Maud, TX
Maud, TX's wildfire risk, in USFS's own numbers
Maud sits at the 78th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 668 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Maud at the 81st national percentile — 2 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
Indirect exposure is dominant in Maud (64.1% of 668 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 35.9% sit in the Direct zone.
How Maud compares
Maud's 78th national percentile looks worse in isolation than its 57th ranking inside Texas does — this place is on the milder end for its own state, by 21 points. Among the 31,521 US communities USFS scores, Maud ranks 6,822 for wildfire risk (1 is highest) and 14,984 by building count (1 is largest). Within Texas alone, it ranks 773 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
At the 78th percentile nationally, Maud carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
Maud's 64.1% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Maud's figures come from
Maud's 78th-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 Maud's dominant indirect exposure actually means, with real examples from across the dataset.