Denmark, SC
How exposed is Denmark to wildfire?
USFS scores Denmark at the 68th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 1,628 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Denmark at the 67th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Denmark's building exposure, zone by zone
USFS classifies 56.8% of Denmark's buildings as Direct exposure, higher than its 43.2% Indirect share and far above its 0% Minimal share — a profile where 925 structures sit close enough to vegetation that lot clearing matters most.
Where Denmark ranks
Denmark's 68th national percentile looks worse in isolation than its 47th ranking inside South Carolina does — this place is on the milder end for its own state, by 21 points. Among the 31,521 US communities USFS scores, Denmark ranks 10,222 for wildfire risk (1 is highest) and 8,660 by building count (1 is largest). Within South Carolina alone, it ranks 253 of 474 places by risk. See the full county-by-county picture for South Carolina on its state page.
Shopping for coverage in Denmark
At the 68th percentile nationally, Denmark 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.
Hardening a home in Denmark
Denmark's 56.8% 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 Denmark's figures come from
The methodology guide shows exactly how USFS turned 1,628 counted buildings into the percentiles shown above for Denmark. The exposure-zones guide covers what Denmark's dominant direct exposure actually means, with real examples from across the dataset.