Sycamore Hills, MO
Sycamore Hills, MO's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Sycamore Hills at the 45th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 342 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Sycamore Hills at the 48th national percentile — 4 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Sycamore Hills's buildings actually sit
Most of Sycamore Hills's buildings (99.4% of 342) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.
Where Sycamore Hills ranks
Sycamore Hills ranks lower within Missouri (24th percentile statewide) than its 45th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Sycamore Hills ranks 17,428 for wildfire risk (1 is highest) and 20,518 by building count (1 is largest). Within Missouri alone, it ranks 811 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
Shopping for coverage in Sycamore Hills
At the 45th national percentile, Sycamore Hills rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
Sycamore Hills'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 Sycamore Hills's figures come from
Sycamore Hills's 45th-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 Sycamore Hills's dominant minimal exposure actually means, with real examples from across the dataset.