Stark, KS
Stark wildfire risk explained
USFS scores Stark at the 69th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 81 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Stark's burn probability — fire likelihood with no building count factored in — sits at the 69th percentile nationally.
What "at risk" means for the buildings here
Direct exposure dominates in Stark: 53.1% of its 81 buildings, versus 46.9% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Stark against the rest of the country
Stark scores 69th nationally and 65th 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, Stark ranks 9,862 for wildfire risk (1 is highest) and 29,842 by building count (1 is largest). Within Kansas alone, it ranks 250 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
Stark and the insurance market
Stark's 69th-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 Stark
With 53.1% of Stark in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Stark's figures come from
Every one of the two percentiles behind Stark's 9,862-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Stark's dominant direct exposure actually means, with real examples from across the dataset.