WildfireRiskFinder

Moneta, VA

Moneta wildfire risk explained

Elevated
60thpercentile nationally

Moneta's 349 buildings earn a 60th-percentile wildfire-risk score nationally under USFS's model — modestly above the national average for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Moneta at the 62nd national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.

Moneta's building exposure, zone by zone

349Total buildings
74.8%Direct exposure
25.2%Indirect exposure
0%Minimal exposure

Direct exposure dominates in Moneta: 74.8% of its 349 buildings, versus 25.2% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.

Where Moneta ranks

Moneta's risk sits at a similar level relative to Virginia (67th percentile statewide) as it does nationally (60th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Moneta ranks 12,718 for wildfire risk (1 is highest) and 20,355 by building count (1 is largest). Within Virginia alone, it ranks 228 of 681 places by risk. See the full county-by-county picture for Virginia on its state page.

Shopping for coverage in Moneta

Moneta's elevated rating (60th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Moneta

Moneta's 74.8% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.

Where Moneta's figures come from

Every one of the two percentiles behind Moneta's 12,718-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Moneta's dominant direct exposure actually means, with real examples from across the dataset.