WildfireRiskFinder

Gholson, TX

Gholson wildfire risk explained

Very High
86thpercentile nationally

USFS's Wildfire Risk to Communities model puts Gholson at the 86th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 847 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Gholson at the 88th percentile, close to its 86th-percentile risk score.

Where Gholson's buildings actually sit

847Total buildings
99.7%Direct exposure
0.4%Indirect exposure
0%Minimal exposure

Direct exposure dominates in Gholson: 99.7% of its 847 buildings, versus 0.4% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.

Gholson against the rest of the country

Gholson scores 86th nationally and 77th 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, Gholson ranks 4,296 for wildfire risk (1 is highest) and 13,195 by building count (1 is largest). Within Texas alone, it ranks 422 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.

Shopping for coverage in Gholson

Gholson's very high rating (86th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.

Lowering exposure, not just insuring around it

Because Direct exposure dominates in Gholson (99.7%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Gholson's figures come from

Gholson's 86th-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 Gholson's dominant direct exposure actually means, with real examples from across the dataset.