Crawford, TX
Crawford wildfire risk explained
Out of every US place USFS scores, Crawford lands at the 91st percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 506 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Crawford at the 90th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
506 buildings are counted in Crawford, and 50.2% of them are Indirect exposure — ember-driven risk rather than the 49.8% in Direct exposure or the 0% rated Minimal.
How Crawford compares
Crawford scores 91st nationally and 87th 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, Crawford ranks 2,749 for wildfire risk (1 is highest) and 17,248 by building count (1 is largest). Within Texas alone, it ranks 227 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
Crawford's 91st-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
Because 50.2% of Crawford's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Crawford's figures come from
Every one of the two percentiles behind Crawford's 2,749-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Crawford's dominant indirect exposure actually means, with real examples from across the dataset.