Mattawana, PA
How exposed is Mattawana to wildfire?
Mattawana sits at the 43rd 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 182 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Mattawana's burn probability — fire likelihood with no building count factored in — sits at the 43rd percentile nationally.
What "at risk" means for the buildings here
53.3% of Mattawana's 182 buildings sit in USFS's Direct exposure zone, roughly 97 structures close enough to burnable vegetation for flame contact, not just embers — 46.7% fall in the Indirect, ember-only zone and 0% are Minimal.
How Mattawana compares
Mattawana's 70th-percentile standing inside Pennsylvania outpaces its 43rd 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, Mattawana ranks 17,853 for wildfire risk (1 is highest) and 25,567 by building count (1 is largest). Within Pennsylvania alone, it ranks 606 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
Mattawana's elevated rating (43rd 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 Mattawana (53.3%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Mattawana's figures come from
Every one of the two percentiles behind Mattawana's 17,853-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Mattawana's dominant direct exposure actually means, with real examples from across the dataset.