Garza-Salinas II, TX
Garza-Salinas II wildfire risk explained
Garza-Salinas II's 169 buildings earn a 88th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Garza-Salinas II at the 87th percentile, close to its 88th-percentile risk score.
What "at risk" means for the buildings here
169 buildings are counted in Garza-Salinas II, and 100% of them are Indirect exposure — ember-driven risk rather than the 0% in Direct exposure or the 0% rated Minimal.
Garza-Salinas II against the rest of the country
Garza-Salinas II scores 88th nationally and 79th within Texas — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Garza-Salinas II ranks 3,918 for wildfire risk (1 is highest) and 26,084 by building count (1 is largest). Within Texas alone, it ranks 371 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
Shopping for coverage in Garza-Salinas II
Garza-Salinas II's 88th-percentile, very 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
With ember exposure the dominant pattern in Garza-Salinas II (100% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Garza-Salinas II's figures come from
Garza-Salinas II's 88th-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 Garza-Salinas II's dominant indirect exposure actually means, with real examples from across the dataset.