WildfireRiskFinder

Solvay, NY

Solvay wildfire risk explained

Low
7thpercentile nationally

USFS's Wildfire Risk to Communities model puts Solvay at the 7th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 2,742 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Solvay at the 7th percentile, close to its 7th-percentile risk score.

Where Solvay's buildings actually sit

2,742Total buildings
4.7%Direct exposure
0%Indirect exposure
95.3%Minimal exposure

USFS classifies 95.3% of Solvay's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 4.7% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.

Where Solvay ranks

Solvay's risk sits at a similar level relative to New York (13th percentile statewide) as it does nationally (7th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Solvay ranks 29,318 for wildfire risk (1 is highest) and 5,866 by building count (1 is largest). Within New York alone, it ranks 1,122 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.

What this risk score means for insurance

At the 7th national percentile, Solvay rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

What would actually reduce this score

With 95.3% of buildings rated Minimal exposure, Solvay gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.

Where Solvay's figures come from

The methodology guide shows exactly how USFS turned 2,742 counted buildings into the percentiles shown above for Solvay. The exposure-zones guide covers what Solvay's dominant minimal exposure actually means, with real examples from across the dataset.