Paxton, FL
Paxton wildfire risk explained
USFS scores Paxton at the 76th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 529 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Paxton at the 77th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Paxton's building exposure, zone by zone
529 buildings are counted in Paxton, and 91.9% 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.
Paxton against the rest of the country
Inside Florida, Paxton sits at just the 38th percentile even though it scores 76th 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, Paxton ranks 7,654 for wildfire risk (1 is highest) and 16,875 by building count (1 is largest). Within Florida alone, it ranks 593 of 955 places by risk. See the full county-by-county picture for Florida on its state page.
Shopping for coverage in Paxton
Paxton's 76th-percentile, 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.
What would actually reduce this score
Because Direct exposure dominates in Paxton (91.9%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Paxton's figures come from
Every one of the two percentiles behind Paxton's 7,654-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Paxton's dominant direct exposure actually means, with real examples from across the dataset.