Pymatuning South, PA
Pymatuning South, PA's wildfire risk, in USFS's own numbers
Pymatuning South's 573 buildings earn a 33rd-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Pymatuning South's burn probability — fire likelihood with no building count factored in — sits at the 34th percentile nationally.
Where Pymatuning South's buildings actually sit
USFS classifies 86% of Pymatuning South's buildings as Direct exposure, higher than its 13.6% Indirect share and far above its 0.4% Minimal share — a profile where 493 structures sit close enough to vegetation that lot clearing matters most.
How Pymatuning South compares
There's little gap between Pymatuning South's 33rd national percentile and its 45th percentile inside Pennsylvania, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Pymatuning South ranks 21,098 for wildfire risk (1 is highest) and 16,200 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,091 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Pymatuning South and the insurance market
Pymatuning South's moderate rating (33rd 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
Because Direct exposure dominates in Pymatuning South (86%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Pymatuning South's figures come from
Pymatuning South's 33rd-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 Pymatuning South's dominant direct exposure actually means, with real examples from across the dataset.