29697 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 29697 Zip Code | South Carolina | U.S. |
Total Housing Units | 4,936, 100% | 1,795,715 | 116,211,092 |
Utility Gas | 1,043, 21.13%, see rank | 23.19% | 48.85% |
Bottled, Tank, or LP Gas | 172, 3.48%, see rank | 4.45% | 4.86% |
Electricity | 3,593, 72.79%, see rank | 69.42% | 36.68% |
Fuel Oil, Kerosene, etc. | 86, 1.74%, see rank | 1.31% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.02% | 0.12% |
Wood | 42, 0.85%, see rank | 1.13% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.05% |
Other Fuel | 0, 0.00%, see rank | 0.11% | 0.47% |
No Fuel Used | 0, 0.00%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 29697 Zip Code | South Carolina | U.S. |
Total Housing Units | 4,454, 100% | 1,768,255 | 115,226,802 |
Utility Gas | 1,178, 26.45%, see rank | 24.20% | 49.42% |
Bottled, Tank, or LP Gas | 215, 4.83%, see rank | 4.95% | 5.03% |
Electricity | 2,947, 66.17%, see rank | 67.56% | 35.51% |
Fuel Oil, Kerosene, etc. | 52, 1.17%, see rank | 1.75% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.12% |
Wood | 62, 1.39%, see rank | 1.12% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.04% |
Other Fuel | 0, 0.00%, see rank | 0.10% | 0.43% |
No Fuel Used | 0, 0.00%, see rank | 0.30% | 0.90% |
US Census 2000 data
| 29697 Zip Code | South Carolina | U.S. |
Total Housing Units | 4,077, 100% | 1,533,854 | 105,480,101 |
Utility Gas | 1,153, 28.28%, see rank | 26.24% | 51.22% |
Bottled, Tank, or LP Gas | 228, 5.59%, see rank | 8.56% | 6.52% |
Electricity | 2,377, 58.30%, see rank | 58.36% | 30.35% |
Fuel Oil, Kerosene, etc. | 264, 6.48%, see rank | 5.11% | 8.97% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.14% |
Wood | 55, 1.35%, see rank | 1.26% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.03% | 0.04% |
Other Fuel | 0, 0.00%, see rank | 0.14% | 0.39% |
No Fuel Used | 0, 0.00%, see rank | 0.29% | 0.69% |
* ACS stands for U.S. Census American Community Survey. According to the U.S. Census, if the date is a range, you can interpret the data as an average of the period of time.