Alamo, TX
How exposed is Alamo to wildfire?
USFS's Wildfire Risk to Communities model puts Alamo at the 61st national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 8,173 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Alamo's burn probability — fire likelihood with no building count factored in — sits at the 62nd percentile nationally.
What "at risk" means for the buildings here
USFS classifies 92.7% of Alamo's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 7.3% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one. At 8,173 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
How Alamo compares
Inside Texas, Alamo sits at just the 15th percentile even though it scores 61st 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, Alamo ranks 12,303 for wildfire risk (1 is highest) and 2,053 by building count (1 is largest). Within Texas alone, it ranks 1,518 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
Alamo and the insurance market
Alamo's high rating (61st 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
Alamo's 92.7% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Alamo's figures come from
Alamo's 61st-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 Alamo's dominant minimal exposure actually means, with real examples from across the dataset.