Oakley, KS
Oakley, KS's wildfire risk, in USFS's own numbers
USFS scores Oakley at the 42nd national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 1,515 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Oakley's burn probability — fire likelihood with no building count factored in — sits at the 40th percentile nationally.
Where Oakley's buildings actually sit
Oakley rates 94.7% Minimal exposure against just 5% Direct and 0.3% Indirect — of 1,515 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Oakley against the rest of the country
Inside Kansas, Oakley sits at just the 19th percentile even though it scores 42nd 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, Oakley ranks 18,148 for wildfire risk (1 is highest) and 9,100 by building count (1 is largest). Within Kansas alone, it ranks 588 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
What this risk score means for insurance
Oakley's elevated wildfire rating (42nd percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Lowering exposure, not just insuring around it
Even with 94.7% of Oakley outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Oakley's figures come from
Every one of the two percentiles behind Oakley's 18,148-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Oakley's dominant minimal exposure actually means, with real examples from across the dataset.