Marlboro, NY
Marlboro wildfire risk explained
USFS scores Marlboro at the 38th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 1,909 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Marlboro's burn probability — fire likelihood with no building count factored in — sits at the 38th percentile nationally.
What "at risk" means for the buildings here
USFS classifies 57.8% of Marlboro's buildings as Direct exposure, higher than its 42.2% Indirect share and far above its 0% Minimal share — a profile where 1,104 structures sit close enough to vegetation that lot clearing matters most.
Where Marlboro ranks
Marlboro's 81st-percentile standing inside New York outpaces its 38th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Marlboro ranks 19,668 for wildfire risk (1 is highest) and 7,740 by building count (1 is largest). Within New York alone, it ranks 246 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Marlboro and the insurance market
Marlboro's moderate wildfire rating (38th 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
With 57.8% of Marlboro in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Marlboro's figures come from
The methodology guide shows exactly how USFS turned 1,909 counted buildings into the percentiles shown above for Marlboro. The exposure-zones guide covers what Marlboro's dominant direct exposure actually means, with real examples from across the dataset.