WildfireRiskFinder

Rule, TX

Rule wildfire risk explained

High
66thpercentile nationally

Rule sits at the 66th 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 553 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Rule's burn probability — fire likelihood with no building count factored in — sits at the 60th percentile nationally.

Where Rule's buildings actually sit

553Total buildings
48.5%Direct exposure
33.8%Indirect exposure
17.7%Minimal exposure

553 buildings are counted in Rule, and 48.5% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 17.7% rated Minimal.

Where Rule ranks

Rule's 66th national percentile looks worse in isolation than its 22nd ranking inside Texas does — this place is on the milder end for its own state, by 43 points. Among the 31,521 US communities USFS scores, Rule ranks 10,791 for wildfire risk (1 is highest) and 16,534 by building count (1 is largest). Within Texas alone, it ranks 1,392 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.

Rule and the insurance market

At the 66th percentile nationally, Rule carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.

What would actually reduce this score

Because Direct exposure dominates in Rule (48.5%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Rule's figures come from

Rule's 66th-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 Rule's dominant direct exposure actually means, with real examples from across the dataset.