Sand Hill, OK
Sand Hill wildfire risk explained
Sand Hill sits at the 91st 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 374 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Sand Hill at the 93rd national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Sand Hill's buildings actually sit
Of Sand Hill's 374 counted buildings, 100% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Sand Hill against the rest of the country
Sand Hill's 91st national percentile looks worse in isolation than its 51st ranking inside Oklahoma does — this place is on the milder end for its own state, by 40 points. Among the 31,521 US communities USFS scores, Sand Hill ranks 2,933 for wildfire risk (1 is highest) and 19,788 by building count (1 is largest). Within Oklahoma alone, it ranks 409 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
What this risk score means for insurance
Sand Hill's very high rating (91st 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.
What would actually reduce this score
Sand Hill's 100% 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 Sand Hill's figures come from
Sand Hill's 91st-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 Sand Hill's dominant direct exposure actually means, with real examples from across the dataset.