28532 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 28532 Zip Code | North Carolina | U.S. |
Total Housing Units | 8,664, 100% | 3,742,514 | 116,211,092 |
Utility Gas | 136, 1.57%, see rank | 24.68% | 48.85% |
Bottled, Tank, or LP Gas | 550, 6.35%, see rank | 8.00% | 4.86% |
Electricity | 7,741, 89.35%, see rank | 60.27% | 36.68% |
Fuel Oil, Kerosene, etc. | 103, 1.19%, see rank | 4.34% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.12% |
Wood | 40, 0.46%, see rank | 2.17% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.03% | 0.05% |
Other Fuel | 6, 0.07%, see rank | 0.15% | 0.47% |
No Fuel Used | 88, 1.02%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 28532 Zip Code | North Carolina | U.S. |
Total Housing Units | 8,963, 100% | 3,693,221 | 115,226,802 |
Utility Gas | 134, 1.50%, see rank | 24.97% | 49.42% |
Bottled, Tank, or LP Gas | 742, 8.28%, see rank | 8.90% | 5.03% |
Electricity | 7,801, 87.04%, see rank | 58.26% | 35.51% |
Fuel Oil, Kerosene, etc. | 124, 1.38%, see rank | 5.24% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.12% |
Wood | 61, 0.68%, see rank | 2.16% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.02% | 0.04% |
Other Fuel | 14, 0.16%, see rank | 0.14% | 0.43% |
No Fuel Used | 87, 0.97%, see rank | 0.29% | 0.90% |
US Census 2000 data
| 28532 Zip Code | North Carolina | U.S. |
Total Housing Units | 8,305, 100% | 3,132,013 | 105,480,101 |
Utility Gas | 131, 1.58%, see rank | 24.19% | 51.22% |
Bottled, Tank, or LP Gas | 827, 9.96%, see rank | 12.59% | 6.52% |
Electricity | 6,930, 83.44%, see rank | 48.81% | 30.35% |
Fuel Oil, Kerosene, etc. | 276, 3.32%, see rank | 11.76% | 8.97% |
Coal or Coke | 0, 0.00%, see rank | 0.01% | 0.14% |
Wood | 45, 0.54%, see rank | 2.10% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.04% | 0.04% |
Other Fuel | 26, 0.31%, see rank | 0.21% | 0.39% |
No Fuel Used | 70, 0.84%, see rank | 0.28% | 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.