Syracuse, NY
How exposed is Syracuse to wildfire?
USFS's Wildfire Risk to Communities model puts Syracuse at the 8th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 46,669 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Syracuse's burn probability — fire likelihood with no building count factored in — sits at the 8th percentile nationally.
Syracuse's building exposure, zone by zone
Most of Syracuse's buildings (87.4% of 46,669) 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. At 46,669 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
How Syracuse compares
Syracuse scores 8th nationally and 17th within New York — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Syracuse ranks 29,003 for wildfire risk (1 is highest) and 195 by building count (1 is largest). Within New York alone, it ranks 1,069 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Shopping for coverage in Syracuse
At the 8th national percentile, Syracuse rates low 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
With 87.4% of buildings rated Minimal exposure, Syracuse gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Syracuse's figures come from
Syracuse's 8th-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 Syracuse's dominant minimal exposure actually means, with real examples from across the dataset.