Canada’s commercial real estate firms lag well behind global peers in adopting, and adapting, data science into their investment strategies, according to Altus Group's The State of Data Science in Commercial Real Estate Investing report.

“Ninety-three per cent of Canadian firms use analytics, but only 35 per cent use data science,” Heidi Learner, head of innovation at Altus Lab, told RENX. “If we look at Canada as a proportion of samples relying on data science, albeit differently, the Canadian response rate is the lowest.”

Data science unearths meticulous information  traditional statistics, which Learner described as comparatively static, aren’t capable of interpreting. 

For example, the latter will reveal a neighbourhood’s vacancy rates going back five years, but data science could instead shed light on who lived in that neighbourhood during the corresponding period as well as how much money those residents earned, their age profiles and who among them have children.

“All of these variables potentially interact. Use of data science allows us to extract the variables that are the most important drivers of rent to come up with fair value assessments of what rents should be going forward,” Learner said.

“The data science approaches are more robust than some of the prior approaches that rely on purely statistics relationships.”

Data science adoption a matter of time

Learner added Canadian firms are more likely slow adopters, rather than Luddites.

After all, Canada’s real estate market has long had an unflattering reputation for trailing its southern neighbour when it comes to innovation and technology, but she noted demand for data science is hearty in the Great White North, even if roughly only a third of the country’s commercial real estate industry presently uses it.

For perspective: 56 per cent of U.S.-based commercial real estate firms employ data science in their operations, while adoption percentages range from the 40s to 50s in Europe. Among Asia Pacific countries — which include Oceania — it’s about 50 per cent.

Learner remains optimistic Canadian firms will follow suit.

“Canadian firms are relying less on data science techniques than their global peers and it suggests there’s further adoption to come as investors become more comfortable with these techniques and realize that, if they don’t have the capability to develop these technologies in-house, they can hire third-party providers,” she said.

The reason it could become necessary is the data can be highly intricate, unlike the preponderant wholly stats-based method that relies on static information, which can result in missed opportunities.

Canada’s largest CRE firms aware of deficit

Colliers is one of the largest commercial real estate companies in Canada and, as such, releases a host of reports throughout the year for the office, retail, industrial and multiresidential sectors.

According to Adam Jacobs, Colliers’ senior director of research in Canada, the firm uses data science for mapping and geographical information system purposes, which has helped it ascertain, for clients, things like whether their commercial enterprises should be set up near particular universities or in downtown cores instead.

“One other thing that comes to mind is we have models that look at traffic — 'How crowded will (a certain) intersection be on Monday morning?' ” he said.

“We have historical data, then we assume there will be ‘this much’ population growth and new cars on the road, so we’re also modelling on that side.”

Colliers has kept abreast of the ways in which data science is slated to evolve, which could entail estimating building sale prices when, or if, they hit the market. 

Such determinants are, however, convoluted in the commercial sphere at present — one painstakingly unclear factor is employees’ post-COVID-19 return to offices. 

Jacobs said forecasting is the next, maybe even final, frontier for data science.

However, that could require accurate population projections that themselves are conditional upon myriad variables, including unemployment rates across various industries (which is no small task in a city like Toronto that has the most diversified economy in the country).

Another factor would be hybrid work configurations and whether employees tend to be in their offices on certain days of the week over others. If so, which ones and why? It’s too early today for such data sets, but Jacobs believes they’re coming.

“The predictive side is the final frontier and I don’t know we necessarily, as a company, are there yet,” Jacobs said, adding, “that’s probably coming for the industry in the next five years.

"These are things we didn’t consider five years ago when it was just based on real estate fundamentals.”

Urgency bucked by simplicity

Unlike the United States, the world’s third-largest country by population which has an abundance of sub-markets, Canada’s commercial real estate market has been relatively easy to predict and, COVID-19 notwithstanding, the industry hasn’t had to contend with many hurdles.

A combination of rising interest rates and contracting credit markets globally, as well as, on the domestic front, inert post-pandemic recovery, are now creating uncertainty for the next few years.

However, that might expedite urgency for more intricate data to allow firms to edge out their competition. 

“I think it’s just a little harder at the ground level of brokerages, where it’s a big investment and they only operate in, say, five markets, so they don’t see the reward in the same way as in the U.S., where it’s maybe 12 times the size and there’s more of an immediate benefit because there are more competitors,” Jacobs said. 

“The impetus is there with us and it’s there with our competitors.”