Clio, SC
Clio wildfire risk explained
USFS scores Clio at the 81st national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 458 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Clio at the 81st national percentile — 0 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
USFS classifies 62.7% of Clio's buildings as Direct exposure, higher than its 37.3% Indirect share and far above its 0% Minimal share — a profile where 287 structures sit close enough to vegetation that lot clearing matters most.
Clio against the rest of the country
Clio scores 81st nationally and 87th within South Carolina — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Clio ranks 6,058 for wildfire risk (1 is highest) and 18,114 by building count (1 is largest). Within South Carolina alone, it ranks 63 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 81st percentile nationally, Clio carries the very 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
Clio's 62.7% 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 Clio's figures come from
Every one of the two percentiles behind Clio's 6,058-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Clio's dominant direct exposure actually means, with real examples from across the dataset.