Monetta, SC
Monetta wildfire risk explained
USFS scores Monetta at the 60th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 193 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Monetta at the 61st percentile, close to its 60th-percentile risk score.
Monetta's building exposure, zone by zone
Of Monetta's 193 counted buildings, 83.9% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Monetta against the rest of the country
Inside South Carolina, Monetta sits at just the 18th percentile even though it scores 60th 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, Monetta ranks 12,776 for wildfire risk (1 is highest) and 25,134 by building count (1 is largest). Within South Carolina alone, it ranks 390 of 474 places by risk. See the full county-by-county picture for South Carolina on its state page.
Monetta and the insurance market
At the 60th national percentile, Monetta rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
With 83.9% of Monetta in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Monetta's figures come from
Monetta's 60th-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 Monetta's dominant direct exposure actually means, with real examples from across the dataset.