WildfireRiskFinder

Catron, MO

Catron wildfire risk explained

Low
0thpercentile nationally

Catron sits at the 0th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places 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.)

Separately from the risk score above, Catron's burn probability — fire likelihood with no building count factored in — sits at the 0th percentile nationally.

Catron's building exposure, zone by zone

85Total buildings
34.1%Direct exposure
0%Indirect exposure
65.9%Minimal exposure

USFS classifies 65.9% of Catron's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 34.1% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.

Catron against the rest of the country

Catron scores 0th nationally and 0th within Missouri — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Catron ranks 31,435 for wildfire risk (1 is highest) and 29,693 by building count (1 is largest). Within Missouri alone, it ranks 1,060 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.

What this risk score means for insurance

Catron's low rating (0th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Catron

Catron's 65.9% 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 Catron's figures come from

Catron's 0th-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 Catron's dominant minimal exposure actually means, with real examples from across the dataset.