Ampere North, NJ
Ampere North, NJ's wildfire risk, in USFS's own numbers
Ampere North sits at the 16th 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 1,171 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 Ampere North at the 19th percentile, close to its 16th-percentile risk score.
Where Ampere North's buildings actually sit
Most of Ampere North's buildings (100% of 1,171) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.
Ampere North against the rest of the country
Ampere North scores 16th nationally and 2nd within New Jersey — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Ampere North ranks 26,476 for wildfire risk (1 is highest) and 10,822 by building count (1 is largest). Within New Jersey alone, it ranks 687 of 700 places by risk. See the full county-by-county picture for New Jersey on its state page.
Shopping for coverage in Ampere North
Ampere North's low rating (16th 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
Ampere North's 100% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Ampere North's figures come from
Every one of the two percentiles behind Ampere North's 26,476-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Ampere North's dominant minimal exposure actually means, with real examples from across the dataset.