10473 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 10473 Zip Code | New York | U.S. |
Total Housing Units | 20,427, 100% | 7,255,528 | 116,211,092 |
Utility Gas | 10,408, 50.95%, see rank | 56.12% | 48.85% |
Bottled, Tank, or LP Gas | 405, 1.98%, see rank | 3.36% | 4.86% |
Electricity | 1,290, 6.32%, see rank | 10.26% | 36.68% |
Fuel Oil, Kerosene, etc. | 7,722, 37.80%, see rank | 26.24% | 5.86% |
Coal or Coke | 9, 0.04%, see rank | 0.26% | 0.12% |
Wood | 29, 0.14%, see rank | 2.04% | 2.12% |
Solar Energy | 20, 0.10%, see rank | 0.03% | 0.05% |
Other Fuel | 164, 0.80%, see rank | 0.97% | 0.47% |
No Fuel Used | 380, 1.86%, see rank | 0.72% | 1.00% |
ACS 2008-2012 data
| 10473 Zip Code | New York | U.S. |
Total Housing Units | 20,297, 100% | 7,230,896 | 115,226,802 |
Utility Gas | 9,687, 47.73%, see rank | 54.92% | 49.42% |
Bottled, Tank, or LP Gas | 349, 1.72%, see rank | 3.16% | 5.03% |
Electricity | 1,104, 5.44%, see rank | 9.44% | 35.51% |
Fuel Oil, Kerosene, etc. | 8,641, 42.57%, see rank | 28.80% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.26% | 0.12% |
Wood | 14, 0.07%, see rank | 1.96% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.02% | 0.04% |
Other Fuel | 160, 0.79%, see rank | 0.86% | 0.43% |
No Fuel Used | 342, 1.68%, see rank | 0.57% | 0.90% |
US Census 2000 data
| 10473 Zip Code | New York | U.S. |
Total Housing Units | 19,354, 100% | 7,056,860 | 105,480,101 |
Utility Gas | 10,487, 54.19%, see rank | 51.75% | 51.22% |
Bottled, Tank, or LP Gas | 517, 2.67%, see rank | 3.37% | 6.52% |
Electricity | 1,600, 8.27%, see rank | 8.72% | 30.35% |
Fuel Oil, Kerosene, etc. | 6,104, 31.54%, see rank | 33.11% | 8.97% |
Coal or Coke | 17, 0.09%, see rank | 0.14% | 0.14% |
Wood | 0, 0.00%, see rank | 1.17% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.04% | 0.04% |
Other Fuel | 247, 1.28%, see rank | 1.04% | 0.39% |
No Fuel Used | 382, 1.97%, see rank | 0.66% | 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.