Starr, SC
Starr wildfire risk explained
Starr sits at the 64th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 189 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Starr's burn probability — fire likelihood with no building count factored in — sits at the 68th percentile nationally.
Starr's building exposure, zone by zone
189 buildings are counted in Starr, and 85.2% 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.
Starr against the rest of the country
Starr ranks lower within South Carolina (32nd percentile statewide) than its 64th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Starr ranks 11,232 for wildfire risk (1 is highest) and 25,295 by building count (1 is largest). Within South Carolina alone, it ranks 323 of 474 places by risk. See the full county-by-county picture for South Carolina on its state page.
Starr and the insurance market
Starr's 64th-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 Starr (85.2%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Starr's figures come from
Every one of the two percentiles behind Starr's 11,232-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Starr's dominant direct exposure actually means, with real examples from across the dataset.