Lone Elm, KS
Lone Elm wildfire risk explained
Out of every US place USFS scores, Lone Elm lands at the 62nd percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 49 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Lone Elm at the 61st national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Lone Elm's building exposure, zone by zone
59.2% of Lone Elm's 49 buildings sit in USFS's Direct exposure zone, roughly 29 structures close enough to burnable vegetation for flame contact, not just embers — 40.8% fall in the Indirect, ember-only zone and 0% are Minimal.
Lone Elm against the rest of the country
There's little gap between Lone Elm's 62nd national percentile and its 55th percentile inside Kansas, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Lone Elm ranks 12,130 for wildfire risk (1 is highest) and 30,955 by building count (1 is largest). Within Kansas alone, it ranks 328 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
What this risk score means for insurance
Lone Elm's high rating (62nd percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
Lone Elm's 59.2% 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 Lone Elm's figures come from
Lone Elm's 62nd-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 Lone Elm's dominant direct exposure actually means, with real examples from across the dataset.