Dill City, OK
Dill City wildfire risk explained
USFS's Wildfire Risk to Communities model puts Dill City at the 59th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 383 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Dill City at the 53rd percentile, close to its 59th-percentile risk score.
Where Dill City's buildings actually sit
USFS classifies 70% of Dill City's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 30% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
How Dill City compares
Inside Oklahoma, Dill City sits at just the 8th percentile even though it scores 59th 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, Dill City ranks 13,059 for wildfire risk (1 is highest) and 19,571 by building count (1 is largest). Within Oklahoma alone, it ranks 767 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Shopping for coverage in Dill City
Dill City's elevated wildfire rating (59th 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 70% of Dill City 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 Dill City's figures come from
The methodology guide shows exactly how USFS turned 383 counted buildings into the percentiles shown above for Dill City. The exposure-zones guide covers what Dill City's dominant minimal exposure actually means, with real examples from across the dataset.