Mesa del Caballo, AZ
Mesa del Caballo wildfire risk explained
Out of every US place USFS scores, Mesa del Caballo lands at the 98th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 517 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 Mesa del Caballo at the 96th percentile, close to its 98th-percentile risk score.
What "at risk" means for the buildings here
517 buildings are counted in Mesa del Caballo, and 76.6% of them are Indirect exposure — ember-driven risk rather than the 23.4% in Direct exposure or the 0% rated Minimal.
How Mesa del Caballo compares
Mesa del Caballo scores 98th nationally and 85th within Arizona — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Mesa del Caballo ranks 774 for wildfire risk (1 is highest) and 17,064 by building count (1 is largest). Within Arizona alone, it ranks 67 of 441 places by risk. See the full county-by-county picture for Arizona on its state page.
Mesa del Caballo and the insurance market
Mesa del Caballo's very high rating (98th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Mesa del Caballo
Mesa del Caballo's 76.6% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Mesa del Caballo's figures come from
Mesa del Caballo's 98th-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 Mesa del Caballo's dominant indirect exposure actually means, with real examples from across the dataset.