[Future of Work Ep. 7] The Future of Real Estate Investing with Raj Tekchandani

Last updated on Sep 24, 2021

Episode highlights:

  • Real estate is a hyper-local industry. Therefore, investing in real estate needs to take into consideration factors such as population growth, employment growth, economic diversity, and economic stability. Using reliable data sources and systematized data analytics is especially important to understand how a property is going to perform and to determine the path of progress of the targeted area.
  • To stay ahead of the pack in a crowded market, real estate investors must gain in-depth insight into each investment opportunity by using all the relevant data at their disposal. In the not-too-distant future, SaaS solutions, machine learning, and AI will become the key to using data to its full extent in the quest for finding the best real estate opportunities.
  • Although data analytics are essential to estimate the value of a property, real estate investing companies still rely on locally visiting the sites to make sure the targeted area is desirable for renters. Factors such as the surrounding areas and the district's safety are decisive when it comes to long-term renting contracts.

Transcript (edited for clarity)

Claudia: What is the last thing that you learned that got you excited and why?

Raj: Learning is a daily journey and every day there is so much going on. My passion is to blend the use of technology with where my passion is, in real estate investing. I just keep an eye on the real estate and technology companies that are coming out from a venture startup side because I would love to invest in some of them. That keeps me excited all the time. A lot is going on, it's a space that has been slow-moving for a while. I think it's slowly catching up and there's a lot to be done there.

 

Claudia: What kind of data do you currently use when it comes to your investment portfolios? What do you think is preventing other investment firms or private investors from leveraging data to optimize their investment portfolios?

Raj: From an investor standpoint, there are a lot of factors that we look at. Real estate is local – that’s only partially true. Real estate is hyper-local, so hyper-local that you may have properties just a few blocks away from each other performing very differently. I use the word ’’perform” because, again, as an investment, we looked at these things as businesses. We have to see - why are they different? Why is this side of the railroad track so different from the other side of the railroad track? We take a lot of factors into consideration, and some of the most important ones are population and population growth. We want to be investing in places that are growing in population, not declining in population. The other thing you want to look at is employment growth. The population growth is one thing, but are there different kinds of employment factors going up and creating that employment for people coming into these states? We have to look at those factors. The economic diversity, as well, you don’t want your investment property to be depending on the employment growth of only one employment sector. Such as maybe an air force base or an army base - if that base shuts down people will leave, right? You want to look at economic diversity - you want to have access to retail, you want education, you want manufacturing, you want healthcare. You look at all those factors, and the worst out of five is the economic stability. We look at the median income, household income, landlord-friendly states. The property should be in what we call the path of progress. Let's take a city called Amarillo, Texas, for example. We invested there, we looked at a lot of factors. The properties were cheaper a couple of years ago, but there was a path of progress. We saw Starbucks coming in. We saw some of these larger chains coming in near the property. That was a clear sign that somebody has done a lot more research than we have, somebody like Starbucks, to establish an outlet there. We looked at all those factors as well and then we said „Is this a good place? Where is this going to place me in two years?”. And we do that by collecting all the data. We use three ways to find the data sources we need: census.gov, city-data.com, and other paid sources like CoStar.

 

C: How does data analytics work at Smart Capital?

R: We try to leverage some software tools and we also do some of our own data analytics. This is still in the initial stages – for now, we use all of the reports from CoStar - a company that does a lot of data analytics. We don't have our algorithms and engines because there is no need to re-engineer something that's already been built. There are other companies like Yardi Matrix that have done a phenomenal amount of data analytics. These are paid sources again, and help us get some of the research done for us.

 

C: How did working from home impacted what you're looking for in terms of investing? Do you think that rural areas are going to continue gaining people?

R: There are multiple factors, one of them being - what kind of jobs are in the area you want to invest in? If there are primarily tech jobs, those can be done remotely. Even then, though, tech professionals like to live in a place that is not too remote from their work, so they can choose whether they want to go to their office or work remotely. Work is going to be a hybrid model for the most part going forward but, for sure, there will be businesses that will require you to come to the office to work. Even with all the safety, precautions, I've already seen a lot of banks and businesses in New York asking people to come back. There's a rise in the suburbia market - that's no question. In pre-pandemic times, that was not the case, but now it is safe and affordable. If you can work from home, who wants to commute for a couple of hours each way into downtown areas? So that's probably going to stay like that. But then again, for us, we look at much broader things. We look at the safety of the community, because our business model is mostly apartment complexes. We look at safe neighborhoods, reasonably close to affordable workspaces, taking into consideration job growth, diversity of jobs, all of those factors.

