Flying Hills, PA
Flying Hills, PA's wildfire risk, in USFS's own numbers
USFS scores Flying Hills at the 20th national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 245 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Flying Hills at the 17th national percentile — 3 points below its risk-to-structures score, a gap driven by how much is actually built there.
Flying Hills's building exposure, zone by zone
USFS classifies 59.6% of Flying Hills's buildings as Direct exposure, higher than its 40.4% Indirect share and far above its 0% Minimal share — a profile where 146 structures sit close enough to vegetation that lot clearing matters most.
Where Flying Hills ranks
Flying Hills scores 20th nationally and 22nd within Pennsylvania — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Flying Hills ranks 25,257 for wildfire risk (1 is highest) and 23,311 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,562 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Shopping for coverage in Flying Hills
Flying Hills's low wildfire rating (20th 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.
Hardening a home in Flying Hills
Because Direct exposure dominates in Flying Hills (59.6%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Flying Hills's figures come from
Flying Hills's 20th-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 Flying Hills's dominant direct exposure actually means, with real examples from across the dataset.