Mesa, CA
Mesa wildfire risk explained
Mesa sits at the 83rd 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 189 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Mesa's burn probability — fire likelihood with no building count factored in — sits at the 86th percentile nationally.
What "at risk" means for the buildings here
Of Mesa's 189 counted buildings, 93.1% 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 Mesa compares
Mesa ranks lower within California (45th percentile statewide) than its 83rd national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Mesa ranks 5,317 for wildfire risk (1 is highest) and 25,269 by building count (1 is largest). Within California alone, it ranks 861 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
Mesa and the insurance market
Mesa sits in California, one of two states that legally require a wildfire-risk disclosure at sale. California's FAIR Plan, the state's insurer of last resort, ended 2025 with 668,609 residential policies after adding 21,859 in Q4 alone — the kind of market shift a 83rd-percentile score like Mesa's can end up mattering for. More on the disclosure law.
Hardening a home in Mesa
Because Direct exposure dominates in Mesa (93.1%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Mesa's figures come from
The methodology guide shows exactly how USFS turned 189 counted buildings into the percentiles shown above for Mesa. The exposure-zones guide covers what Mesa's dominant direct exposure actually means, with real examples from across the dataset.