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Real estate industry needs to retool for the technological revolution ahead

The future of work is uncertain. From blue-collar to white-collar to all the jobs in between, technological advances are likely to impact the nature and scope of careers.

The real estate sector, traditionally slow in adopting technology, is even starting to feel the effects of disruption.

Proptech, a term to describe advanced technologies in real estate, is beginning to emerge, with start-ups applying machine learning and artificial intelligence techniques to previously labour-intensive tasks to achieve higher productivity.

The mass deployment of such tools in the future is likely to generate greater profits. However, such advancement may come at a human cost.

A recent survey of real estate executives by the Altus Group, a Canadian commercial real estate services and software company, revealed that the real estate industry is “sitting at the cusp of realizing meaningful returns from technology investments.” The survey collected insights from 400 C-Suite executives in the commercial real estate (CRE) industry.

Almost 75 per cent of the executives surveyed believed that increased automation was likely to eliminate jobs. At the same time, 71 per cent thought that automation would introduce new types of jobs in the real estate industry, or “shift jobs towards higher value-added tasks.”

Realizing the potential of machine learning and AI, the Journal of Portfolio Management recently dedicated an entire issue to “the changes being brought to real estate investment by new technology.” While technology is affecting all aspects of the industry, from construction to financing to investments and beyond, the application of predictive analytics to financial outcomes have attracted the greatest attention.

Writing in the same journal, Chad Cowden and co-authors deployed advanced tools to predict the default rates for commercial real estate loans. They compared the predictive accuracy of the traditional statistical tools, such as regression models, with those relying on machine learning paradigms.

The findings of their comparative analysis are in line with what others have found: Machine learning tools, such as the “support vector machine technique for predicting defaults on commercial property loans significantly outperforms other methods.” Furthermore, such tools perform well even with imperfect data.

New predictive analytics algorithms are already being deployed for the valuation of real estate properties. Automated valuation (AV) models forecast valuations based on the structural characteristics and location of a property with little or no human intervention.

Such technical advancements are likely to create redundancies in the workforce.

One option to meet with this challenge for the real estate industry will be to explore ways to retrain and repurpose the existing workforce, which is uniquely advantaged because of its domain-specific knowledge and experience.

This is likely why Bridget Frey, chief technology officer (CTO) of Redfin, an innovative real estate brokerage based out of Seattle, is also not convinced that algorithms will completely replace human insights.

Speaking at in 2017, Frey observed the “algorithms work better when we leave a place for a human to be in the loop, and I think that’s where the direction needs to go.”

The Altus Group survey revealed that almost half of the real estate firms were spending two to three months in a year “managing and organizing data to drive decision-making.” This suggests the industry lacks ready access to data scientists who would help the industry reduce the time spent on managing and organizing data efficiently.

An earlier survey in 2015 reported 29 per cent of the industry leaders were of the view that the lack of internal expertise and capability was preventing their companies from collecting or utilizing data to drive decision-making. By 2020, a much lager proportion of 52 per cent highlighted a lack of internal expertise in data management and utilization.

The commercial real estate industry is poised for a significant change. The era of smart buildings is upon us. Using the latest tech, building managers can determine where users are in real-time to determine the intensity of space use over time.

Companies such as Innerspace use the unique digital signatures of ubiquitous smart devices to determine where people are within a building and how to optimize energy use and security and limit harmful emissions to improve sustainability and profitability. This will require a data-centric, analytics-oriented workforce, which currently does not exist.

The commercial real estate industry should make every effort to collaborate with different levels of government and institutes of higher learning to train the workforce needed for the data- and technology-centric real estate management industry of the future.

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