Newell, PA
How exposed is Newell to wildfire?
Out of every US place USFS scores, Newell lands at the 36th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 332 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Newell at the 38th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Newell's building exposure, zone by zone
USFS puts 73.2% of Newell's 332 buildings in the Indirect exposure zone, versus 25.3% Direct and 1.5% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
How Newell compares
Newell's 52nd-percentile standing inside Pennsylvania outpaces its 36th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Newell ranks 20,247 for wildfire risk (1 is highest) and 20,774 by building count (1 is largest). Within Pennsylvania alone, it ranks 959 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
Newell's moderate wildfire rating (36th 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.
What would actually reduce this score
Newell's 73.2% 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 Newell's figures come from
Newell's 36th-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 Newell's dominant indirect exposure actually means, with real examples from across the dataset.