The Importance of Data in Business & Strategy

 

PropertyQuants was founded by a team of Ph.D. ex-quant traders with a track record of successfully deploying automated trading models in global financial markets.
 
They're bringing the quantitative revolution to real estate investing. Property investing today is often based on incomplete and inadequate information. Analysis, forecasts, and decisions are not rigorously produced and are sometimes driven by emotion, untested assumptions, and rules of thumb. Portfolio considerations in real estate are often ignored.
 
PropertyQuants works to improve real estate decision-making. Real estate decision-making should be evidence-based, data-driven, and systematic instead. PropertyQuants are leading the way forward by applying quantitative finance and data science methods to global real estate, helping investors beat the market.

 

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The Importance Of Data In Business & Strategy | Nelson Lau & Zain Jaffer

 

On this show, we have Nelson Lau, the Founder and CEO of PropertyQuants. I'm excited to have Nelson because he's an expert in AI, and he was my teacher. I did a course on AI and Data Science, and that's how I met Nelson. How are you doing?

 

I’m good. Thanks so much for having me on the show.

 

It's good to have an AI expert on the show because I felt like there couldn't be any industry more ripe for disruption than the one we're in now in real estate and property tech.

 

In part of the course that you did with us, we talked about some of the monetization opportunities and what's going on. We see something in every space, from setting the data to building the indexes to ultimate evaluation, real estate forecasting, and all of the new stuff like image recognition and chatbots. There are tons of stuff going on.

 

Before we dive into all of that, you've got a fascinating background because you were educated in the finance world, where there is a lot of focus on quantity and trading. You go all the way to PhD level. Why don't you tell us a bit about your journey and how you got here?

 

That's in a nutshell how I got here. Starting from the beginning, in university, I did Economics and Math-Stat. I was always interested in stock trading. In that year, they had these books about technical analysis of the markets. They showed these patents and stuff. I was like, “Is that right? Does this stuff work?” There are these two thick books. By a lot of luck, I happened to be at Columbia. I was in New York. There are a lot of hedge funds, and there was an opportunity to do an internship.

 

Luckily enough, I got into a place where they were doing everything in a data science and data-driven way. They had this piece of software that would enable you to propose a hypothesis when X, Y, and Z happened, what happens next? It was able to apply this data-driven, evidence-based way of verifying, “Is this pattern useful?”

 

It turns out that most of them don't work, but there are a few things that are significant and predictive of the markets. That's where I cut my teeth, and I always have this idea that if we're going to invest, we should be looking at the data and finding reliable signals about what we should be doing next, like buy and sell in particular markets.

 

 

If you are going to invest, you should look at the data and find reliable signals about what you should do next, like buying and selling in particular markets. 

 

 

Along the way, I decided to continue my education. I got into data science. How do we take a business problem and make it a math problem? How do we solve that? What are people doing? I continued into asset management outside of sovereign wealth fund GIC in Singapore, looking at tactical as an inpatient for a bit and high-frequency trading. I’m building these data-driven models to invest in.

 

I was looking at real estate investing. I was investing for myself in a couple of different places, London, Tokyo, and Singapore. I was looking around and saying, “How are people doing this? This seems different from what we're doing on a day-to-day basis in the financial world.” We spent some time poking around and saying, “People are doing this, and we don't know that people are taking quantitative bent.”

 

After looking for some time, we determined that there seems to be a little bit of a gap in the way that we think about it, and maybe real estate is a little bit where we were in finance several years ago, when you know you had these quantitative funds, and that was still a new thing. When I was starting my career, there were some. You had the renaissance technologies and the bridge ones. They could have been doing it before then, but it was still a relatively new thing.

 

One of the statistics we like to point out is that if we think about it now, one-third of every dollar in hedge funds is invested in quantitative hedge funds. Depending on who you ask the statistics from, 60% to 80% of all trades are done by computer algorithms in the stock market. Can we bring the same thing into real estate? That's how we got here. A lot of that is beginning to move away, but we might still be at the earliest.

 

You casually say, “The rest of the industry is several years behind.” Can you imagine if the finance industry or the internet world were several behind? We wouldn't be doing this. Our world changed so much during COVID, and I know the property and real estate are changing a lot too, but this is the one thing that shocked me.

 

When I came into real estate, I was horrified at how much reliance there is on spreadsheets, how the industry thrives, and information asymmetry, where you're relying on an opinion from a human being, the gut, and the instinct. There's a place for gut and instinct, but it's falling behind. Infrastructure is lacking.

