Moffett, OK
How exposed is Moffett to wildfire?
Moffett sits at the 85th percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 101 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Moffett's burn probability — fire likelihood with no building count factored in — sits at the 88th percentile nationally.
What "at risk" means for the buildings here
Most of Moffett's buildings (75.3% of 101) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.
How Moffett compares
Inside Oklahoma, Moffett sits at just the 31st percentile even though it scores 85th 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, Moffett ranks 4,859 for wildfire risk (1 is highest) and 29,007 by building count (1 is largest). Within Oklahoma alone, it ranks 576 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Moffett and the insurance market
At the 85th percentile nationally, Moffett carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
What would actually reduce this score
With 75.3% of buildings rated Minimal exposure, Moffett gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Moffett's figures come from
Every one of the two percentiles behind Moffett's 4,859-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Moffett's dominant minimal exposure actually means, with real examples from across the dataset.