WildfireRiskFinder

Blum, TX

Blum wildfire risk explained

Very High
92ndpercentile nationally

USFS's Wildfire Risk to Communities model puts Blum at the 92nd national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 279 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Blum at the 91st national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.

What "at risk" means for the buildings here

279Total buildings
40.1%Direct exposure
59.9%Indirect exposure
0%Minimal exposure

USFS puts 59.9% of Blum's 279 buildings in the Indirect exposure zone, versus 40.1% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.

How Blum compares

There's little gap between Blum's 92nd national percentile and its 88th percentile inside Texas, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Blum ranks 2,687 for wildfire risk (1 is highest) and 22,243 by building count (1 is largest). Within Texas alone, it ranks 214 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.

Blum and the insurance market

Blum's very high rating (92nd 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.

Lowering exposure, not just insuring around it

With ember exposure the dominant pattern in Blum (59.9% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.

Where Blum's figures come from

Blum's 92nd-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 Blum's dominant indirect exposure actually means, with real examples from across the dataset.