North Sea, NY
North Sea, NY's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts North Sea at the 41st national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 4,490 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts North Sea at the 40th percentile, close to its 41st-percentile risk score.
Where North Sea's buildings actually sit
65.7% of North Sea's 4,490 buildings sit in USFS's Direct exposure zone, roughly 2,948 structures close enough to burnable vegetation for flame contact, not just embers — 30.1% fall in the Indirect, ember-only zone and 4.2% are Minimal.
Where North Sea ranks
Compare North Sea's two percentiles: 86th within New York, only 41st nationally — a gap of 45 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, North Sea ranks 18,472 for wildfire risk (1 is highest) and 3,802 by building count (1 is largest). Within New York alone, it ranks 176 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Shopping for coverage in North Sea
North Sea's elevated wildfire rating (41st 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.
Lowering exposure, not just insuring around it
With 65.7% of North Sea in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where North Sea's figures come from
North Sea's 41st-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 North Sea's dominant direct exposure actually means, with real examples from across the dataset.