Imagine a software app that creates peace and understanding between landlords and tenants. How much value would that have in this world of constant rental turnover and strife?
This is the challenge taken on by Obligo, a New York-based fintech company that is using AI and machine learning to determine the level of risk of renters so that landlords feel safer about transactions. The company just announced a series B funding of $35 million.
“Our whole idea here is simple: We want to make renting an apartment or single-family home as easy as getting a hotel room,” Omri Dor, cofounder and COO of Obligo, told VentureBeat. “The main barrier to doing this has been the security deposit, which is as much [of] a pain to landlords as it is to tenants. It’s all about trust. If we can establish trust between landlords and tenants, then most of these barriers that cause strife to fall away.”
Open banking is an important factor for determining renter eligibility
At move-in time, Obligo’s platform uses open banking data and AI-based underwriting to determine a renter’s eligibility to rent a unit without putting down a deposit.
Open banking is a relatively new approach that requires all deposit-taking financial institutions to open up customer and/or payment data to regulated providers to access, use and share. This breaks up the monopolies of financial services and allows more players to enter the market.
Obligo has done AI- and machine-learning-based software development incorporating open banking in its platform.
“There are a lot of interesting technological challenges,” Dor said. “On the one hand, the unspoken heroes of all these kinds of products are really the integrations and the engineers building the integrations to work with them, the accounting systems that the landlords use — and these are various industry-standard ones that you’ve got to work with very seamlessly.” The more sophisticated landlords actually use Obligo’s API, Dor said.
The more challenging type of technology, certainly, is focused on machine learning and AI. “That’s where I think there’s really incredible progress that we’ve been able to make, because we get all this rich data that I mentioned,” Dor said. “We’ll take a bank account, but I’m not going to look at too much data … we don’t want to know where you go shopping, for example. We take the data and extract (meta-type) features. Then they’re basically aggregated and anonymized, so we don’t know exactly where you’ve been shopping. Here’s an example:
“We’ll look at the average balance in your bank account in the last six months divided by your monthly rent,” Dor said. “Is that number high or low? If that number is low, that means that there isn’t a lot of cash usually floating in your account, and that’s potentially a riskier situation. If there’s a lot of money floating around, usually that may mean that you are a safer renter. So we use these kinds of features.”
What Obligo’s AI engine produces
The AI engine of Obligo’s platform predicts which renters are most or least risky, in the sense that their lease could result in unpaid debt to the landlord, Dor said. Traditional solutions to predict renter risk had a few drawbacks that Obligo was able to solve.
First, Dor said, the data used for traditional solutions was not very rich, relying on items such as FICO scores, background checks, and total income. In contrast, Dor said, Obligo’s AI engine predominantly relies on very rich open banking data. This means that, with the renter’s consent, Obligo gains access to the renter’s bank account transaction history.
The second drawback of traditional attempts to predict renter risk is that they are usually not aware of the outcome of the lease. Those traditional models are set in stone, relying on old datasets that are not just outdated but typically biased due to the specific property portfolio from which they draw, Dor said. In contrast, since Obligo handles the move-out process, Obligo has visibility into the outcome of every lease, enabling a true machine-learning cycle to take place.
One of the key challenges that Obligo faces on its AI front is that it takes a very long time for leases to end. This means Obligo must wait a long time to observe sufficiently many lease-ends to allow its AI engine to learn, Dor said.
Getting deeper into the Obligo tech
Senior Engineer Ori Zviran, head of Obligo’s Core Technology team, answered a few detailed questions from VB on how this all works.
VentureBeat: What AI and ML tools are you using specifically?
Zviran: “We are researching on Jupyter notebooks with pandas, Scikit-learn, and Statsmodels (python libraries). We then deploy to production on AWS Sagemaker.”
VentureBeat: Are you using models and algorithms out of a box — for example, from DataRobot or other sources?
Zviran: “We are using Scikit-learn and Statsmodels.”
VentureBeat: What overall cloud solutions are you using? Are you an AWS shop and using a lot of the AI workflow tools there, for example, Sagemaker?
Zviran: “Yes, we use Sagemaker and our entire platform is hosted on AWS.” We use AWS-managed Mongo and Postgres.
VentureBeat: How much do you do yourselves?
Zviran: “We are piecing the model together ourselves on Python, Scikit, and of course relying on our own platform’s backend to get the data and preprocess it. We deploy the model to Sagemaker for production.
VentureBeat: How are you “labeling” data for the ML and AI workflows?
Zviran: “This is our secret sauce and our domain expertise. We need to define very carefully what is the lease ‘outcome’ that we are optimizing for. I’m afraid I can’t share more about this.”
VentureBeat: Can you talk about how much data you are processing?
Zviran: Our open banking data is not super high dimensional (no videos, images), and we dimensionally reduce it further. This means our models can be trained in memory pretty quickly. In the future, I’m sure we will need to use more sophisticated solutions to handle the increasing scale.”
Obligo’s value proposition
Landlords and property managers can use Obligo to simplify their move-in process, comply with the ever-changing regulatory landscape, and make their listings more appealing to renters, Dor said.
Obligo’s product suite provides a streamlined rental process that includes an option for landlords to do away with security deposits, although it’s always available if needed. Renters then proceed to make their move-in payments online.
At move-out, Obligo handles any end-of-lease deductions, refunding the deposit or billing the renter for any open charges. Landlords are off the hook for all of this, and if the prospective tenant is a qualifier, he or she is off the hook for a security deposit. All the conventional paperwork becomes unnecessary, Dor said.
Partnering with property owners
Obligo has partnered with more than 100 tech-savvy U.S. property owners and managers, including AIR, Beam Living (StuyTown), and Common.
“Obligo has achieved remarkable technological milestones, both in its ability to make predictions about renter risk and in its effective debt recovery process,” Yoram Snir, managing partner of 83North, said in a media advisory. “We believe the product suite that Obligo’s team is building may soon become an irreplaceable industry standard, in the U.S. and beyond.”
The funding round was led by investor 83North. Additional investors participating in the round include Highsage Ventures, 10D, Entree Capital, Alumni Venture Group, and MUFG.
Combined with its recent series A round, Obligo has raised $50 million in the last 12 months. The company said its new funding will be used to expand its product suite, grow market share and bring industry-changing rental solutions to millions of homes across the U.S.
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