Haywood, OK
How exposed is Haywood to wildfire?
Out of every US place USFS scores, Haywood lands at the 94th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 137 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Haywood at the 96th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
137 buildings are counted in Haywood, and 92% 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 Haywood ranks
Inside Oklahoma, Haywood sits at just the 73rd percentile even though it scores 94th 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, Haywood ranks 1,895 for wildfire risk (1 is highest) and 27,413 by building count (1 is largest). Within Oklahoma alone, it ranks 225 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Shopping for coverage in Haywood
Haywood's 94th-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 Haywood
Haywood's 92% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Haywood's figures come from
The methodology guide shows exactly how USFS turned 137 counted buildings into the percentiles shown above for Haywood. The exposure-zones guide covers what Haywood's dominant direct exposure actually means, with real examples from across the dataset.