Gleneagle Green is a private community in Sea Pines on Hilton Head Island, SC. In 1956, Charles Fraser developed Sea Pines into the first private plantation community on the island. Sea Pines is one of the biggest residential and resort plantations on Hilton Head Island spanning 5,200 acres. Within Sea Pines are 3839 homes and 2042 villas. Villa prices start in the low $300,000s and homes start in the high $400,000s.
The community is most famous for its three championship golf courses, including the Harbour Town Golf Links, home of the PGA Tour's annual MCI Heritage. Sea Pines is also associated with the world-esteemed Harbour Town Marina and its landmark red-and-white-striped lighthouse, built in 1969 to aid boats traveling on the Calibogue Sound. The marina hosts awe-inspiring yachts as big as 115 feet.
Sea Pines has a lush, 605-acre forest preserve, access to five miles of Atlantic Ocean beaches, over 100 tennis courts, two security gates, and many swimming pools. It is also the location of the South Beach Racquet Club, Sea Pines Racquet Club, Lawton Stables equestrian center, and restaurants and outdoor activities held at South Beach Marina Village.
Gleneagle Green Amenities
Beach Access |
Biking |
Community Pool |
Equestrian |
Fitness Center |
Gated/Security Guard |
Harbour/Marina |
Kayaking |
On-Site Tennis |
Private Clubhouse |
Private Golf Course |
Public Clubhouse |
Public Golf Course |
Walking Paths |
Gleneagle Green
GPS Latitude: 32.132202000000 | GPS Longitude: -80.801273000000
Average Listing Price Index is calculated as an average listing price furnished by MLS data. These calculations are derived on every page load, and do not reflect extremes or anomalies in the data.
Available Data Breakdown
1. Average Home Price: $1,413,466
2. Average Condo Price: $479,798
3. Average Homesite Price: $885,905
Daily Average
Listing Price Index HH/Off Plantation
Average Listing Price Index is calculated as an average listing price furnished by MLS data. These calculations are derived on every page load, and do not reflect extremes or anomalies in the data.
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