Brent, OK
Brent, OK's wildfire risk, in USFS's own numbers
USFS scores Brent at the 95th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 609 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Brent's burn probability — fire likelihood with no building count factored in — sits at the 97th percentile nationally.
Brent's building exposure, zone by zone
99.8% of Brent's 609 buildings sit in USFS's Direct exposure zone, roughly 608 structures close enough to burnable vegetation for flame contact, not just embers — 0.2% fall in the Indirect, ember-only zone and 0% are Minimal.
Brent against the rest of the country
Brent's 95th national percentile looks worse in isolation than its 78th ranking inside Oklahoma does — this place is on the milder end for its own state, by 17 points. Among the 31,521 US communities USFS scores, Brent ranks 1,640 for wildfire risk (1 is highest) and 15,706 by building count (1 is largest). Within Oklahoma alone, it ranks 183 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
Brent'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.
What would actually reduce this score
Brent's 99.8% 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 Brent's figures come from
The methodology guide shows exactly how USFS turned 609 counted buildings into the percentiles shown above for Brent. The exposure-zones guide covers what Brent's dominant direct exposure actually means, with real examples from across the dataset.