Monee, IL
Monee wildfire risk explained
Out of every US place USFS scores, Monee lands at the 34th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 2,413 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Monee at the 35th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Monee's building exposure, zone by zone
Only 3.5% of Monee's 2,413 buildings carry Direct exposure and 0% carry Indirect; the remaining 96.5% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
Monee against the rest of the country
Within Illinois, Monee ranks higher (86th percentile) than it does nationally (34th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Monee ranks 20,695 for wildfire risk (1 is highest) and 6,485 by building count (1 is largest). Within Illinois alone, it ranks 207 of 1,445 places by risk. See the full county-by-county picture for Illinois on its state page.
Monee and the insurance market
At the 34th national percentile, Monee rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Even with 96.5% of Monee outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Monee's figures come from
Every one of the two percentiles behind Monee's 20,695-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Monee's dominant minimal exposure actually means, with real examples from across the dataset.