Zephyr, TX
Zephyr, TX's wildfire risk, in USFS's own numbers
Zephyr's 243 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.)
Fire likelihood alone (USFS's burn-probability figure) ranks Zephyr at the 88th national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Zephyr's buildings actually sit
USFS classifies 52.3% of Zephyr's buildings as Direct exposure, higher than its 47.7% Indirect share and far above its 0% Minimal share — a profile where 127 structures sit close enough to vegetation that lot clearing matters most.
Where Zephyr ranks
Zephyr's risk sits at a similar level relative to Texas (81st percentile statewide) as it does nationally (88th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Zephyr ranks 3,735 for wildfire risk (1 is highest) and 23,379 by building count (1 is largest). Within Texas alone, it ranks 352 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
Shopping for coverage in Zephyr
Zephyr'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.
Lowering exposure, not just insuring around it
With 52.3% of Zephyr 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 Zephyr's figures come from
The methodology guide shows exactly how USFS turned 243 counted buildings into the percentiles shown above for Zephyr. The exposure-zones guide covers what Zephyr's dominant direct exposure actually means, with real examples from across the dataset.