South Lima, NY
South Lima, NY's wildfire risk, in USFS's own numbers
USFS scores South Lima at the 5th national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 164 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks South Lima at the 5th national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Direct exposure dominates in South Lima: 96.3% of its 164 buildings, versus 0% Indirect and 3.7% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
South Lima against the rest of the country
There's little gap between South Lima's 5th national percentile and its 6th 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, South Lima ranks 29,857 for wildfire risk (1 is highest) and 26,261 by building count (1 is largest). Within New York alone, it ranks 1,216 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
South Lima and the insurance market
South Lima's low rating (5th 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
With 96.3% of South Lima 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 South Lima's figures come from
The methodology guide shows exactly how USFS turned 164 counted buildings into the percentiles shown above for South Lima. The exposure-zones guide covers what South Lima's dominant direct exposure actually means, with real examples from across the dataset.