Mount Hope, KS
Mount Hope wildfire risk explained
USFS's Wildfire Risk to Communities model puts Mount Hope at the 66th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 501 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Mount Hope at the 60th national percentile — 5 points below its risk-to-structures score, a gap driven by how much is actually built there.
Mount Hope's building exposure, zone by zone
501 buildings are counted in Mount Hope, and 77.5% of them are Indirect exposure — ember-driven risk rather than the 5.8% in Direct exposure or the 16.8% rated Minimal.
Mount Hope against the rest of the country
Mount Hope scores 66th nationally and 60th within Kansas — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Mount Hope ranks 10,810 for wildfire risk (1 is highest) and 17,333 by building count (1 is largest). Within Kansas alone, it ranks 287 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
Mount Hope and the insurance market
Mount Hope's 66th-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.
Hardening a home in Mount Hope
With ember exposure the dominant pattern in Mount Hope (77.5% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Mount Hope's figures come from
Every one of the two percentiles behind Mount Hope's 10,810-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Mount Hope's dominant indirect exposure actually means, with real examples from across the dataset.