 

C: What do you think is the future of work in real estate investing?

R: The biggest software that is used in this industry is still called Microsoft Excel. That needs to change because there's so much data being collected and some smart companies are already implementing new software. We have all these data from all these properties, we have been collecting rent data and the capital expenditure data on all of them. Let’s say a property in Texas had put in a swimming pool and that data is all captured. How much expense went into it? How much did it reflect on rent growth? Did people want to move into a property that had a swimming pool given it's Texas? This was only an example, but we can extrapolate it to cabinet colors, the flooring, countertops, all of that can be related to how much expense went into it and how much was the profitability from that. That data has been collected in property management systems for years. Now, somebody can go in and say - in the future if I'm buying a property that needs an upgrade, should I be putting black color appliances in it because the data tells me: ’’we put in the black appliances, but there was no cause and effect for that. Nobody was willing to pay extra rent for that.” What if we add a swimming pool or add a dog park in this zip code? If your zip code is Z, adding a dog park is not going to make a difference. If your zip code is X, then based on the last five years' data, adding a dog park has done a tremendous amount of value add to that property. There's a lot more that goes into these factors and data that can be mined, and then you can say - okay, five years from now, if you put this kind of upgrade in a property, that will give you this kind of result. Nobody has seen that before, but it's all here, in the data, so let's make that investment. I think that is going to be the future in one of those areas.

 

C: How do you see the industry getting from where it is today to where it needs to be in the future to be able to leverage all of this data?

R: I think there's a lot of investments already being made into software technology. As I said, there are companies out there that are trying to take that path. No more of Excel, right? If you want to do underwriting on a property, just upload something and they will figure it out. They will analyze the data in the cloud and then present to you a pro-forma based on what they have learned on the data they have collected. This is what you can expect over the next five years, versus me going into a separate tab in Excel for year one, and writing what can I expect, then doing the same for year two. These guys will come with a five-year pro-forma for you based on the data and the location of the property. Real estate is hyper-local so if you're saying ’’my zip code is 7 8 7 5 0” then based on that they will tell you what the values of the properties around you are, and whether this property that you're buying makes sense or not. Because they have analyzed all the properties in the three-mile radius or in the five-mile radius, and this is what they've been doing in the last three to five years. This is what the data tells us. We'll do the underwriting for you. Very smartly, very intelligently, because we have mined the data in the past, the data that is available in the property management system. 

 

C: What's a cool project or initiative that you've been working on that you'd like to share with us?

R: This is a work in progress but we have these underwriting models that we've been using over the past months and there are a lot of people writing models that we have been using. I think there are a lot of people in the real estate industries, from the REITs and successful syndicators that have figured out what underwriting works for them. They take the current income, it's called the T12 ("Trailing Twelve Financials") income, the expenses from the last year, and then, depending on where they are, they predict the taxes and their cash flows going into the next few years. And that's what we present to our investor saying ’’Okay, this is a property we are investing in. This is an underwriting model. These are our assumptions and, based on that, this is what the outcome would look like, this is why you should invest with us.” It's still a very manual job and it has taken years to come to a place that we used very smart Excel spreadsheets, but one thing that I want to do, and I'm looking to do it with some of my partners, is to make that available to everyone through a cloud-based underwriting model. We would request from them access to the data collected from the brokers, asking them to upload it to our website. The outcome will be a suggestion for whether you should invest or not, and it will make it easier for you to see whether you should be investing in this property or not. We are not investment advisors to say whether you should make this decision or not, but we will give you a lot of completed work that you would otherwise usually have to do manually. We will take a lot of the red flags that we have learned about over the years into consideration. For example, at the end of the day, it's Excel spreadsheets - you can make it look any way you want, right? If I change the assumptions from a rental growth being 3% to 5%, everything will look good. But then when you do that in our system, it will tell you that you cannot do that or that it is not advisable to do that. We start with the user's spreadsheets. We don't want them to have to type anything. We just take them in, clean them digitally, run our models and give out our guided suggestion of what this property is worth or what are the assumptions that they have been making that aren't correct. That's one of the projects that I'm very passionate about.

About the author

Claudia is a data scientist, consultant and trainer. She is the CEO of Edlitera, a data science and machine learning training and consulting company helping teams and businesses futureproof themselves and turn their data into profits.

Before Edlitera, Claudia taught Computer Science at Harvard, and worked in biotech (Qiagen), marketing tech (ZoomInfo), and ecommerce (Wayfair). Claudia earned her degree in Economics from Yale, with a focus on Statistics and Computer Science.