How valuable is big data becoming in Commercial Real Estate?
Big data is changing established ways of working within real estate from the inside out.
Transaction data used in analysis is now days, rather than months old. Numerous site visits are no longer the first part of the buying or leasing process; long before investors or companies set foot in the building, they’ll have reviewed a shortlist that uses data to match their requirements to its plus and minus points.
As new technology gathers bigger amounts of data more effectively than ever before – and it becomes easier and more cost effective to store, organize and analyse the figures — it’s helping both investors and companies looking for space to drill down into risks and opportunities in much greater depth. In doing so, it’s facilitating informed real estate decisions, whether that’s gaining first-mover advantage or not missing out on a fast-moving development.
“Data has always been a central part of the real estate decision-making process yet traditionally it wasn’t collected digitally,” says Isaac Pernas, CIO of JLL for Southern Europe. “Now, mathematical models applied to big data are allowing the commercial real estate industry to assess and act on information in ways that were unthinkable in the past in terms of volume, timing and accuracy – not to mention improving market transparency and making it easier to predict future behaviors.”
Making sense of the data
Yet huge amounts of raw data are just the starting point. In fact, 90 percent of the world’s data has been created in the last two years.
“Mobile, the Internet of Things and other communication technologies have brought all business sectors to a new data reality where we have more data than we could process, or we do not process the correct parts,” says Pernas.
Firstly, it’s about getting good quality data, he explains. In Spain, for example, JLL is using data gathered from phones on Vodafone’s network to monitor changes in how people interact with the built environment and predict trends in its big cities.
Then, it requires the right algorithms and visualizations to spot the patterns and draw out the insights. Companies like Zoopla or Zillow that have disrupted the residential real estate markets are regularly refining their websites to provide clear information on current prices, buying and selling trends, and neighborhood characteristics such as traffic level and demographics.
In commercial real estate, today’s models are drawing on a wider range of factors than even a few years ago, many at a hyperlocal rather than a city level from local air quality or popular commuting routes along with more traditional inputs such as the asking and closing prices of buildings.
Retailers, for example, want to know where their competitors are located, what’s the economic and social profile of people visiting a certain area and how people’s movements on the streets are linked with their economic transactions in those spaces.
“Today’s models allow for greater precision and customization in line with different requirements,” says Pernas. “We can understand what has happened in a specific location in the past 10 years even if we have never been there and make decisions about any location on which we have data with high accuracy. This was unthinkable some years ago when consultants had to travel to any location to study it.”
Generating the right insights also requires specialist skills. “This is why we are seeing a real boom in demand for big data experts not just in real estate but across the wider business world,” says Pernas. He points to a report from the EAE Business School indicating that big data experts are the most in-demand professionals, accounting for one in every ten new openings. Back in 2017, IBM also predicted that the number of Data Science and Analytics job listings in the U.S. is projected to grow by nearly 364,000 listings to around 2.7 million by 2020.
Data in demand
Demand for big data analysis is only set to increase as more powerful technology hits the market – and becomes more affordable. The technical challenges in terms of processing and storing all the new data coming in from potentially billions of sensors, is just part of the picture.
There are also big social and ethical implications. “The new way to develop big data algorithms is based on learning from data patterns,” says Pernas. “If the data that feeds these models is biased, the decisions that the model makes will be biased too.”
Growing privacy concerns are putting the onus on analysts to collect and use data responsibly as it becomes an increasingly embedded part of real estate business processes – and delivers a significant competitive advantage.
“Big data is becoming the norm in all companies, the same way that they have an IT or an administration department,” says Pernas. “Companies which are not able to integrate it will simply cease to exist in one or two economic cycles, losing the possibility to reposition themselves and monetize their data, either by improving their services, or by selling knowledge.”
With technology moving at a rapid rate and big data set to be a $103 billion industry by 2027, according to market research firm Wikibon, attracting high-quality data scientists and developing fresh algorithms that can provide top quality insights remains a priority for real estate.
“If you ask the right questions and use the correct tools to extract the data with the help of a skilled team, you will surely get new answers, advise clients with more accuracy or make decisions with more background information than ever before,” concludes Pernas.
