29169 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 29169 Zip Code | South Carolina | U.S. |
Total Housing Units | 10,275, 100% | 1,795,715 | 116,211,092 |
Utility Gas | 3,721, 36.21%, see rank | 23.19% | 48.85% |
Bottled, Tank, or LP Gas | 100, 0.97%, see rank | 4.45% | 4.86% |
Electricity | 6,403, 62.32%, see rank | 69.42% | 36.68% |
Fuel Oil, Kerosene, etc. | 16, 0.16%, see rank | 1.31% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.02% | 0.12% |
Wood | 16, 0.16%, see rank | 1.13% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.05% |
Other Fuel | 8, 0.08%, see rank | 0.11% | 0.47% |
No Fuel Used | 11, 0.11%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 29169 Zip Code | South Carolina | U.S. |
Total Housing Units | 10,021, 100% | 1,768,255 | 115,226,802 |
Utility Gas | 3,663, 36.55%, see rank | 24.20% | 49.42% |
Bottled, Tank, or LP Gas | 71, 0.71%, see rank | 4.95% | 5.03% |
Electricity | 6,223, 62.10%, see rank | 67.56% | 35.51% |
Fuel Oil, Kerosene, etc. | 9, 0.09%, see rank | 1.75% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.12% |
Wood | 44, 0.44%, see rank | 1.12% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.04% |
Other Fuel | 8, 0.08%, see rank | 0.10% | 0.43% |
No Fuel Used | 3, 0.03%, see rank | 0.30% | 0.90% |
US Census 2000 data
| 29169 Zip Code | South Carolina | U.S. |
Total Housing Units | 9,211, 100% | 1,533,854 | 105,480,101 |
Utility Gas | 4,182, 45.40%, see rank | 26.24% | 51.22% |
Bottled, Tank, or LP Gas | 130, 1.41%, see rank | 8.56% | 6.52% |
Electricity | 4,676, 50.77%, see rank | 58.36% | 30.35% |
Fuel Oil, Kerosene, etc. | 188, 2.04%, see rank | 5.11% | 8.97% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.14% |
Wood | 24, 0.26%, 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 | 11, 0.12%, 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.