Sehili, AZ
Sehili wildfire risk explained
Out of every US place USFS scores, Sehili lands at the 91st percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 69 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Sehili at the 86th percentile, close to its 91st-percentile risk score.
Sehili's building exposure, zone by zone
Direct exposure dominates in Sehili: 100% of its 69 buildings, versus 0% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Where Sehili ranks
Sehili ranks lower within Arizona (66th percentile statewide) than its 91st national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Sehili ranks 2,696 for wildfire risk (1 is highest) and 30,299 by building count (1 is largest). Within Arizona alone, it ranks 151 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
Sehili'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.
Hardening a home in Sehili
Sehili'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 Sehili's figures come from
The methodology guide shows exactly how USFS turned 69 counted buildings into the percentiles shown above for Sehili. The exposure-zones guide covers what Sehili's dominant direct exposure actually means, with real examples from across the dataset.