Manchester, KS
Manchester wildfire risk explained
Manchester sits at the 81st percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 85 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Manchester at the 73rd national percentile — 8 points below its risk-to-structures score, a gap driven by how much is actually built there.
Manchester's building exposure, zone by zone
USFS classifies 52.9% of Manchester's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 47.1% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Where Manchester ranks
Manchester's risk sits at a similar level relative to Kansas (79th percentile statewide) as it does nationally (81st) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Manchester ranks 6,140 for wildfire risk (1 is highest) and 29,688 by building count (1 is largest). Within Kansas alone, it ranks 149 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
Manchester and the insurance market
Manchester's very high rating (81st 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.
Hardening a home in Manchester
With 52.9% of buildings rated Minimal exposure, Manchester gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Manchester's figures come from
Manchester's 81st-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 Manchester's dominant minimal exposure actually means, with real examples from across the dataset.