Penalosa, KS
How exposed is Penalosa to wildfire?
Out of every US place USFS scores, Penalosa lands at the 78th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 43 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Penalosa at the 70th national percentile — 8 points below its risk-to-structures score, a gap driven by how much is actually built there.
Penalosa's building exposure, zone by zone
Penalosa rates 90.7% Minimal exposure against just 9.3% Direct and 0% Indirect — of 43 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Penalosa compares
Penalosa's risk sits at a similar level relative to Kansas (76th percentile statewide) as it does nationally (78th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Penalosa ranks 6,934 for wildfire risk (1 is highest) and 31,101 by building count (1 is largest). Within Kansas alone, it ranks 175 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
Penalosa and the insurance market
Penalosa's 78th-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.
What would actually reduce this score
Penalosa's 90.7% 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 Penalosa's figures come from
Every one of the two percentiles behind Penalosa's 6,934-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Penalosa's dominant minimal exposure actually means, with real examples from across the dataset.