Sterling, OK
How exposed is Sterling to wildfire?
USFS scores Sterling at the 95th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 519 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Sterling at the 95th percentile, close to its 95th-percentile risk score.
Where Sterling's buildings actually sit
Indirect exposure is dominant in Sterling (58.2% of 519 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 41.8% sit in the Direct zone.
Where Sterling ranks
There's little gap between Sterling's 95th national percentile and its 81st percentile inside Oklahoma, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Sterling ranks 1,492 for wildfire risk (1 is highest) and 17,044 by building count (1 is largest). Within Oklahoma alone, it ranks 162 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Shopping for coverage in Sterling
Sterling'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.
Hardening a home in Sterling
With ember exposure the dominant pattern in Sterling (58.2% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Sterling's figures come from
Every one of the two percentiles behind Sterling's 1,492-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Sterling's dominant indirect exposure actually means, with real examples from across the dataset.