Moody, TX
Moody wildfire risk explained
Moody sits at the 80th 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 834 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Moody's burn probability — fire likelihood with no building count factored in — sits at the 77th percentile nationally.
Moody's building exposure, zone by zone
Most of Moody's buildings (52.5%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Moody compares
Inside Texas, Moody sits at just the 61st percentile even though it scores 80th 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, Moody ranks 6,318 for wildfire risk (1 is highest) and 13,310 by building count (1 is largest). Within Texas alone, it ranks 701 of 1,795 places by risk. See the full county-by-county picture for Texas on its state page.
Moody and the insurance market
At the 80th percentile nationally, Moody 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.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Moody (52.5% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Moody's figures come from
Moody's 80th-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 Moody's dominant indirect exposure actually means, with real examples from across the dataset.