How Greystone Labs’ Machine Learning Algorithm Expedites Originations

Zac Rosenberg, director of the company’s innovation incubator, uses his engineering background to develop solutions that streamline the firm’s multifamily agency lending process.

Zac Rosenberg

Three years ago, Greystone Labs Director Zac Rosenberg set out to find a tech-enabled solution to simplify the firm’s agency lending process. By offering multifamily owners more comprehensive data faster, the firm is changing the way it serves its clients. A powerful machine learning algorithm is enabling these improvements, Rosenberg explained, with the potential to materially transform the way the firm underwrites loans.

MHN: When and why did Greystone decide to launch an in-house tech/innovation incubator?

Rosenberg: It came from humble beginnings around January 2015. I’ve always been the geek of the family. I have an engineering background and have worked at different tech startups. When I came to Greystone and started applying my engineering background as an analyst, I realized that some time-consuming tasks could be done faster with some easily scripted code. So it all started from small hacks, which ultimately became scripts that we used to automate manual tasks. One of the first scripts ended up bringing in an additional $150 million in loan originations.

What are the goals of Greystone Labs?

Rosenberg: When Greystone Labs started, I don’t think anyone knew how far we’d run with this. In the beginning, the goals for Greystone were to find more opportunities to quickly make someone’s life easier or to make the process more efficient. I wanted to work on something fun, something I’d be excited about at two o’ clock in the morning. It snowballed from there, and now our goals have become a lot more ambitious. We’re going to change the way you get a loan and access capital for multifamily and hopefully health care, too. My goal is to change the experience and the process of underwriting and risk assessment.

What is the concept behind the sizing app that you’re releasing?

Rosenberg: It’s a pretty straightforward concept. The idea is that if you’re an owner or borrower  looking at a property you own or would like to own, you enter an address in the app, and we’re able to look at that subject property as well as all of the properties in the vicinity. We can see what income those properties are generating and their expenses. That gives us a sense of the cash flows for properties in that area. Then we look at the volatility—analyzing current capital markets by today’s rates, Libor, Treasuries, investor spreads—and try to assess the risk. From there, we come up with loan options that we think would work for that subject property today.

What’s unique about it is that, if you just give us an address, we can give you ballpark figures based on assumptions that are heavily based on data we have on properties in the vicinity. If you already have some information that you can share with us—say, you already know the rents that the property is commanding—we can be even more accurate.

The response from users has been positive so far, I think, not because it’s such a revolutionary idea, but it’s the realization of an idea that should have always been there. It allows people to see all of their options, instead of just one type of loan product. There’s even an educational aspect to it because you can see, for example, what types of features a Fannie Mae loan offers that CMBS doesn’t.

I envision this going much further. Right now, we’re working to get valuations on properties across the U.S. in order to see where prices are trending. I want to get to a point where we can actually start looking at forward rate locks. I want our confidence in our rates to be so solid that we’re able to lock it instantly.

The platform is powered by machine learning. How does that work?

We are leveraging a clustering algorithm so we can provide the right type of useful information to the many different kinds of multifamily owners. Looking at an owner’s profile, we want to give them something that fits their portfolio strategy, activity level or the type of properties that interest them.

In analyzing data, we look at the performance of properties in relation to a subject property. What’s key is picking a good set of representative properties. There might be a ton of properties in the area, but they don’t all correlate with the subject property. Up until today, people have used traditional models to do this heuristically, asking if they were built around the same time or have the same number of units. We use machine learning to deepen that analysis.

Let’s say we look at one block with a few multifamily buildings. Over time, we can look at the impact of changing rents in one property on the others in the vicinity. If you see rents increasing in one and not another, then renters likely don’t view those as comparable properties. We feed that information into the model, which tries to predict which properties are most similar. And because we have the historical data, we’re able to tell if our predictions were right. As we repeat this with thousands of transactions, the algorithm learns how to spot perfect comparables.

Tell me about the pilot that launched in Seattle.

We wanted to see if there was a better way to connect with owners in a particular market. Instead of going in with a best guess of what kind of loan they may want, you might consider what their current loan is. In the past, for example, if an owner already had a Fannie Mae loan, one might pitch the same type of financing to them since they’re familiar with it. What we wanted to do this time was to apply a more data-driven approach to that process.

We wanted to consider what each owner’s focus areas were. If you’re good at flipping a property from Class C to Class A, for instance, then we’ll consider which Greystone product could help you do that more effectively. We also created an individual landing page for the owner of every property in the market, so they would see one of the properties they own on a map that shows similar properties, influential businesses in the area, how the building compares to the competition on the block and then give real financing options. We don’t give a range but a specific rate based on where we think the property would size out to today. And even if you don’t do a loan with us, we’re hoping the information we provide is still valuable.

What is the benefit of this tool to the end user? 

What I’d like to give multifamily owners a way to view their properties from the lender’s standpoint. When it comes to underwriting risk, lenders look at properties differently than investors do. Often, when we first speak to owners, there is a little bit of shock when they’re making a certain kind of cash flow, but we have to use a lesser amount because we can’t always include everything, such as certain ancillary fees. The shock comes from the owner learning they can’t get the type of financing they thought they could.

The sizing tool allows an owner to see how Fannie Mae, Freddie Mac or FHA would look at their property. This enables an owner to plan ahead for when they refinance the loan and assess how capital improvements might impact potential leverage.

How do artificial intelligence and machine learning technology enable better solutions for commercial real estate investors?

 Machine learning helps us see trends that we may not have caught ourselves. When you’re trying to predict the cash flow of a property, it’s not just the building’s historical operations that matter. It’s also important to think about the other businesses in the area, job growth and upcoming public works projects. There’s so much that factors into the desirability of the building, but we humans can only analyze maybe 10 things at once. Machine learning algorithms can analyze thousands of data points, which gives us greater ability to predict more accurately. We want to be able to walk the entire block, not just look at a single parcel.

How do you see technology disrupting multifamily lending?

I think the lending space will change because the way multifamily properties are built will become very different, due to methods like modular construction. You’re going to have more standardization. For the borrower, the development process will be faster, which means the lender needs to move faster, too. I don’t see the 60-day closing process continuing. We’re going to have to be very quick in locking in terms and delivering capital.

I don’t think there’s an area of the multifamily industry that won’t be impacted be technology. Take smart homes, for example. We might be able to tap into these technologies and extract data on how a property is operating and refinance a loan as soon as rates drop without any additional paperwork because we’re plugged into your property and we’re plugged into the capital markets.

What do you think the multifamily lending process or landscape will look like in five years? 

Five years from now, we’ll be able plug into property management software and evaluate in real time the risk level and see how the property compares to its peers, every day. As long as we have direct access to information, I think executing the loan will be the easy part. I’d like to think we’d be able to finance a transaction within a few hours because you’ll have very accurate underwriting up front. From a technology standpoint, we could definitely get there.


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