How AI is changing the real estate world

The investor was satisfied. Four brokers had been given the task of finding real estate in the US city of Denver that would meet his expectations. The brief was to find buildings similar to those the client had previously acquired. In the end, there was a clear winner with exactly the right price, location and facilities. We don’t know whether the three brokers whose proposals were rejected were disappointed, but it’s a fact that the winner showed no human emotion at his success, because he wasn’t human – the winning proposal was selected by artificial intelligence.

This example from the US shows that artificial intelligence (AI) has the potential to transform the real estate industry. “This technology will have an impact on every single activity in the real estate industry – and on every phase in the life cycle of buildings,” says Heiko Gsell, Professor of Business Information Technology and Director of the Digitalisation and Real Estate Management programme at the EBZ Business School in Bochum, Germany. 

AI reduces energy consumption by using past consumption data to make reliable predictions about the future. It improves security because it can detect noises and camera images faster than security personnel, analyse the information, and alert the police or fire brigade in the event of danger. It can identify patterns in the operation of heating, ventilation and air conditioning systems and thus accurately forecast dates for repair and maintenance work. It also speeds up transactions by matching offers from previous acquisitions with property descriptions and then crafting customised purchase offers – as in the case of the Denver example cited above. “Processes become significantly easier and more efficient,” says Carsten Kreutze, Managing Director of Bonn-based software company Recogizer, which uses AI to reduce energy consumption in buildings. “Ongoing development will at some point make it possible to collate all the data relevant for real estate transactions and managing properties at the touch of a button,” he adds. What condition is the property in? When will the radiators need replacing? How many tenants are there, and when do their leases expire? Can I push through rent increases?

But none of this will work without good data quality and availability, notes Patrick Penn, CEO of Docunite, the provider of an AI-based document management system: “For a sale, for instance, you need not only leases and land register extracts but also the energy performance certificate or more exotic documents, such as information on contamination of the site by explosive ordnance.” In many companies, these documents are typically spread over a range of departments. “The leases might be held by the property manager, and the building services plans by the asset management team,” says Penn. Sometimes a search will reveal multiple versions of the same document with different dates: “It can take six months for everything to be sent to the buyer in some cases.”

The real estate industry is still too analogue in many respects, notes Penn. “This is an area where companies need to do their homework.” Which means digitalising data and merging both their own internal data and publicly available data via interfaces. “This will generate opportunities that can deliver a huge competitive advantage,” says Penn. That is particularly true since the inception of generative AI, like Chat GPT. “Now you can chat with your documents,” Penn adds.

Many companies now have a kind of control room that brings together their air conditioning, ventilation and heating systems, says Kreutze: “But it’s only with AI that you can really address the big consumers.” This is because AI can take into account a host of factors that influence consumption: What will the weather be like over the next few days? Is the space let or vacant? Does a room have unusually large windows? What is the building’s function – is it used as a gym, an office or for retail? At what time would you normally expect the biggest concentration of people?

AI not only recognises correlations, it can also use them to come up with answers to complex questions, says Carsten Kreutze: “A question of this sort might be something like why is the energy consumption in our top five properties higher than in the others.” Furthermore, artificial intelligence can make specific suggestions for improvement, advising on whether flow temperatures should be optimised, for example, fan speeds adjusted or system operating hours made dynamic. “With measures like these, we’re already achieving average savings of 28 percent in energy consumption in commercial real estate,” says Kreutze.

A question that currently looms large in the real estate industry is whether use of AI will cost jobs. “In facility management, finding well-qualified employees is a major challenge. Facility staff also currently have to perform a large number of tasks that are often technically very demanding – frequently all at the same time,” notes Kreutze.