Nissequogue, NY
Nissequogue, NY's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Nissequogue at the 37th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 900 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Nissequogue at the 37th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Nissequogue's buildings actually sit
Direct exposure dominates in Nissequogue: 95.3% of its 900 buildings, versus 3.8% Indirect and 0.9% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Where Nissequogue ranks
Within New York, Nissequogue ranks higher (79th percentile) than it does nationally (37th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Nissequogue ranks 20,017 for wildfire risk (1 is highest) and 12,724 by building count (1 is largest). Within New York alone, it ranks 276 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
What this risk score means for insurance
At the 37th national percentile, Nissequogue rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Nissequogue's 95.3% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Nissequogue's figures come from
Nissequogue's 37th-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 Nissequogue's dominant direct exposure actually means, with real examples from across the dataset.