Green Sea, SC
How exposed is Green Sea to wildfire?
USFS's Wildfire Risk to Communities model puts Green Sea at the 65th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 79 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Green Sea's burn probability — fire likelihood with no building count factored in — sits at the 63rd percentile nationally.
What "at risk" means for the buildings here
79.8% of Green Sea's 79 buildings sit in USFS's Direct exposure zone, roughly 63 structures close enough to burnable vegetation for flame contact, not just embers — 20.3% fall in the Indirect, ember-only zone and 0% are Minimal.
Where Green Sea ranks
Green Sea ranks lower within South Carolina (35th percentile statewide) than its 65th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Green Sea ranks 11,071 for wildfire risk (1 is highest) and 29,944 by building count (1 is largest). Within South Carolina alone, it ranks 308 of 474 places by risk. See the full county-by-county picture for South Carolina on its state page.
What this risk score means for insurance
At the 65th percentile nationally, Green Sea carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Green Sea (79.8%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Green Sea's figures come from
Green Sea's 65th-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 Green Sea's dominant direct exposure actually means, with real examples from across the dataset.