Seba Dalkai, AZ
How exposed is Seba Dalkai to wildfire?
Out of every US place USFS scores, Seba Dalkai lands at the 26th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 102 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Seba Dalkai at the 32nd national percentile — 6 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Seba Dalkai's buildings actually sit
Of Seba Dalkai's 102 counted buildings, 100% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
How Seba Dalkai compares
Seba Dalkai ranks lower within Arizona (6th percentile statewide) than its 26th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Seba Dalkai ranks 23,343 for wildfire risk (1 is highest) and 28,933 by building count (1 is largest). Within Arizona alone, it ranks 417 of 441 places by risk. See the full county-by-county picture for Arizona on its state page.
Shopping for coverage in Seba Dalkai
Seba Dalkai's moderate rating (26th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Seba Dalkai's 100% 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 Seba Dalkai's figures come from
The methodology guide shows exactly how USFS turned 102 counted buildings into the percentiles shown above for Seba Dalkai. The exposure-zones guide covers what Seba Dalkai's dominant direct exposure actually means, with real examples from across the dataset.