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Real estate: Are you ready for AI?

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Pick any successful real estate developer from a previous generation and try to put your finger on what made them successful.

Whoever you’re thinking about, their success was almost certainly heavily skewed towards who they knew, which in turn dictated how they found the best sites and accessed the finance to build them out. 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 artificial
intelligence (AI) – one that will enable us to make sense of quantities of information that would have seemed impossible only a few years ago.

Indeed, our expanding AI-driven data processing capabilities will result in the largest improvement to productivity since the arrival of the internet, saving the average worker more than 100 hours a year, according to Google.

In the UK alone, the company estimates that AI-powered innovation is expected to create more than £400 billion in economic value by 2030. GDP is expected to be more than 10% higher over the same period than it otherwise would have been, according to PwC.

Early adopters

Those investors who engage with AI early stand to command the largest slice of those gains. In the property sector in particular, rapidly improving technology offers a once-in-a-generation opportunity to make money.

But first, some terminology. The explosion in popularity of large language models such as Open AI’s ChatGPT can make it appear as though AI only arrived in 2023, but the natural language processing (NLP) technology that underpins these chatbots has been with us for several years.

The beauty of ChatGPT was its simple interface that enabled hundreds of millions of people to get their first taste of what AI might offer. NLP enables us to extract value from huge amounts of unstructured text.

Some readers of The Wealth Report might have experienced the frustration of manually moving data from a PDF into a spreadsheet so it can be manipulated and mined for insights. 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.


“Investors who engage with AI early stand to command the largest slice of those gains. In the property sector, rapidly improving technology offers a once-in-a-generation opportunity to make money.”


 

Vision of the future

We began experimenting with NLP four years ago by turning it loose on planning databases. It immediately became apparent that there were troves of information in the thickets of commentary added by planners that would be impossible to compile manually.

We were able to track how businesses across the country were adapting to the Covid-19 pandemic by changing the use of properties, for example.

It was immediately clear that NLP could be used most profitably to find sites, particularly when paired with other techniques. The Knight Frank Research Analytics Team has deep
expertise across data manipulation, analysis and visualisation.

We view AI as a chain of processes that includes language processing but also spans machine vision, classification and learning techniques that enable us to address data gaps and make cleareyed predictions.

If you could take a walk with me through prime central London right now, for example, I could show you 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.

I could also point out how many wealthy individuals live nearby, their property preferences, plus how much they have to spend. With AI we can do it all over coffee at Knight Frank’s HQ.

Local intelligence

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 to achieve higher BREEAM or EPC ratings varies depending on underlying values or rents.

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

Seniors housing is another sector for which development viability turns on dozens of factors.

People over 60 tend to purchase property only within three miles of where they live, so any feasible development site must have the depth of market nearby.

Our models account for the 20% equity release that most movers in that demographic seek to lock in.

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.

One of the many examples of scalability was our study of car park land for officials at the Department for Levelling Up, Housing and Communities who wanted to find out whether any public sector-owned car parks could be put to more valuable uses, such as retail or
residential.

We studied more than 30,000 car parks and found that, while they were well served by public transport, almost 70% didn’t appear to support a retail centre.

Considering land values and redevelopment potential, we identified enough land to support more than 100,000 new homes, all while maintaining car parking services that are vital in supporting our high streets.

New Heights

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

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 NLP, computer vision and machine learning techniques will all play increasingly prominent roles, but they are still best combined with the relationship-building and foresight that have been utilised by successful developers for decades.


“We view AI as a chain of processes that includes language processing but also spans machine vision, classification and learning techniques that enable us to address data gaps and make clear-eyed predictions”


The abundance of information fuels the potential for a generation of property investors to achieve new heights – if they can bring fresh perspectives and discern the signals within the noise.

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

About the Author


Ian McGuinness is head of Research Analytics at Knight Frank.

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