One of the most important considerations in an apartment hunt is the comparison of utilities (electricity, water, cable/internet) costs at different apartment units. We propose a way to leverage existing Green Button data and make the comparison of electricity costs at different apartment units more comprehensive and user-friendly. There are two important metrics when comparing between candidate apartments: the total cost of the electricity bill, and the amount of electricity consumed in that apartment. If averaged over several similar apartments in the same building, these two metrics (and especially the latter) can provide a good sense of how efficient heating and cooling systems may be, since they often take up over half of electricity usage during the month. One apartment may cost the average occupant $100 in December in electricity, while an older one with less insulation may cost $140.
For this to be possible, the web-app would have to pre-populate with energy consumption data from apartments across a city. This could be possible by requesting consumption data from individual utility companies, who then pass the request along to their users. Individual users of the app looking to compare apartments and have access to their own Green Button data can submit their data to the app to determine their current energy consumption habits. The web-app would then use that consumption data to determine the amount that an individual would pay in electricity at each apartment of interest. There are two complementary effects happening at the same time. We would request electricity usage data from existing Green Button users at many apartments across the city through their utility company (this information would be kept anonymous to other users, since it would only be used as a point of reference), and users of the app would populate the database with their own information at the same time.
By making electricity consumption data trends more accessible to users, the public can more accurately assess apartments. A parallel effect which may be observed is that buildings which are less energy efficient will be rejected based on this data, creating an incentive for building owners of subpar properties to invest in upgrading their buildings to be more energy efficient.