Viborg, SD
Viborg wildfire risk explained
Viborg's 564 buildings earn a 37th-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Viborg at the 35th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Most of Viborg's buildings (84.4% of 564) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.
Viborg against the rest of the country
Inside South Dakota, Viborg sits at just the 10th percentile even though it scores 37th 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, Viborg ranks 19,836 for wildfire risk (1 is highest) and 16,346 by building count (1 is largest). Within South Dakota alone, it ranks 394 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
What this risk score means for insurance
Viborg's moderate wildfire rating (37th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Hardening a home in Viborg
Even with 84.4% of Viborg outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Viborg's figures come from
Every one of the two percentiles behind Viborg's 19,836-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Viborg's dominant minimal exposure actually means, with real examples from across the dataset.