WildfireRiskFinder

Clint, TX

How exposed is Clint to wildfire?

Low
8thpercentile nationally

Clint's 681 buildings earn a 8th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Clint at the 6th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.

Clint's building exposure, zone by zone

681Total buildings
22.9%Direct exposure
0%Indirect exposure
77.1%Minimal exposure

Most of Clint's buildings (77.1% of 681) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.

How Clint compares

There's little gap between Clint's 8th national percentile and its 0th percentile inside Texas, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Clint ranks 29,169 for wildfire risk (1 is highest) and 14,855 by building count (1 is largest). Within Texas alone, it ranks 1,794 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

Clint's low rating (8th 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 Clint

Even with 77.1% of Clint 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 Clint's figures come from

Every one of the two percentiles behind Clint's 29,169-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Clint's dominant minimal exposure actually means, with real examples from across the dataset.