21403 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 21403 Zip Code | Maryland | U.S. |
Total Housing Units | 12,582, 100% | 2,155,983 | 116,211,092 |
Utility Gas | 3,679, 29.24%, see rank | 44.54% | 48.85% |
Bottled, Tank, or LP Gas | 383, 3.04%, see rank | 3.17% | 4.86% |
Electricity | 6,780, 53.89%, see rank | 39.67% | 36.68% |
Fuel Oil, Kerosene, etc. | 1,633, 12.98%, see rank | 10.31% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.11% | 0.12% |
Wood | 61, 0.48%, see rank | 1.37% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.03% | 0.05% |
Other Fuel | 12, 0.10%, see rank | 0.45% | 0.47% |
No Fuel Used | 34, 0.27%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 21403 Zip Code | Maryland | U.S. |
Total Housing Units | 12,997, 100% | 2,138,806 | 115,226,802 |
Utility Gas | 4,343, 33.42%, see rank | 44.82% | 49.42% |
Bottled, Tank, or LP Gas | 308, 2.37%, see rank | 3.21% | 5.03% |
Electricity | 6,363, 48.96%, see rank | 38.74% | 35.51% |
Fuel Oil, Kerosene, etc. | 1,868, 14.37%, see rank | 11.18% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.11% | 0.12% |
Wood | 56, 0.43%, see rank | 1.27% | 2.08% |
Solar Energy | 14, 0.11%, see rank | 0.02% | 0.04% |
Other Fuel | 12, 0.09%, see rank | 0.43% | 0.43% |
No Fuel Used | 33, 0.25%, see rank | 0.23% | 0.90% |
US Census 2000 data
| 21403 Zip Code | Maryland | U.S. |
Total Housing Units | 12,769, 100% | 1,980,859 | 105,480,101 |
Utility Gas | 4,266, 33.41%, see rank | 46.03% | 51.22% |
Bottled, Tank, or LP Gas | 238, 1.86%, see rank | 3.09% | 6.52% |
Electricity | 5,915, 46.32%, see rank | 33.09% | 30.35% |
Fuel Oil, Kerosene, etc. | 2,184, 17.10%, see rank | 15.99% | 8.97% |
Coal or Coke | 7, 0.05%, see rank | 0.13% | 0.14% |
Wood | 46, 0.36%, see rank | 1.00% | 1.68% |
Solar Energy | 37, 0.29%, see rank | 0.02% | 0.04% |
Other Fuel | 26, 0.20%, see rank | 0.37% | 0.39% |
No Fuel Used | 50, 0.39%, 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.