Waverly, SD
Waverly wildfire risk explained
Out of every US place USFS scores, Waverly lands at the 42nd percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 45 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Waverly's burn probability — fire likelihood with no building count factored in — sits at the 36th percentile nationally.
Where Waverly's buildings actually sit
Waverly rates 60% Minimal exposure against just 40% Direct and 0% Indirect — of 45 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Waverly compares
Inside South Dakota, Waverly sits at just the 16th percentile even though it scores 42nd 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, Waverly ranks 18,390 for wildfire risk (1 is highest) and 31,061 by building count (1 is largest). Within South Dakota alone, it ranks 369 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Shopping for coverage in Waverly
Waverly's elevated wildfire rating (42nd 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.
Lowering exposure, not just insuring around it
Even with 60% of Waverly 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 Waverly's figures come from
Every one of the two percentiles behind Waverly's 18,390-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Waverly's dominant minimal exposure actually means, with real examples from across the dataset.