Alva, OK
Alva wildfire risk explained
Alva sits at the 70th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 3,491 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Alva at the 65th national percentile — 5 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Alva's buildings actually sit
USFS puts 58.4% of Alva's 3,491 buildings in the Indirect exposure zone, versus 4% Direct and 37.6% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
How Alva compares
Inside Oklahoma, Alva sits at just the 11th percentile even though it scores 70th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Alva ranks 9,428 for wildfire risk (1 is highest) and 4,784 by building count (1 is largest). Within Oklahoma alone, it ranks 743 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Alva and the insurance market
Alva's 70th-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.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Alva (58.4% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Alva's figures come from
Alva's 70th-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 Alva's dominant indirect exposure actually means, with real examples from across the dataset.