Affton, MO
Affton, MO's wildfire risk, in USFS's own numbers
Affton's 8,920 buildings earn a 43rd-percentile wildfire-risk score nationally under USFS's model — modestly above the national average for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Affton's burn probability — fire likelihood with no building count factored in — sits at the 47th percentile nationally.
What "at risk" means for the buildings here
USFS classifies 99.4% of Affton's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 0.6% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one. At 8,920 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Affton against the rest of the country
Affton ranks lower within Missouri (19th percentile statewide) than its 43rd national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Affton ranks 17,992 for wildfire risk (1 is highest) and 1,867 by building count (1 is largest). Within Missouri alone, it ranks 863 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
Affton and the insurance market
At the 43rd national percentile, Affton rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Affton
Affton's 99.4% 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 Affton's figures come from
Every one of the two percentiles behind Affton's 17,992-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Affton's dominant minimal exposure actually means, with real examples from across the dataset.