Kaylor, SD
How exposed is Kaylor to wildfire?
USFS scores Kaylor at the 49th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 116 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 Kaylor at the 48th percentile, close to its 49th-percentile risk score.
Kaylor's building exposure, zone by zone
65.5% of Kaylor's 116 buildings sit in USFS's Direct exposure zone, roughly 76 structures close enough to burnable vegetation for flame contact, not just embers — 0% fall in the Indirect, ember-only zone and 34.5% are Minimal.
Kaylor against the rest of the country
Inside South Dakota, Kaylor sits at just the 27th percentile even though it scores 49th 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, Kaylor ranks 16,023 for wildfire risk (1 is highest) and 28,384 by building count (1 is largest). Within South Dakota alone, it ranks 319 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Shopping for coverage in Kaylor
Kaylor's elevated rating (49th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Lowering exposure, not just insuring around it
With 65.5% of Kaylor 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 Kaylor's figures come from
Every one of the two percentiles behind Kaylor's 16,023-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Kaylor's dominant direct exposure actually means, with real examples from across the dataset.