About the data

Where did your consumption data come from?

We obtained residential energy consumption from the utility, and then gathered apartment data manually. The utility data is screened in compliance with open records laws, stating monthly consumption (in kilowatt hours, kilo gallons, or therms) for an address. We matched the utility data to apartment data based on address.

Where did your property data come from?

The property data was created by blending consumption data with property appraiser data to get a basic list of property names and addresses. The property details (square feet, beds, baths, and business name) were gathered manually based on individual property websites and a few apartment aggregation websites.

How did you select properties to appear on the website?

We identified properties based upon the consumption data. The consumption data contained an address, and multi-dwelling units (MDUs) were identified via an apartment number field in the export. If our utility data export did not contain records inside an MDU, we exclude that MDU from our list. Many MDUs include utilities costs into the rent, and we do not have data to rank them.

How accurate is the property information?

The property information is a best effort, and should not be consider authoritative. In many cases, properties did not have websites so we were unable to gather any information about the units. Properties and rents change frequently. A comprehensive survey is outside the scope of this project. The data will be refreshed minimally once a year before the peak lease-signing months.

What are your plans for improving the property information?

We plan to improve our coverage of the rental market:

  • Obtain sufficient utility data to include MDUs that bundle electricity with the rent.

  • Coordinate with property management firms to include single family homes that are commonly rented out.

How are the rankings determined?

The rankings are currently simple averages. For each property, month and year we discard readings 3 standard deviations over the mean, then average the remaining to get a single number representing that property in a given month and year. This is graphed in the line chart on the comparison results page.

These monthly numbers are averaged to get the final estimated electric consumption, cost, and CO2e. The map pins are colored according to this final average of all consumption data we have available.

What about vacant units, partial bills, and other factors

These are not accounted for, our hope was the long-term average and outlier filtering would suffice.

What about apartment square footage?

Unlike our GainesvilleGreen.com sister project, we do not normalize consumption based on apartment size. There is no data available to associate an apartment number (which comes in the consumption data) with a specific floor plan.

What are your plans for improving the rankings?

We are planning to investigate statistical models to make better comparisons with our limited data set, (in addition to expanding our property information data). We also plan to examine the consumption data rigorous and attempt to identify unit characteristics based upon consumption.

Other questions?

Have a question, suggestion, or problem with our data? Please fill out our Feedback form and we'll get on it.