New Oxford, PA
New Oxford wildfire risk explained
New Oxford sits at the 4th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 716 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts New Oxford at the 4th percentile, close to its 4th-percentile risk score.
Where New Oxford's buildings actually sit
90.4% of New Oxford's 716 buildings sit in USFS's Minimal exposure zone, with only 9.6% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
How New Oxford compares
There's little gap between New Oxford's 4th national percentile and its 2nd percentile inside Pennsylvania, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, New Oxford ranks 30,211 for wildfire risk (1 is highest) and 14,506 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,956 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Shopping for coverage in New Oxford
At the 4th national percentile, New Oxford rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in New Oxford
Even with 90.4% of New Oxford outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where New Oxford's figures come from
The methodology guide shows exactly how USFS turned 716 counted buildings into the percentiles shown above for New Oxford. The exposure-zones guide covers what New Oxford's dominant minimal exposure actually means, with real examples from across the dataset.