The property industry often feels like a zero-sum game. If one investor gets a deal, another loses out on it. If one asset manager finds a better way to research, the others are at a disadvantage. This mentality has led to a culture of secrecy when it comes to how property companies operate on a day-to-day basis. While protecting a competitive advantage can certainly be a good business strategy, the opaque nature of most property companies could be hampering innovation and slowing tech adoption.
This seems to be the case with data science tools. While other financial service industries are transforming around technology, the property industry, particularly the commercial property sector, has been slower to incorporate new techniques into their business models. To find out exactly how much data science was being used in commercial real estate investing a new report was commissioned by the Altus Group about the state of data science in commercial real estate.
The surveyors interviewed over 400 global decision makers and influencers of data decisions at commercial real estate investment companies worldwide. By asking what types of tools companies were using, what capabilities they were investing in, what challenges they were experiencing with establishing these new capabilities, and what they hoped to get out of their investment, the report was able to build a detailed view into the state of data science and advanced analytics in the commercial property industry.
The results showed that, despite the slow adoption, data science tools were starting to be used widely by the industry. Fifty five percent of the respondents claimed to have already been using data science or advanced analytics in some form. The top reason that companies were investing in data science and analytics was no surprise: to gain competitive advantages and differentiate from competitors.
Data science has a lot of overlap with traditional statistical analysis so the report separated the two. By doing this it found that the most common tools being deployed were still, by far, analytic by nature. The actual data science techniques, like machine learning, predictive modeling, and writing algorithms were in the bottom half of the total responses. Other, less involved techniques, like using statistical tools and identifying patterns, were much more prevalent.
While many companies might be using data science, the impact of these tools is still unclear. This didn’t seem to alter most companies’ perceptions of their capabilities. Seventy three percent of the respondents to the survey said that they thought that they were ahead of the cycle when it came to using data science, relative to others.
Some of this distorted perception might stem from the protectionary nature of the industry. “People want to keep things proprietary so that means there is not widespread knowledge of what one’s competitors are doing,” said Heidi Learner, Head of Innovation at Altus Group. She thinks that more industry collaboration could help the entire industry become sophisticated without requiring companies to sacrifice their secret sauce. “We would feel comfortable being able to discuss the models that we use and how we test them, even if the inputs are secretive,” Learner said.
There seems to be plenty of room for improvement for many firms to start adopting a data science approach to their property businesses. But doing so can be a daunting task. Data science is not a magic pill that can solve all of a company’s problems. Much like all scientific disciplines, data science is only able to validate or reject certain questions. “You have to be very specific about your hypothesis or else you end up just throwing too many darts and you get nothing back,” said Learner
For smaller companies without the ability to create an entirely new division, there are plenty of third parties that will help firms build out their analytical capabilities. According to Learner, a lot can be outsourced to these third parties but companies still need to have someone on the team that understands data science that works with them. “You might not need a data scientist but you do need a partner that can help explain the models,” she said. “Clarity is vital and so it is about showing the work and not just the final insights.”
Commercial property companies might not have incorporated tech as much as some other finance and investment sectors, but the pace of innovation certainly seems to be quickening. The slow pace of digital transformation might not all be the fault of the industry either. “There will always be the labor intensive process of managing a physical asset so not everything will be about data science,” Learner said. “But when it comes to allocating resources, data can really transform organizations.”
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To learn more about the state of data science in commercial real estate investing you can download a copy of the full report here.