Vaiva Vo, AZ
Vaiva Vo wildfire risk explained
USFS's Wildfire Risk to Communities model puts Vaiva Vo at the 73rd national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 42 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Vaiva Vo at the 73rd percentile, close to its 73rd-percentile risk score.
Vaiva Vo's building exposure, zone by zone
42 buildings are counted in Vaiva Vo, and 100% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Where Vaiva Vo ranks
Vaiva Vo ranks lower within Arizona (34th percentile statewide) than its 73rd national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Vaiva Vo ranks 8,592 for wildfire risk (1 is highest) and 31,109 by building count (1 is largest). Within Arizona alone, it ranks 290 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
Vaiva Vo's 73rd-percentile, 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 Vaiva Vo
With 100% of Vaiva Vo 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 Vaiva Vo's figures come from
Vaiva Vo's 73rd-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 Vaiva Vo's dominant direct exposure actually means, with real examples from across the dataset.