Real estate managers seek edge with better data, tech experts

The new hot jobs in real estate are data scientists and technology experts as managers try to adapt to a new way of doing business.

Real estate managers need this influx of talent to help reduce costs and boost returns by using internal and external data, investment performance attribution and predictive analytical algorithms to better determine which properties to buy and sell and improve cash flow.

Among the most recent hires, Blackstone Group LP in August hired Sean Muellers, a former technology consultant, as a managing director with its real estate group to assist the firm with real estate technology across its asset classes. Four months ago, LaSalle Investment Management named Simone Caschili, a trained environmental engineer and data scientist, as its new vice president and data strategist to drive the firm's data strategy programs.

"The use case for real estate is changing and it's driven by technology," said Mr. Muellers, who added that his role is not an investment position. Instead, he is helping the investment team analyze and anticipate the impact of technology and data analysis on their portfolios, he said, speaking Oct. 4 on a panel at the Pension Real Estate Association's 28th Annual Institutional Investor Conference in Boston.

He is assisted by data scientist Matthew Katz, vice president and head of data science, who joined Blackstone three years ago to head a team of data scientists to help analyze its portfolios using advanced data analytics and statistical modeling.

"There are a lot of entrenched companies in real estate" that are stalling innovation because, for example, they do not have open architecture, which would help them gain "proprietary insights from their data," he said.

The push for data talent stems from the fact that real estate managers are beginning to realize the health of their bottom lines depends on how well they use the data pouring from their properties.

Real estate managers are just starting to talk about adding data experts, said Robert Kohn, New York-based partner at placement agent Park Madison Partners. "I believe we will see much more of this in the future."

Some managers are not only hiring new data scientists and technology experts but insisting that current employees become more tech savvy.

LaSalle Investment Management, for example, is changing its organization both culturally and structurally to position the firm as "digital goals and initiatives continue to become increasingly central to LaSalle's strategy and everyone's day-to-day activities," said a recent staff memo from Chicago-based Jeff Jacobson, global CEO; Cindy Parker, director of IT; and Jacques Gordon, global head of research and strategy, obtained by Pensions & Investments.

"New skill sets and roles will be required for LaSalle to be competitive in the new digital age, particularly in the areas of data science and data management," the memo stated. "More broadly, our organization as a whole needs to increase its 'tech fluency.'"

LaSalle has formed a cross-functional data and technology committee in each region globally to help identify and prioritize its key digital initiatives and to continuously monitor local market developments, the memo stated.

The firm also is setting up a data governance model and is involved in a number of global data standardization initiatives to form a foundation for capturing data. LaSalle then plans to layer on business intelligence and data analytics to produce insights and improve investment performance.

Future success

Industry insiders predict that in the not-too-distant future, a firm's economic success will no longer be based on its assets under management but how well it uses data to supercharge its earnings.

Technology can help real estate companies boost their overall value exponentially above the value of its assets, said William O'Donnell, managing partner at Prologis Ventures, San Francisco, the 2-year-old corporate venture capital arm of industrial real estate investment trust Prologis Inc.

"It's not IT, it's a core strategy," Mr. O'Donnell said.

The right experts can also help real estate managers figure out how to capture the data from their properties and put it to use to make better investment decisions.

Many respondents to a recent Deloitte LLC survey cited data analytics as a primary tool for making their investment decisions. Some 39% said they plan to use predictive analysis, while 38% intend to use business intelligence in making real estate investment decisions. Fifty-six percent said they make 41% to 70% of their real estate investment decisions using data analytics.

Deloitte survey respondents included C-suite executives who invest in real estate at global private equity firms, hedge funds, mutual funds, asset management arms of banks and insurance companies, sovereign wealth funds, pension funds and real estate investment trusts.

"Real estate firms are now recognizing the enormous value of leveraging their data to do everything from maximizing their lease rates to enhancing the efficiency and experience of their buildings," said Casey Berman, Washington-based managing director of Camber Creek, a venture capital firm focusing on real estate technology companies. "Companies like (Camber portfolio companies) VTS, CompStak and Measurabl use individual customer data combined with market data to benchmark and provide valuable insights."

Real estate managers are using new types of data and technology to unlock the value in their real estate portfolios. They include predictive analytics, social media, robotics, blockchain and gamification. These new data sources and advanced technologies can be used to analyze the benefits of offering tenants flexible leases.

Managers can use cognitive technologies such as machine learning to generate insights from large data sets derived from new sources of information. Some managers are now collecting data on building temperatures, humidity, air quality, vibration and sound to help reduce energy consumption, Deloitte's report noted.

But Mr. Muellers said managers "have the infrastructure internally to be able to ingest and use the data. … Buildings spit out data that is unknown by the building owner."

Prologis' Mr. O'Donnell said his firm uses technology to run its business more efficiently and create value from the data embedded in its real estate portfolio. Data can be used to allow the firm to be "more scientific in how we price," he said.

For example, Prologis executives use information on transportation availability and utilization and employment levels to let the team know when it can push rents higher, Mr. O'Donnell said. "These are things that didn't exist before," he added.

Property data

Data from commercial properties can provide property owners with information on energy consumption and building occupants, said Clelia Warburg Peters, New York-based co-founding partner of Metaprop NYC, a consultant and seed-stage venture capital firm focused on real estate technology companies.

"You can use the data to give you insights into what people are doing in the building, who is in the building and what they are doing there. ... Artificial intelligence will provide insights (to base investment decisions) but we are at the very early stages of collating that data. Technology will also reduce your cost of operations," said Ms. Warburg Peters, who spoke on a panel at the PREA conference.

But Brad Greiwe, Los Angeles-based co-founder and partner at Fifth Wall, a venture capital firm that invests in real estate technology companies, cautioned investors and managers that "there's no silver bullet." Real estate managers are just learning what they have, and "there's no venture capital-backed company that will solve your data problems," he said, speaking on a panel at the PREA conference.

Real estate firms have far to go before they can fully use the data embedded in their portfolios, he said.

"It does not matter how much data you have. You can't do anything with it without organizing it. Artificial intelligence won't help without organizing your data better. Once the data is clean, the implications are incredible," Mr. Greiwe said.