WildfireRiskFinder

Strang, OK

Strang, OK's wildfire risk, in USFS's own numbers

Very High
88thpercentile nationally

Strang's 60 buildings earn a 88th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Strang's burn probability — fire likelihood with no building count factored in — sits at the 90th percentile nationally.

Where Strang's buildings actually sit

60Total buildings
65%Direct exposure
35%Indirect exposure
0%Minimal exposure

Direct exposure dominates in Strang: 65% of its 60 buildings, versus 35% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.

How Strang compares

Inside Oklahoma, Strang sits at just the 40th percentile even though it scores 88th 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, Strang ranks 3,758 for wildfire risk (1 is highest) and 30,660 by building count (1 is largest). Within Oklahoma alone, it ranks 500 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.

What this risk score means for insurance

Strang's very high rating (88th 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.

What would actually reduce this score

Strang's 65% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.

Where Strang's figures come from

Strang's 88th-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 Strang's dominant direct exposure actually means, with real examples from across the dataset.