20910 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 20910 Zip Code | Maryland | U.S. |
Total Housing Units | 19,018, 100% | 2,155,983 | 116,211,092 |
Utility Gas | 10,279, 54.05%, see rank | 44.54% | 48.85% |
Bottled, Tank, or LP Gas | 73, 0.38%, see rank | 3.17% | 4.86% |
Electricity | 7,893, 41.50%, see rank | 39.67% | 36.68% |
Fuel Oil, Kerosene, etc. | 471, 2.48%, see rank | 10.31% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.11% | 0.12% |
Wood | 6, 0.03%, see rank | 1.37% | 2.12% |
Solar Energy | 5, 0.03%, see rank | 0.03% | 0.05% |
Other Fuel | 111, 0.58%, see rank | 0.45% | 0.47% |
No Fuel Used | 180, 0.95%, see rank | 0.35% | 1.00% |
ACS 2008-2012 data
| 20910 Zip Code | Maryland | U.S. |
Total Housing Units | 17,892, 100% | 2,138,806 | 115,226,802 |
Utility Gas | 9,048, 50.57%, see rank | 44.82% | 49.42% |
Bottled, Tank, or LP Gas | 115, 0.64%, see rank | 3.21% | 5.03% |
Electricity | 7,975, 44.57%, see rank | 38.74% | 35.51% |
Fuel Oil, Kerosene, etc. | 542, 3.03%, see rank | 11.18% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.11% | 0.12% |
Wood | 7, 0.04%, see rank | 1.27% | 2.08% |
Solar Energy | 6, 0.03%, see rank | 0.02% | 0.04% |
Other Fuel | 116, 0.65%, see rank | 0.43% | 0.43% |
No Fuel Used | 83, 0.46%, see rank | 0.23% | 0.90% |
US Census 2000 data
| 20910 Zip Code | Maryland | U.S. |
Total Housing Units | 16,259, 100% | 1,980,859 | 105,480,101 |
Utility Gas | 10,421, 64.09%, see rank | 46.03% | 51.22% |
Bottled, Tank, or LP Gas | 215, 1.32%, see rank | 3.09% | 6.52% |
Electricity | 4,407, 27.10%, see rank | 33.09% | 30.35% |
Fuel Oil, Kerosene, etc. | 1,010, 6.21%, see rank | 15.99% | 8.97% |
Coal or Coke | 0, 0.00%, see rank | 0.13% | 0.14% |
Wood | 0, 0.00%, see rank | 1.00% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.02% | 0.04% |
Other Fuel | 118, 0.73%, see rank | 0.37% | 0.39% |
No Fuel Used | 88, 0.54%, 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.