If you’ve ever shopped for a house or an apartment, you know it can be hard to put your finger on exactly what it is that makes certain units cost more than others. Sure, you’re paying a premium for hardwood floors, but how big of a premium, exactly? And how much do you pay for proximity to a train station?
For landlords and real estate developers, getting granular data on how much renters are willing to pay— and for what — would be a complete game changer. And now there’s a Chicago startup dedicated to providing it.
Founded this January, Enodo Score is a predictive analytics platform centered specifically around untangling the factors that determine the value of multifamily rental units. For instance, if you’re a landlord and you know you’ll have a unit opening up soon, you can use the platform to determine whether you should renovate the kitchen before putting it back on the market.
The platform provides a simple score designed to give users a sense of how renovations and other changes affect a property's value. It also lets users “test” out new buildings at different locations, helping real estate developers determine what kind of building is best suited for a particular location — or vice versa.
Enodo Score’s predictions draw on a variety of private and publicly available metrics, ranging from census data to extensive information about approximately 800,000 multifamily properties across the country. Over time, Enodo Score's machine learning algorithm will also draw on user input to get a better sense of market shifts.
Currently, the company plans to target real estate professionals, from investors to developers, appraisers and real estate brokers. But since the platform is calculating risk-adjusted returns, CTO Marc Rutzen (pictured above) said their software could also be an attractive tool for lenders, who need to make educated guesses about their borrowers' prospects.
“If you can look at every property in a particular market and say, ‘This is the relative risk of each of these properties compared to each other,’ and have an apples-to-apples comparison, that hasn’t been offered to the lending community before,” he said.
Though the functionality at the heart of the platform is a complicated multivariate regression analysis, the application has been designed with non-technical users in mind. Property information is overlaid on a stylized map, with “switches” for enabling and disabling particular features and amenities.
The members of the team, whose backgrounds are all in real estate, got their start doing this kind of analysis through consulting jobs — among them an analysis of how the real estate market of current day Logan Square compares to Lakeview 30 years ago. But after running a number of projects, they came to the conclusion that the team was too small to keep pumping out analyses and decided to build software that could do it on its own.
Enodo Score plans to make its platform available to beta testers this summer and is hoping start bringing on paying customers by the end of the year. On Thursday, they’ll be pitching alongside 20 other real estate startups at the DisruptCRE conference at the Chicago Board of Trade.
“If they can get the idea in 45 seconds and they’re very interested, I think that will be a good indicator of where this thing is going,” said Rutzen.
Images via Enodo Score.
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