Prague, OK
Prague wildfire risk explained
Prague sits at the 96th 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 1,403 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Prague at the 97th percentile, close to its 96th-percentile risk score.
Prague's building exposure, zone by zone
USFS puts 76.8% of Prague's 1,403 buildings in the Indirect exposure zone, versus 23.2% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
Where Prague ranks
There's little gap between Prague's 96th national percentile and its 89th percentile inside Oklahoma, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Prague ranks 1,140 for wildfire risk (1 is highest) and 9,614 by building count (1 is largest). Within Oklahoma alone, it ranks 97 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Prague and the insurance market
Prague's 96th-percentile, very 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 Prague
Because 76.8% of Prague's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Prague's figures come from
Prague's 96th-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 Prague's dominant indirect exposure actually means, with real examples from across the dataset.