Pinal, AZ
Pinal wildfire risk explained
USFS scores Pinal at the 95th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 261 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Pinal at the 94th percentile, close to its 95th-percentile risk score.
What "at risk" means for the buildings here
USFS classifies 66.7% of Pinal's buildings as Direct exposure, higher than its 33.3% Indirect share and far above its 0% Minimal share — a profile where 174 structures sit close enough to vegetation that lot clearing matters most.
Where Pinal ranks
Pinal ranks lower within Arizona (75th percentile statewide) than its 95th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Pinal ranks 1,594 for wildfire risk (1 is highest) and 22,798 by building count (1 is largest). Within Arizona alone, it ranks 112 of 441 places by risk. See the full county-by-county picture for Arizona on its state page.
Pinal and the insurance market
Pinal's 95th-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 Pinal
With 66.7% of Pinal in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Pinal's figures come from
The methodology guide shows exactly how USFS turned 261 counted buildings into the percentiles shown above for Pinal. The exposure-zones guide covers what Pinal's dominant direct exposure actually means, with real examples from across the dataset.