Kistler, PA
Kistler, PA's wildfire risk, in USFS's own numbers
Kistler sits at the 44th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 159 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Kistler at the 43rd national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Kistler's building exposure, zone by zone
Indirect exposure is dominant in Kistler (79.9% of 159 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 20.1% sit in the Direct zone.
Where Kistler ranks
Compare Kistler's two percentiles: 71st within Pennsylvania, only 44th nationally — a gap of 27 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Kistler ranks 17,626 for wildfire risk (1 is highest) and 26,498 by building count (1 is largest). Within Pennsylvania alone, it ranks 582 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
At the 44th national percentile, Kistler rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Kistler
Kistler's 79.9% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Kistler's figures come from
Kistler's 44th-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 Kistler's dominant indirect exposure actually means, with real examples from across the dataset.