29732 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 29732 Zip Code | South Carolina | U.S. |
Total Housing Units | 21,457, 100% | 1,795,715 | 116,211,092 |
Utility Gas | 12,243, 57.06%, see rank | 23.19% | 48.85% |
Bottled, Tank, or LP Gas | 288, 1.34%, see rank | 4.45% | 4.86% |
Electricity | 8,693, 40.51%, see rank | 69.42% | 36.68% |
Fuel Oil, Kerosene, etc. | 77, 0.36%, see rank | 1.31% | 5.86% |
Coal or Coke | 26, 0.12%, see rank | 0.02% | 0.12% |
Wood | 115, 0.54%, 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 | 15, 0.07%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 29732 Zip Code | South Carolina | U.S. |
Total Housing Units | 21,478, 100% | 1,768,255 | 115,226,802 |
Utility Gas | 12,523, 58.31%, see rank | 24.20% | 49.42% |
Bottled, Tank, or LP Gas | 186, 0.87%, see rank | 4.95% | 5.03% |
Electricity | 8,474, 39.45%, see rank | 67.56% | 35.51% |
Fuel Oil, Kerosene, etc. | 123, 0.57%, see rank | 1.75% | 6.46% |
Coal or Coke | 36, 0.17%, see rank | 0.01% | 0.12% |
Wood | 121, 0.56%, 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 | 15, 0.07%, see rank | 0.30% | 0.90% |
US Census 2000 data
| 29732 Zip Code | South Carolina | U.S. |
Total Housing Units | 15,058, 100% | 1,533,854 | 105,480,101 |
Utility Gas | 8,099, 53.79%, see rank | 26.24% | 51.22% |
Bottled, Tank, or LP Gas | 335, 2.22%, see rank | 8.56% | 6.52% |
Electricity | 5,986, 39.75%, see rank | 58.36% | 30.35% |
Fuel Oil, Kerosene, etc. | 545, 3.62%, see rank | 5.11% | 8.97% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.14% |
Wood | 93, 0.62%, 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.