Saw Creek, PA
Saw Creek wildfire risk explained
Out of every US place USFS scores, Saw Creek lands at the 64th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 2,292 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Saw Creek's burn probability — fire likelihood with no building count factored in — sits at the 63rd percentile nationally.
Where Saw Creek's buildings actually sit
2,292 buildings are counted in Saw Creek, and 94.7% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Where Saw Creek ranks
Saw Creek's 99th-percentile standing inside Pennsylvania outpaces its 64th 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, Saw Creek ranks 11,389 for wildfire risk (1 is highest) and 6,718 by building count (1 is largest). Within Pennsylvania alone, it ranks 27 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Saw Creek and the insurance market
Saw Creek's 64th-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Hardening a home in Saw Creek
Because Direct exposure dominates in Saw Creek (94.7%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Saw Creek's figures come from
Saw Creek's 64th-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 Saw Creek's dominant direct exposure actually means, with real examples from across the dataset.