The decision to upgrade to hardwood floors and granite counter tops or to add hot new amenities is usually based off industry trends and gut instinct. But that’s not always the case. Amenities and finishes differ from market to market, and some markets are willing to pay more than others for those upgrades.

A company called Enodo Score wants to standardize the renovation process by tapping into the apartment industry’s wealth of data to help owners and investors decide if the extra expense for these upgrades is worth the potential return. Launched earlier this year, Enodo Score is a predictive analytic platform that uses a regression analysis to track the relation between rent and specific apartment features, such as gyms, bathtubs, and walk-in-closets.

“There’s been no quantification of adding or removing amenities or locating a development in a specific neighborhood,” says Enodo Score co-founder Marc Rutzen. “This conducts a full market analysis on a granular level.”

The platform evaluates three primary values: how a new property would perform, how a current property could perform with different features, and how a current property would perform if it was replicated in other markets. A building’s performance in each market is quantified in its Enodo Score.

A sample of Enodo Score's interface

The objective composite score is based on a combination of public and private data, including census data and data from roughly 800,000 apartment properties across the country. Enodo Score’s learning algorithm will also rely on user data in the future to monitor shifts across markets.

The platform goes beyond the single Enodo Score, as well. It breaks down the incremental rent increases of various features. For example, if a developer wants to add a rooftop pool to a project because it would make the property standout, he could identify whether the market would really pay the expected return.

Enodo Score is currently conducting a beta test with multifamily partners over the summer. The biggest challenge Rutzen says his team is confronting is making sure the data is accurate.

“If you search for market values in various different platforms, you’re probably going to get various different results,” he says. “We’re trying to solve this by analyzing the data in bulk for one aggregate data set.”

Rutzen says his team hopes to have a product on the market by the end of the year.