Sackets Harbor, NY
Sackets Harbor wildfire risk explained
Out of every US place USFS scores, Sackets Harbor lands at the 10th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 794 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Sackets Harbor at the 9th national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Most of Sackets Harbor's buildings (54% of 794) 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.
Sackets Harbor against the rest of the country
Sackets Harbor's risk sits at a similar level relative to New York (22nd percentile statewide) as it does nationally (10th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Sackets Harbor ranks 28,532 for wildfire risk (1 is highest) and 13,694 by building count (1 is largest). Within New York alone, it ranks 1,007 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Shopping for coverage in Sackets Harbor
Sackets Harbor's low rating (10th 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
Sackets Harbor's 54% 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 Sackets Harbor's figures come from
Every one of the two percentiles behind Sackets Harbor's 28,532-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Sackets Harbor's dominant minimal exposure actually means, with real examples from across the dataset.