WildfireRiskFinder

Lisle, NY

Lisle wildfire risk explained

Low
9thpercentile nationally

Out of every US place USFS scores, Lisle lands at the 9th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 194 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Lisle at the 9th national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.

What "at risk" means for the buildings here

194Total buildings
63.4%Direct exposure
36.6%Indirect exposure
0%Minimal exposure

Of Lisle's 194 counted buildings, 63.4% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.

How Lisle compares

Lisle scores 9th nationally and 22nd 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, Lisle ranks 28,597 for wildfire risk (1 is highest) and 25,091 by building count (1 is largest). Within New York alone, it ranks 1,012 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.

Shopping for coverage in Lisle

Lisle's low rating (9th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

What would actually reduce this score

Lisle's 63.4% 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 Lisle's figures come from

The methodology guide shows exactly how USFS turned 194 counted buildings into the percentiles shown above for Lisle. The exposure-zones guide covers what Lisle's dominant direct exposure actually means, with real examples from across the dataset.