Maud, OK
Maud, OK's wildfire risk, in USFS's own numbers
Maud sits at the 97th percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 726 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Maud at the 98th percentile, close to its 97th-percentile risk score.
Maud's building exposure, zone by zone
726 buildings are counted in Maud, and 57.6% of them are Indirect exposure — ember-driven risk rather than the 42.4% in Direct exposure or the 0% rated Minimal.
Where Maud ranks
There's little gap between Maud's 97th national percentile and its 91st percentile inside Oklahoma, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Maud ranks 1,050 for wildfire risk (1 is highest) and 14,385 by building count (1 is largest). Within Oklahoma alone, it ranks 75 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
What this risk score means for insurance
Maud's 97th-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
Maud's 57.6% 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 97th-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.