Jugtown, PA
Jugtown, PA's wildfire risk, in USFS's own numbers
Jugtown sits at the 52nd 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 44 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Jugtown at the 50th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Of Jugtown's 44 counted buildings, 72.7% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Jugtown against the rest of the country
Compare Jugtown's two percentiles: 86th within Pennsylvania, only 52nd nationally — a gap of 34 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Jugtown ranks 15,203 for wildfire risk (1 is highest) and 31,077 by building count (1 is largest). Within Pennsylvania alone, it ranks 288 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Jugtown and the insurance market
At the 52nd national percentile, Jugtown rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
Jugtown's 72.7% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Jugtown's figures come from
Jugtown's 52nd-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 Jugtown's dominant direct exposure actually means, with real examples from across the dataset.