Binghamton University, NY
Binghamton University wildfire risk explained
Binghamton University's 134 buildings earn a 9th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Binghamton University's burn probability — fire likelihood with no building count factored in — sits at the 9th percentile nationally.
Where Binghamton University's buildings actually sit
USFS puts 90.3% of Binghamton University's 134 buildings in the Indirect exposure zone, versus 9.7% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
Where Binghamton University ranks
There's little gap between Binghamton University's 9th national percentile and its 21st percentile inside New York, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Binghamton University ranks 28,657 for wildfire risk (1 is highest) and 27,553 by building count (1 is largest). Within New York alone, it ranks 1,022 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Binghamton University and the insurance market
At the 9th national percentile, Binghamton University rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Binghamton University (90.3% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Binghamton University's figures come from
Every one of the two percentiles behind Binghamton University's 28,657-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Binghamton University's dominant indirect exposure actually means, with real examples from across the dataset.