At Markerr, we believe insights are a two-way street: our customers rely on us for insights into demand and supply dynamics, and we rely on them to learn how they use data to develop new approaches to acquisition and underwriting decisions.

That’s why our Chief Marketing Officer Erin Crapser was delighted to sit down with Brad Dillman, Chief Economist at Cortland to discuss how data is leveraged in the investment process.

We’re sharing our conversation with Brad in three installments. In Erin and Brad’s initial conversation, they talk about the role of the Cortland Research Team and the impact they have throughout the company. 

What does it mean to be Chief Economist at a multifamily investment, management and development firm like Cortland? 

Brad Dillman: The Head of Research comes down to thinking about what the world around us is doing. Real estate firms very often focus just on their own operations, their own thinking about how an asset underwrites, within the confines of how they approach a value add execution. Whereas the reality is that a lot of what’s determining the course of a real estate investment is the macro cyclical and what’s going on beyond what is our own control as a firm. 

It’s also about quantifying that view. So it’s about having this proprietary view but quantifying it in a way that allows it to be unwrapped by other people elsewhere in the firm. So if I have to walk around and give my view on every single thing, it’s impossible. But when we operate our own forecasting systems, our own market scoring models the story can all get wrapped up in data. I often liken it  to being something like DNA. If you think about the story that our own DNA tells about who we are, this quantified view is sort of the same thing that can be unwrapped by anybody in the firm. It can be used directly in things like underwriting models.

Erin Crapser: Can you tell me about how your team works with the rest of Cortland?

BD: It can be in a couple of forms. You will have an element of internal consulting where, in effect, it’s the glue that holds the different verticals together. This can often be about sharing quantitative information and insights to summarize what we think the data is telling us about what we should be doing. Should we be slowing down or speeding up? 

Of course in the context of the coronavirus pandemic last year this was a very critical function in the firm. Other times, it can just be a direct input. For example, our system produces proprietary, multifamily rent growth forecasts at the submarket level, and in fact, at the different parts of the distribution at the submarket level. Now of course there’s a whole world of other things that can be said about modeling and that sort of geographic scale, but the point is that we’re creating our own metrics that are tied to information that we can get in the firm itself from our own operating data (and our own view about the go forward) and then quantifying that. And then that turns into something that’s a direct input into our underwriting model. 

So you may have a scenario where it’s like, here’s a team of leasing agents who want to understand just what’s going on and in the housing market. Let’s have a presentation and talk about the macro-cyclical all the way down to we need a very discrete data point for a specific project at a specific time. 

So, from that perspective, you really have to be ambidextrous. You have to be somebody who’s technically capable, quantitatively inclined but also capable of communicating and seeing the bigger picture. 

EC: How do you go about standing up a data team in an organization that might not feel that there’s a need or priority for a data team?

BD: I have to credit our Vice President of Analytics, Erin Fisher, with standing up our data team. This began back in 2017 and was a need to get our own operating data — about what’s going on within the firm — to the appropriate parts of the organization and to be able to track those trends through things like dashboards and the use of DOMO, as an example. Over time that grew with our capacity because we were able to buy data and take it to the kind of analyses that I do that are really about things outside the firm — that being this big forecasting system that we put together. 

From the perspective of standing up a data team, it’s critically important and it’s all about internal communication. It’s about people at the top understanding what the vision of that really is. And to be perfectly blunt, that’s a big challenge in something like real estate where a lot of the people who are highly successful at real estate, let’s just say, didn’t get there because they knew how to do a linear regression by hand. These are people who maybe knew how to organize something like financing or just bought at the right time in the cycle or are just really astute on the sticks and bricks side of real estate. 

So to really get them on board with understanding about what’s happened with data over the last 10 or 15 years, how it’s become more and more important, how we can use it to generate alpha in the company, and to demonstrate to our investors that we are doing so. At the same time that we’re compiling all this information and all these projections for our own use, investors like to see that too. So, at the end of the day, this function helps us raise capital better and deploy capital better. Those two really feed off of each other. There’s plenty of skilled professionals out there. There’s plenty of data scientists. 

I don’t think finding talent in any way, shape or form is an issue. I think the real issue comes down to internal communication and people at the right parts of the company structure understanding what the purpose of the data team is, what the purpose of estimates and forecasts are and how they can be utilized. And at the same time, not being threatened by this. You also will get people who may be afraid that they’re going to get automated out of role or that they’ll become less critical to the investment process and they would be uncomfortable because of that. So, there’s the right kind of communication about how data can be used and how the organization itself will evolve if it’s used.