Chancellor, SD
Chancellor, SD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Chancellor at the 28th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 181 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Chancellor at the 27th percentile, close to its 28th-percentile risk score.
Chancellor's building exposure, zone by zone
Chancellor rates 71.3% Minimal exposure against just 28.7% Direct and 0% Indirect — of 181 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Chancellor compares
Chancellor ranks lower within South Dakota (3rd percentile statewide) than its 28th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Chancellor ranks 22,799 for wildfire risk (1 is highest) and 25,599 by building count (1 is largest). Within South Dakota alone, it ranks 427 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
Chancellor's moderate wildfire rating (28th 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 Chancellor
With 71.3% of buildings rated Minimal exposure, Chancellor gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Chancellor's figures come from
Every one of the two percentiles behind Chancellor's 22,799-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Chancellor's dominant minimal exposure actually means, with real examples from across the dataset.