Praesel, TX
Praesel, TX's wildfire risk, in USFS's own numbers
USFS scores Praesel at the 74th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 311 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Praesel's burn probability — fire likelihood with no building count factored in — sits at the 75th percentile nationally.
What "at risk" means for the buildings here
311 buildings are counted in Praesel, and 77.5% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
How Praesel compares
Inside Texas, Praesel sits at just the 42nd percentile even though it scores 74th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Praesel ranks 8,333 for wildfire risk (1 is highest) and 21,357 by building count (1 is largest). Within Texas alone, it ranks 1,050 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
What this risk score means for insurance
Praesel's 74th-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 Praesel
With 77.5% of Praesel 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 Praesel's figures come from
The methodology guide shows exactly how USFS turned 311 counted buildings into the percentiles shown above for Praesel. The exposure-zones guide covers what Praesel's dominant direct exposure actually means, with real examples from across the dataset.