WildfireRiskFinder

Lane, IL

Lane wildfire risk explained

Low
12thpercentile nationally

Out of every US place USFS scores, Lane lands at the 12th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 99 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Lane at the 11th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.

Lane's building exposure, zone by zone

99Total buildings
7.1%Direct exposure
0%Indirect exposure
92.9%Minimal exposure

Only 7.1% of Lane's 99 buildings carry Direct exposure and 0% carry Indirect; the remaining 92.9% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.

Where Lane ranks

Within Illinois, Lane ranks higher (46th percentile) than it does nationally (12th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Lane ranks 27,851 for wildfire risk (1 is highest) and 29,071 by building count (1 is largest). Within Illinois alone, it ranks 781 of 1,445 places by risk. See the full county-by-county picture for Illinois on its state page.

What this risk score means for insurance

Lane's low rating (12th 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

Lane's 92.9% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.

Where Lane's figures come from

Lane's 12th-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 Lane's dominant minimal exposure actually means, with real examples from across the dataset.