Hewlett Neck, NY
Hewlett Neck, NY's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Hewlett Neck at the 53rd national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 196 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Hewlett Neck's burn probability — fire likelihood with no building count factored in — sits at the 49th percentile nationally.
Hewlett Neck's building exposure, zone by zone
Most of Hewlett Neck's buildings (81.6% of 196) 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.
How Hewlett Neck compares
Compare Hewlett Neck's two percentiles: 97th within New York, only 53rd nationally — a gap of 44 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Hewlett Neck ranks 14,785 for wildfire risk (1 is highest) and 25,007 by building count (1 is largest). Within New York alone, it ranks 41 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Hewlett Neck and the insurance market
Hewlett Neck's elevated wildfire rating (53rd 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
Even with 81.6% of Hewlett Neck 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 Hewlett Neck's figures come from
Hewlett Neck's 53rd-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 Hewlett Neck's dominant minimal exposure actually means, with real examples from across the dataset.