Clever, MO
Clever wildfire risk explained
Clever sits at the 71st percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 1,146 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Clever at the 72nd percentile, close to its 71st-percentile risk score.
Clever's building exposure, zone by zone
Clever rates 77.5% Minimal exposure against just 22.5% Direct and 0% Indirect — of 1,146 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Where Clever ranks
Clever scores 71st nationally and 83rd within Missouri — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Clever ranks 9,301 for wildfire risk (1 is highest) and 10,986 by building count (1 is largest). Within Missouri alone, it ranks 183 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
What this risk score means for insurance
Clever's 71st-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Lowering exposure, not just insuring around it
Clever's 77.5% 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 Clever's figures come from
Every one of the two percentiles behind Clever's 9,301-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Clever's dominant minimal exposure actually means, with real examples from across the dataset.