Rio Verde, AZ
Rio Verde, AZ's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Rio Verde lands at the 92nd percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 1,541 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Rio Verde's burn probability — fire likelihood with no building count factored in — sits at the 91st percentile nationally.
Where Rio Verde's buildings actually sit
78.1% of Rio Verde's 1,541 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 21.9% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
How Rio Verde compares
Rio Verde's 92nd national percentile looks worse in isolation than its 67th ranking inside Arizona does — this place is on the milder end for its own state, by 25 points. Among the 31,521 US communities USFS scores, Rio Verde ranks 2,511 for wildfire risk (1 is highest) and 8,978 by building count (1 is largest). Within Arizona alone, it ranks 146 of 441 places by risk. See the full county-by-county picture for Arizona on its state page.
Rio Verde and the insurance market
At the 92nd percentile nationally, Rio Verde carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Rio Verde (78.1% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Rio Verde's figures come from
Rio Verde's 92nd-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 Rio Verde's dominant indirect exposure actually means, with real examples from across the dataset.