Sheakleyville, PA
Sheakleyville wildfire risk explained
USFS's Wildfire Risk to Communities model puts Sheakleyville at the 38th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 115 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 Sheakleyville at the 39th percentile, close to its 38th-percentile risk score.
Sheakleyville's building exposure, zone by zone
115 buildings are counted in Sheakleyville, and 67.8% of them are Indirect exposure — ember-driven risk rather than the 32.2% in Direct exposure or the 0% rated Minimal.
Sheakleyville against the rest of the country
Within Pennsylvania, Sheakleyville ranks higher (57th percentile) than it does nationally (38th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Sheakleyville ranks 19,585 for wildfire risk (1 is highest) and 28,424 by building count (1 is largest). Within Pennsylvania alone, it ranks 859 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
What this risk score means for insurance
Sheakleyville's moderate wildfire rating (38th 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.
What would actually reduce this score
With ember exposure the dominant pattern in Sheakleyville (67.8% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Sheakleyville's figures come from
Every one of the two percentiles behind Sheakleyville's 19,585-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Sheakleyville's dominant indirect exposure actually means, with real examples from across the dataset.