WildfireRiskFinder

Robinhood, MS

How exposed is Robinhood to wildfire?

High
65thpercentile nationally

Robinhood's 842 buildings earn a 65th-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Robinhood's burn probability — fire likelihood with no building count factored in — sits at the 67th percentile nationally.

What "at risk" means for the buildings here

842Total buildings
96.8%Direct exposure
2.3%Indirect exposure
1%Minimal exposure

842 buildings are counted in Robinhood, and 96.8% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 1% rated Minimal.

How Robinhood compares

Robinhood ranks lower within Mississippi (45th percentile statewide) than its 65th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Robinhood ranks 11,003 for wildfire risk (1 is highest) and 13,232 by building count (1 is largest). Within Mississippi alone, it ranks 233 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.

Robinhood and the insurance market

Robinhood's high rating (65th 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.

Hardening a home in Robinhood

With 96.8% of Robinhood in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.

Where Robinhood's figures come from

The methodology guide shows exactly how USFS turned 842 counted buildings into the percentiles shown above for Robinhood. The exposure-zones guide covers what Robinhood's dominant direct exposure actually means, with real examples from across the dataset.