New Cordell, OK
New Cordell, OK's wildfire risk, in USFS's own numbers
USFS scores New Cordell at the 54th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 1,923 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts New Cordell at the 50th percentile, close to its 54th-percentile risk score.
Where New Cordell's buildings actually sit
USFS classifies 81% of New Cordell's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 6.3% Direct and 12.7% Indirect — a landscape-level risk rather than a building-by-building one.
How New Cordell compares
Inside Oklahoma, New Cordell sits at just the 6th percentile even though it scores 54th 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, New Cordell ranks 14,443 for wildfire risk (1 is highest) and 7,701 by building count (1 is largest). Within Oklahoma alone, it ranks 783 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
New Cordell and the insurance market
New Cordell's elevated rating (54th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Hardening a home in New Cordell
With 81% of buildings rated Minimal exposure, New Cordell gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where New Cordell's figures come from
Every one of the two percentiles behind New Cordell's 14,443-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what New Cordell's dominant minimal exposure actually means, with real examples from across the dataset.