Cuba, MO
Cuba, MO's wildfire risk, in USFS's own numbers
USFS scores Cuba at the 66th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 1,866 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Cuba at the 68th national percentile — 3 points above its risk-to-structures score, a gap driven by how much is actually built there.
Cuba's building exposure, zone by zone
Most of Cuba's buildings (76.7%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Cuba against the rest of the country
Cuba's risk sits at a similar level relative to Missouri (72nd percentile statewide) as it does nationally (66th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Cuba ranks 10,750 for wildfire risk (1 is highest) and 7,859 by building count (1 is largest). Within Missouri alone, it ranks 300 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
Cuba's high rating (66th 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.
Lowering exposure, not just insuring around it
Cuba's 76.7% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Cuba's figures come from
Cuba's 66th-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 Cuba's dominant indirect exposure actually means, with real examples from across the dataset.