Camp Swift, TX
Camp Swift, TX's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Camp Swift at the 70th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 2,907 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Camp Swift at the 72nd national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
2,907 buildings are counted in Camp Swift, and 88.1% 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.
Where Camp Swift ranks
Inside Texas, Camp Swift sits at just the 32nd percentile even though it scores 70th 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, Camp Swift ranks 9,368 for wildfire risk (1 is highest) and 5,575 by building count (1 is largest). Within Texas alone, it ranks 1,224 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
Camp Swift's high rating (70th 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.
What would actually reduce this score
Camp Swift's 88.1% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Camp Swift's figures come from
Camp Swift's 70th-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 Camp Swift's dominant direct exposure actually means, with real examples from across the dataset.