Sewall's Point, FL
Sewall's Point wildfire risk explained
Sewall's Point's 1,068 buildings earn a 61st-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Sewall's Point's burn probability — fire likelihood with no building count factored in — sits at the 63rd percentile nationally.
Where Sewall's Point's buildings actually sit
USFS classifies 85.2% of Sewall's Point's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 14.8% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
How Sewall's Point compares
Sewall's Point ranks lower within Florida (16th percentile statewide) than its 61st national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Sewall's Point ranks 12,379 for wildfire risk (1 is highest) and 11,470 by building count (1 is largest). Within Florida alone, it ranks 808 of 955 places by risk. See the full county-by-county picture for Florida on its state page.
What this risk score means for insurance
Sewall's Point's 61st-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
Sewall's Point's 85.2% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Sewall's Point's figures come from
The methodology guide shows exactly how USFS turned 1,068 counted buildings into the percentiles shown above for Sewall's Point. The exposure-zones guide covers what Sewall's Point's dominant minimal exposure actually means, with real examples from across the dataset.