Milfay, OK
Milfay wildfire risk explained
Milfay sits at the 96th 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 98 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Milfay's burn probability — fire likelihood with no building count factored in — sits at the 97th percentile nationally.
Where Milfay's buildings actually sit
98 buildings are counted in Milfay, and 90.8% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Where Milfay ranks
Milfay's risk sits at a similar level relative to Oklahoma (86th percentile statewide) as it does nationally (96th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Milfay ranks 1,267 for wildfire risk (1 is highest) and 29,149 by building count (1 is largest). Within Oklahoma alone, it ranks 120 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Shopping for coverage in Milfay
Milfay's 96th-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 Milfay
With 90.8% of Milfay in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Milfay's figures come from
Milfay's 96th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Milfay's dominant direct exposure actually means, with real examples from across the dataset.