Machine learning algorithm processes 56 million data points to identify the greatest investment potential across the U.S.

 

(August 28, 2018) CHICAGO – Enodo, an automated underwriting platform for multifamily real estate, recently applied its price and cap rate prediction algorithms to its database of 1.7 million assets nationwide to identify the nation’s most statistically undervalued multifamily properties.

According to Enodo’s research, the assets identified by its algorithm are statistically underpriced for their respective markets, and rents in these properties can be raised by as much as 20% with minimal loss of occupancy.

“By focusing our efforts on identifying undervalued assets, we’ve created an entirely new line of business for Enodo,” said Enodo COO, Thomas Delaney. “Our clients are continually searching for the needle in the haystack, and Enodo just invented the metal detector. The implications are huge and we will be cautious and diligent as we explore this new opportunity.”

Using machine learning Enodo developed a method to systematically identify undervalued assets based on their individual performance amongst competing properties. The algorithm flags properties that are either prime value-add candidates or properties that are charging under market rate rents. After identifying the undervalued assets, Enodo applies its machine-generated cap rate to calculate the market value for each asset as it is currently performing, and identifies the potential yield if acquired.

“Enodo currently predicts market rents nationally with under 5.5% median error, and cap rates within 0.35% of actuals. This gives us all the insight we need to determine when an asset is renting below market, and what the incremental income from rent increases will be worth to investors in each market,” said Enodo CEO, Marc Rutzen.

Enodo trained its algorithms on data from more than 21 million apartments, 12,500 operating expense statements, and over 3,000 closed multifamily transactions nationwide to develop its automated valuation model (“AVM”). By predicting rent, operating expenses, and cap rate separately, Enodo is able to accurately replicate the income approach used by multifamily appraisers.

“This advancement by our data science team has created the ability for Enodo to monitor the entirety of the multifamily market and automatically identify opportunities to acquire properties at below market value,” said Rutzen.

Enodo launched its SaaS platform for subscription in January of 2018 and has amassed a noteworthy roster of clients, including Mid-America Communities (MAA), Lennar Multifamily Communities (LMC), Edward Rose & Sons, Draper & Kramer, Dinerstein Companies, and Waterton Associates.

About Enodo:
Enodo is an automated multifamily underwriting platform that helps users analyze opportunities faster and make better investment decisions backed by data science. Utilizing machine learning, the platform collects, cleans and analyzes real-time multifamily rent and unit availability data from over two million properties nationwide. Enodo’s core features allow users to calculate optimal rent, identify statistically relevant comps, and test value-add strategies. Learn more at www.enodoinc.com.