Sayville, NY
How exposed is Sayville to wildfire?
USFS scores Sayville at the 46th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 6,272 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Sayville's burn probability — fire likelihood with no building count factored in — sits at the 42nd percentile nationally.
Where Sayville's buildings actually sit
USFS classifies 82.8% of Sayville's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 15.2% Direct and 2% Indirect — a landscape-level risk rather than a building-by-building one. At 6,272 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Where Sayville ranks
Compare Sayville's two percentiles: 91st within New York, only 46th nationally — a gap of 45 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Sayville ranks 17,026 for wildfire risk (1 is highest) and 2,764 by building count (1 is largest). Within New York alone, it ranks 121 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Shopping for coverage in Sayville
Sayville's elevated rating (46th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Hardening a home in Sayville
With 82.8% of buildings rated Minimal exposure, Sayville gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Sayville's figures come from
Sayville's 46th-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 Sayville's dominant minimal exposure actually means, with real examples from across the dataset.