Shillington, PA
Shillington, PA's wildfire risk, in USFS's own numbers
Shillington sits at the 21st percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 1,899 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Shillington at the 19th percentile, close to its 21st-percentile risk score.
Where Shillington's buildings actually sit
Shillington rates 82.5% Minimal exposure against just 7.5% Direct and 10% Indirect — of 1,899 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Where Shillington ranks
There's little gap between Shillington's 21st national percentile and its 23rd percentile inside Pennsylvania, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Shillington ranks 24,881 for wildfire risk (1 is highest) and 7,771 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,534 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
Shillington's moderate rating (21st 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
Shillington's 82.5% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Shillington's figures come from
Shillington's 21st-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Shillington's dominant minimal exposure actually means, with real examples from across the dataset.