Mountain Park, OK
Mountain Park wildfire risk explained
Mountain Park sits at the 77th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 330 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Mountain Park's burn probability — fire likelihood with no building count factored in — sits at the 72nd percentile nationally.
Where Mountain Park's buildings actually sit
80.9% of Mountain Park's 330 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 19.1% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Where Mountain Park ranks
Inside Oklahoma, Mountain Park sits at just the 16th percentile even though it scores 77th 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, Mountain Park ranks 7,131 for wildfire risk (1 is highest) and 20,831 by building count (1 is largest). Within Oklahoma alone, it ranks 699 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Mountain Park and the insurance market
Mountain Park's high rating (77th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
Because 80.9% of Mountain Park's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Mountain Park's figures come from
The methodology guide shows exactly how USFS turned 330 counted buildings into the percentiles shown above for Mountain Park. The exposure-zones guide covers what Mountain Park's dominant indirect exposure actually means, with real examples from across the dataset.