Mathis, TX
Mathis wildfire risk explained
USFS scores Mathis at the 70th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 2,084 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Mathis's burn probability — fire likelihood with no building count factored in — sits at the 69th percentile nationally.
Where Mathis's buildings actually sit
86.1% of Mathis's 2,084 buildings sit in USFS's Minimal exposure zone, with only 12% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
How Mathis compares
Mathis ranks lower within Texas (30th percentile statewide) than its 70th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Mathis ranks 9,594 for wildfire risk (1 is highest) and 7,239 by building count (1 is largest). Within Texas alone, it ranks 1,257 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
Shopping for coverage in Mathis
Mathis's 70th-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
Even with 86.1% of Mathis outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Mathis's figures come from
Mathis's 70th-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 Mathis's dominant minimal exposure actually means, with real examples from across the dataset.