Orchard Park, NY
Orchard Park wildfire risk explained
Out of every US place USFS scores, Orchard Park lands at the 26th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 1,446 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Orchard Park's burn probability — fire likelihood with no building count factored in — sits at the 28th percentile nationally.
Orchard Park's building exposure, zone by zone
USFS classifies 60.6% of Orchard Park's buildings as Direct exposure, higher than its 1.3% Indirect share and far above its 38.1% Minimal share — a profile where 876 structures sit close enough to vegetation that lot clearing matters most.
Where Orchard Park ranks
Compare Orchard Park's two percentiles: 55th within New York, only 26th nationally — a gap of 29 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Orchard Park ranks 23,203 for wildfire risk (1 is highest) and 9,406 by building count (1 is largest). Within New York alone, it ranks 582 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
Orchard Park's moderate wildfire rating (26th 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
Orchard Park's 60.6% 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 Orchard Park's figures come from
The methodology guide shows exactly how USFS turned 1,446 counted buildings into the percentiles shown above for Orchard Park. The exposure-zones guide covers what Orchard Park's dominant direct exposure actually means, with real examples from across the dataset.