Seligman, AZ
How exposed is Seligman to wildfire?
Seligman sits at the 85th 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 486 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Seligman's burn probability — fire likelihood with no building count factored in — sits at the 81st percentile nationally.
What "at risk" means for the buildings here
486 buildings are counted in Seligman, and 74.9% of them are Indirect exposure — ember-driven risk rather than the 25.1% in Direct exposure or the 0% rated Minimal.
Seligman against the rest of the country
Seligman ranks lower within Arizona (55th percentile statewide) than its 85th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Seligman ranks 4,747 for wildfire risk (1 is highest) and 17,595 by building count (1 is largest). Within Arizona alone, it ranks 198 of 441 places by risk. See the full county-by-county picture for Arizona on its state page.
What this risk score means for insurance
At the 85th percentile nationally, Seligman carries the very 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
Seligman's 74.9% 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 Seligman's figures come from
Seligman's 85th-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 Seligman's dominant indirect exposure actually means, with real examples from across the dataset.