 

When I was starting out on the trading desk, they were starting to use FX platforms. Now, most trading on FX is algorithmic. You go on the platforms, or you turn the futures, and so on. It was the beginning of that. You would hit something on what used to be called ethics, and you'd get a coach. You didn't 100% know where the market was, but it was already a lot better because you got an electronic code. From all these different brokers, you get like a CD code, a Goldman code, and things like that. You keep who you want to deal with. The traders were already telling me that this is so much better because several years ago, nobody knew what Sterling versus USD was.

 

If you call different guys, you might get a spread, and it was way too hot. You can take advantage of that all-day critical view. The thing about real estate is the way it is now. How much is this building worth? How much should it be traded for? How much did the next-door building trade for? Things like that still remain OP, but that's beginning to clear up. We're beginning to see a lot more organizations across the world, more companies pulling this data together, trying to aggregate that. That's why I say several years behind in that sense.

 

 

In the finance industry, you talked about simple examples, like the mispricing of assets and how that allows someone to make a quick buck. Is that what's going on in real estate? People are making quick, easy money because of some arbitrage opportunity they've seen, and the data isn't there.

 

There's still a lot of that going on, right. This is a broader conversation topic. We got into the space saying, “We want to take data-driven methods and use that to make advisory or to invest and so on.” Along the way, we chatted with some of these larger companies and said, “We can help you make sense of this data and figure out insights.”

 

They said, “This is great. What do you need?” We'd say, “You need to share with us the transactions you're seeing or the data sets that you have.” There's an absolute caution or apprehension in that because knowing what the price is, is the alpha for a lot of real estate guys, especially commercial real estate. I agree with that.

 

What does that mean? Does that mean that the market's going to see a shift as it did in the FinTech or the finance world where your average consumer now versus institutions? Institutions are becoming more prevalent. I know there's this seesaw and a fight constantly with the Reddit crowd and AMC, GME type of stuff happening, but what's going to happen in real estate? Is it going to be that the larger institutions who have dates are going to dominate, or do you think it'll stay the same and will be fragmented?

 

There are a couple of paths. Nobody knows for sure. The data transparency piece is increasing. The US is a particularly good place for this. First and foremost, a lot of the transaction data, especially residential, is recorded in the county offices. You have all these companies trying to put it together. The house and atom data, a bunch of other people are putting this together.

 

Over time that becomes more cost-effective to access, and you have a couple of players who might become the Bloomberg or Thomson Reuters of real estate. We still get discussion, like which market has gone up the most and what is the current yield? We are trying to put that together for a couple of markets. We monitor and try to have a dashboard that we can then use and on-sell to tell people exactly what's going on, where we think the current yield is, and all of these factors and so on.

 

The fact that you do that versus in finance, this is free on Apple Finance or Google Finance. If you log into any of your trading apps, they'll show you this stuff. That's one path. We go down that way. Another path is that there are parts of finance that remain a little bit more well picked. Even with the corporate bonds, there is some information, but it is not as clear. It's a little bit fragmented. Maybe we ended up a little bit there, but even that is changing a little bit.

 

There is a little bit more push for the electronic trading of corporate bonds. There is some activity in that space that is making it more. In terms of who's doing the trading, here are some interesting articles. Some of the iBuyers are stepping in and buying from the developers before the individuals. Maybe that happens. They're more informed.

 

 

Creating culture is understanding that sharing data creates a better potential for all of us together. 

 

 

The other piece that determines it is government regulation. The governments typically step in with a lot of legislation, especially residential real estate. I think that didn't happen so much in finance because it's viewed as more open less legislation is better. People should be knowing what they're doing versus real estate.

 

Do you see in the market that larger firms are building out data science and analysis functions? Are they hiring more? There's interest now. I did your course, too, because I felt that AI was going to disrupt real estate. It’s probably one of the biggest opportunities ever. Where is the disruption happening? Is a large firm hiring, or is it more individual startups? The large firms are going to be disrupted because they're not embracing AI.

 

I think both. Right there, it's a lot of interest. One of the things that we often point out is, as we talked with people, we looked at those KPMG PropTech surveys, 2018 and 2019. Halfway, participants have all the technologies out there, like data science, AI, VR, and AR. They have tons of stuff. Half of them say that it's going to be this data science, AI, and big data are going to have the biggest impact on the real estate industry.

 

That's a way to show that there are still 80% of companies who used to go in terms of harnessing data-driven decision-making. There's a skills gap. They're also saying that of all the people leading data science or digital transformation efforts in real estate companies, only 5% of people have the right background for that.

