Batesland, SD
Batesland, SD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Batesland at the 81st national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 84 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Batesland at the 79th 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 Batesland's buildings (100%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Batesland compares
Batesland scores 81st nationally and 81st within South Dakota — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Batesland ranks 6,029 for wildfire risk (1 is highest) and 29,743 by building count (1 is largest). Within South Dakota alone, it ranks 85 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
Batesland's very high rating (81st percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
Because 100% of Batesland's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Batesland's figures come from
Batesland's 81st-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 Batesland's dominant indirect exposure actually means, with real examples from across the dataset.