Newcastle, TX
Newcastle, TX's wildfire risk, in USFS's own numbers
Newcastle sits at the 96th percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 503 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Newcastle's burn probability — fire likelihood with no building count factored in — sits at the 96th percentile nationally.
Where Newcastle's buildings actually sit
USFS puts 56.3% of Newcastle's 503 buildings in the Indirect exposure zone, versus 43.7% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
Newcastle against the rest of the country
There's little gap between Newcastle's 96th national percentile and its 97th percentile inside Texas, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Newcastle ranks 1,212 for wildfire risk (1 is highest) and 17,298 by building count (1 is largest). Within Texas alone, it ranks 61 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
What this risk score means for insurance
At the 96th percentile nationally, Newcastle carries the very 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 56.3% of Newcastle's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Newcastle's figures come from
The methodology guide shows exactly how USFS turned 503 counted buildings into the percentiles shown above for Newcastle. The exposure-zones guide covers what Newcastle's dominant indirect exposure actually means, with real examples from across the dataset.