 

What are we seeing? We saw people coming in. We ran this course on teaching data science machinery learning for real estate. There are some large companies producing analysts and researchers to the cost. Especially investment funds will say, “We want to use these methods. Can we send a couple of images for this?” There are startups in the space doing that as well.

 

What did you say? Five percent of people in these roles are only qualified, and 95% aren’t for the role.

 

That's what the survey suggests. It was the KPMG PropTech in 2019 saying of the people leading a digital transformation effort. In real estate companies, only 5% have a background in data analytics, and the others are all sorts of other things.

 

Many of the people reading this blog are trying to figure out the digital transformation and the first thing you do in real estate, and you realize the industry has been disrupted. You get excited about the idea of PropTech, and you figure out how you can implement it. Either you use it as a vendor, bring in a product or service, and pay for it, or you go higher and build internal competency. Talk us through it. What advice do you have for folks at large companies? How do they lead the digital transformation, and specifically, how should they think about AI? What do they need to have in place for AI to make sense, and any practical advice for these types of people?

 

 

First and foremost, it depends on what you're doing. Some large companies will be generating data that's useful. If you are a brokerage firm, you're going to be generating tons of data that you could use internally about where various markets are at, operating costs over the years in buildings, all of these things that could create insights that will be unique to you, or help your clients.

 

The first thing first, how is that being recorded? Oftentimes, maybe you think about leases. Commercial leases tend to be these long PDF documents. That's not easily usable in terms of doing some analysis. There are companies that are springing up in this space, like in the college of textiles program a couple of years back, together with some companies in that space. They will help you to make that into a digital record. Take some steps to have all of the information that you already own be in a format that's usable to do analysis.

 

The next thing is sharing of data. In a large company, right there may be silos, or they may be a lot of organic acquisitions. How do we make sure that various teams in the firm are willing and able to share? It depends on the setup. My commissions are what is important. Why should I share this with somebody else? That becomes a little bit of a problem. Creating culture was understanding that sharing the data creates a better potential for all of us together. I think that's also useful.

 

The next step is, what do you want to do? Do you want to build out your own team, or do you want to engage outsiders to do that? A lot of times, what we're seeing is that if people might come and they might ask, “We want to build out some analysis of this. If I use an external company, what do they need?” The company would say, “We need to work on the data, which means you need the share rails.” There's a lot of apprehension about that. I think that doesn't need to be. You can contract, and a lot of companies are not interested in stealing your data but help you to do that. There is a little bit more openness on that front.

 

If, to the extent that if that's insurmountable, you want to go down this route of building it out yourself and hire your own team. You can do that. The next thing is, who do you hire? How do you figure that out? It's helpful a little bit. We're going to tell you a little bit about the various things you might want to do. You can go a little bit in-depth on that.

 

The last note is people will say, “I want to get into data science. One of the common pitfalls is? I'm going to go hire a PhD researcher in data science.” That's maybe not the right guy because he's going to want to build new algorithms and figure out new intellectual things, new models. What you want is somebody who understands that, interested in applying it, sees your take, and makes an impact in the actual world.

 

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Creating the World's First Co-Living Start-Up: https://www.youtube.com/watch?v=K2MQNZnfvOA
Innovative Housing that can change the world! https://www.youtube.com/watch?v=K92fRT04Qjo
Building an Online E-Commerce Furniture Empire: https://www.youtube.com/watch?v=WhznBbYMhhg

 

About Zain Jaffer:
Zain Jaffer is an accomplished executive, investor, and entrepreneur. He started his first company at the age of 14 and later moved to the US as an immigrant to found Vungle, after securing $25M from tech giants including Google & AOL in 2011. Vungle recently sold for $780m.  
 
His achievements have garnered international recognition and acclaim; he is the recipient of prestigious awards such as “Forbes 30 Under 30,” “Inc. Magazine’s 35 Under 35,” and the “SF Business Times Tech & Innovation Award.” He is regularly featured in major business & tech publications such as The Wall Street Journal, VentureBeat, and TechCrunch.

 

 

Important Links

• PropertyQuants
• https://Bit.ly/2SWhYW5
• https://Apple.co/2Izoznu
• https://Spoti.fi/2STWDwq
• https://Bit.ly/2H7s6c0
• https://Twitter.com/zainjaffer
• https://ZainJaffer.com/
• https://Zain-Ventures.com/
• https://www.LinkedIn.com/in/zainjaffer/
• Building An Online Luxury Furniture Store - Ella Jade - YouTube
• Innovative Housing That Could Change the World - Boxabl - YouTube
• Creating The First Co-Living Startup Bedly - Marty Greenberg - YouTube