and 53.2% of them are Indirect exposure — ember-driven risk rather than the 46.8% in Direct exposure or the 0% rated Minimal.
Jennings against the rest of the country
Jennings's risk sits at a similar level relative to Oklahoma (81st percentile statewide) as it does nationally (95th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Jennings ranks 1,521 for wildfire risk (1 is highest) and 22,130 by building count (1 is largest). Within Oklahoma alone, it ranks 165 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Shopping for coverage in Jennings
Jennings's 95th-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 53.2% of Jennings'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 Jennings's figures come from
Every one of the two percentiles behind Jennings's 1,521-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Jennings's dominant indirect exposure actually means, with real examples from across the dataset.