What Next?
AI, machine learning,
and IOT disruption

AI and machine learning are heralded as the future of CRE – but what do these terms really mean for our sector? Incendium’s data expert Shaun Guyver assesses the latest tech and how it is being used.

The clamour around Artificial Intelligence (AI) has been deafening as tech companies around the world look to automate roles that have traditionally been undertaken by humans. In a recent white paper, the RICS has already predicted that up to 75% of surveying roles could be undertaken by AI on an automated learning basis. But this isn’t just an issue for the future, AI is already powering multiple systems used by supply chains.


One of the main ways AI and machine learning are used in real estate is with big data. Historically, CRE teams have operated using traditional data sets that could be easily understood and acted upon, using a mix of intuition and job experience. Now, enormous data sets that are being refreshed on a daily and even hourly rate – like desk booking, attendance, and utilisation of space – have become commonplace, requiring a whole new approach.

CRE teams must invest in IOT hardware and integration platforms to truly understand day-to-day activity across their portfolios.

One interesting example is with contextual, or geo-spatial data. In Boston, Massachusetts, landlord investors have been able to plan ahead of the property market by analysing the proximity of properties to certain commercial outlets. This might entail, for example, being close to one or two Starbucks correlating with high property prices, while being close to four or more coffee outlets correlating with lower property prices. Meanwhile, a higher number of negative Yelp reviews correlated with high property prices, while a lower number of average or good reviews correlated with lower property prices.

Insights like these are difficult to achieve using traditional ‘human’ intuition, due to their complexity. As a result, a well-thought out investment in machine learning and AI technologies has allowed CRE teams to tap into unexpected insights they can use to better plan their future location strategy and cost profile. Ultimately, this is saving them money by giving them data-led foresight, and not, therefore, having to play catch up with reality.

The Internet of Things

The IOT is, in essence, a series of smart devices that talk to one another and feed back into a wider data loop that allow the overseer to take an overview of co-joined performance. In theory, IOT tech should be perfect for the workplace as it allows CRE teams to have smart insights into a series of interlinked workplace data sets.

Over the past five years, key IOT devices such as sensors have dramatically fallen in price, while advances in IOT integration software have presented new opportunities to connect existing devices across real estate portfolios.

In the mid-term, advanced IOT platforms can connect sensors, access control systems, CCTV cameras, lifts, and even coffee machines to analyse how occupants use buildings in real-time. Instead of depending on unreliable headcount projections and retrospective spend patterns, CRE teams can now shape their workplace, real estate, and capital project work streams with greater precision and confidence using an IOT-led approach.

Longer-term, there is technology on the horizon that can actually analyse individual and team behaviour. Researchers at MIT have developed hardware that reads body language for one or more individuals and is able to determine whether or not the working culture is a positive one. While this might sound intrusive, anonymised readings of team behaviours in a controlled environment can let CRE teams see how their workplace projects impact the people using them, accelerating the trial-and-error approach of curated workplace design.

What is next?

The nascent PropTech scene is growing fast but investment in such technology is very small (0.007% of total CRE market) compared to other sectors such as FinTech (0.4%) or MedTech (0.8%). It is a little harsh to generalise, but adoption of technology within CRE is still very much at the “theory” stage rather than being actionable and deliverable. Inevitably, this will have to accelerate to enable CRE teams to better exploit opportunities currently hidden from them, but this will require the introduction of new skills to the sector around programming and data-analysis.

To put themselves in an optimal position, CRE teams must invest in IOT hardware and integration platforms to truly understand day-to-day activity across their portfolios, ensure that they are equipped with AI and machine learning enabled platforms to process the data, and invest in recruitment outside of conventional real estate hiring pools to make sense of the data and provide leadership with consistent, actionable insights.