South Waverly, PA
South Waverly wildfire risk explained
South Waverly's 604 buildings earn a 18th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts South Waverly at the 18th percentile, close to its 18th-percentile risk score.
South Waverly's building exposure, zone by zone
Most of South Waverly's buildings (46.9%) 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.
South Waverly against the rest of the country
South Waverly's risk sits at a similar level relative to Pennsylvania (19th percentile statewide) as it does nationally (18th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, South Waverly ranks 25,860 for wildfire risk (1 is highest) and 15,795 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,615 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
South Waverly and the insurance market
South Waverly's low rating (18th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
With ember exposure the dominant pattern in South Waverly (46.9% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where South Waverly's figures come from
Every one of the two percentiles behind South Waverly's 25,860-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what South Waverly's dominant indirect exposure actually means, with real examples from across the dataset.