“In the mortgage and property sectors, rapidly improving technology offers a once-in-a-generation opportunity to make money.”

The Wealth Report’s section on AI shows the existing spotlight is on improving productivity in knowledge-centric roles – for example, streamlining mortgage assessments and valuations, and identifying sites for development.

Until now successful developers relied heavily on who they knew, which in turn dictated how they found the best sites and accessed the finance to develop them.

Success in this industry has always been about insider knowledge. That’s why it’s so exciting that we are at the beginning of a revolution in AI – one that will enable us to make sense of quantities of information that would have seemed impossible only a few years ago, McGuinness, says.

The natural language processing (NLP) technology that underpins AI enables people or companies to extract value from huge amounts of unstructured text.

AI automates that process on an enormous scale. These models can put words into context and draw inferences in a manner that the world is only just beginning to understand.

Knight Frank turned (NLP) loose on planning databases and it immediately became apparent there were troves of information in the thickets of commentary added by planners that would be impossible to compile manually.

It was immediately clear NLP could be used most profitably to find sites, particularly when paired with other techniques, McGuinness says.

Over a coffee, AI now allows him to take a client on a walk through prime central London and point out every available development site and explain their positioning relative to lifestyle elements such as the highest performing schools, fine dining establishments and private members’ clubs. He could also point out how many wealthy individuals live nearby, their property preferences, plus how much they have to spend.

He says these technologies get more interesting when they are applied to more complex problems, particularly during periods of elevated borrowing costs.

The financial viability of retrofitting commercial buildings varies depending on underlying values or rents.

By feeding its models with the relevant data, Knight Frank can produce accurate contour lines over a vast area that display exactly where investing is most likely to generate the best returns.

“Developers come to us for highly localised answers as to where it is feasible to start digging, but at the touch of a button we can apply our criteria to the entire country, at times providing hundreds of suitable sites,” McGuinness says. 

Data is rapidly proliferating, and how real estate investors manage so much of it is now among their biggest challenges.

He says AI will help capture “lightning in a bottle” opportunities, because only its computational power can fully resolve and scale the myriad physical, socioeconomic and environmental conditions underpinning success. 

“Returns will increasingly correlate with the quality of the data and interpretation they have access to. Success is still about “insider knowledge”; but that term no longer means what it used to, he says.