20036 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 20036 Zip Code | District of Columbia | U.S. |
Total Housing Units | 3,756, 100% | 267,415 | 116,211,092 |
Utility Gas | 1,798, 47.87%, see rank | 59.12% | 48.85% |
Bottled, Tank, or LP Gas | 6, 0.16%, see rank | 0.93% | 4.86% |
Electricity | 1,721, 45.82%, see rank | 35.97% | 36.68% |
Fuel Oil, Kerosene, etc. | 122, 3.25%, see rank | 2.45% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.02% | 0.12% |
Wood | 0, 0.00%, see rank | 0.01% | 2.12% |
Solar Energy | 19, 0.51%, see rank | 0.05% | 0.05% |
Other Fuel | 10, 0.27%, see rank | 0.51% | 0.47% |
No Fuel Used | 80, 2.13%, see rank | 0.94% | 1.00% |
ACS 2008-2012 data
| 20036 Zip Code | District of Columbia | U.S. |
Total Housing Units | 3,974, 100% | 261,192 | 115,226,802 |
Utility Gas | 1,965, 49.45%, see rank | 61.97% | 49.42% |
Bottled, Tank, or LP Gas | 15, 0.38%, see rank | 0.94% | 5.03% |
Electricity | 1,825, 45.92%, see rank | 32.85% | 35.51% |
Fuel Oil, Kerosene, etc. | 93, 2.34%, see rank | 2.97% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.02% | 0.12% |
Wood | 0, 0.00%, see rank | 0.02% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.01% | 0.04% |
Other Fuel | 37, 0.93%, see rank | 0.52% | 0.43% |
No Fuel Used | 39, 0.98%, see rank | 0.70% | 0.90% |
US Census 2000 data
| 20036 Zip Code | District of Columbia | U.S. |
Total Housing Units | 3,017, 100% | 248,338 | 105,480,101 |
Utility Gas | 1,516, 50.25%, see rank | 65.42% | 51.22% |
Bottled, Tank, or LP Gas | 50, 1.66%, see rank | 1.78% | 6.52% |
Electricity | 1,156, 38.32%, see rank | 24.17% | 30.35% |
Fuel Oil, Kerosene, etc. | 267, 8.85%, see rank | 6.86% | 8.97% |
Coal or Coke | 0, 0.00%, see rank | 0.03% | 0.14% |
Wood | 0, 0.00%, see rank | 0.04% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.02% | 0.04% |
Other Fuel | 28, 0.93%, see rank | 0.69% | 0.39% |
No Fuel Used | 0, 0.00%, see rank | 1.00% | 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.