Preparing for the AI revolution in real estate

Thousands of Britons faced a race against the clock to complete their home purchases during the Coronavirus pandemic.

Not only did lockdown present numerous challenges, it coincided with the the looming deadline for property tax relief to expire.

The measure offered up to £15,000 off the total cost of purchasing a property. That sparked a surge in transactions that overwhelmed estate agents, surveyors, and lawyers.

Seeing the strain on the system, the UK government announced it was extending the stamp-duty ‘holiday’ by a few months.

The decision relieved the immediate pressure. However, it also underscored the pressing need to digitise cumbersome process of property transactions.

Indeed, ‘proptech’ can streamline processes for buyers, sellers, tenants, and landlords alike.

Compared to other sectors, such as banking or insurance, real estate has been slower to adopt digital technologies. 

This lag means many processes still rely heavily on manual methods, paperwork, and face-to-face interaction. This can be time-consuming and prone to errors.

Why has real estate been slower to adopt digital technology?

Real-estate data, especially – such as geographical maps and traffic patterns – pose unique challenges.

Financial transactions commonly contain structured data such as numbers in spreadsheets.

However, property data often comes in unstructured or semi-structured formats. This makes it difficult to capture, organise, and analyse. 

Consider blueprints, the intricate technical drawings that depict the layout, dimensions and specifications of buildings.

Converting physical blueprints into digital formats that retain their accuracy and detail can be a complex and labour-intensive process. Yet this data is essential for effective decision-making, risk assessment, and market analysis. 

Artificial intelligence (AI) is likely to be a game-changer for property.

It is also the driving force behind the new FutureFinance.AI research group that I lead at Warwick Business School.

We aim to use generative AI (gen AI) as a tool to improve the efficiency of real estate operations. 

How generative AI could revolutionise data management

Gen AI is capable of performing a wide range of tasks. It is also able to adapt to a range of contexts, unlike ‘narrow’ AI systems that focus on specific tasks.

With gen AI, the real estate industry can transcend the limitations of manual data management and interpretation, ushering in an era of unprecedented efficiency.  

Traditionally, data management has been a labour-intensive and time-consuming process. This involved gathering, organising, and analysing vast amounts of data from disparate sources. 

However, gen AI can streamline this process by ingesting and processing data automatically. This means real estate professionals can quickly analyse complex datasets and extract actionable insights, saving time and effort. 

What will FutureFinance.AI accomplish? 

We plan to build AI tools for integrating and processing diverse data sources – including drawings, blueprints, and surveys.  

Rather than simply digitising images or files, we aim to imbue real-estate data with deeper meaning and semantics. 

One key feature of the upcoming tools is their ability to represent data in formats that are easy to work with.

Our platform will provide insights in user-friendly formats, rather than raw data or complex analyses. 

This includes visualisations, summaries and interactive dashboards.  

The process involves extracting and interpreting the underlying context, relationships, and insights embedded within the data.

By capturing the essence of real estate information, gen AI systems can provide more valuable insights for stakeholders. 

We also plan to develop an open platform. This will make our tools accessible to everyone in the real estate industry.  

This liberalisation of AI has the potential to foster innovation and level the playing field.

How will AI transform proptech?

Real estate has historically been dominated by established players. However, advancements in technology can foster greater competition and innovation.   

Ultimately, it can drive collective growth in a sector crucial to the UK economy. Commercial property alone contributes £137 billion annually, about 7 per cent percent of the total gross value added. 

FutureFinance.AI’s platform is still under development and operates on the principle of “AI as a service”. This means we will provide AI capabilities to users on-demand. 

This allows real estate companies to access advanced AI tools and technologies without the need for significant upfront investment. 

That will improve access to cutting-edge technology and empower users to enhance their operations and decision-making processes.

There are several key challenges we must overcome to build our platform.

For example, we need to obtain sufficient training data to teach our machine learning models to interpret and embed real estate blueprints effectively. 

Training AI datasets on blueprints

This process requires curated datasets containing a diverse range of blueprints and associated metadata.

Once trained, generative AI models can automatically analyse the data, extract relevant information, and generate descriptive summaries or insights. 

The ultimate goal of the research is to automate design processes within the real estate industry.

By leveraging AI, stakeholders can streamline tasks traditionally performed manually, such as modifying designs, interpreting blueprints, and generating design alternatives. 

This automation promises to accelerate project timelines, reduce costs, and enhance overall efficiency. 

Job displacement is not a significant risk in the real estate sector. The focus is on enhancing efficiency rather than replacing human workers. 

While some job losses may occur, AI has the potential to lower barriers to entry for new market participants.

This, in turn, can lead to increased job opportunities as new entrants bring fresh perspectives and solutions to the market. 

Technology trends in the property sector

In addition to AI, blockchain technology is a key focus area for FutureFinance.AI.

One crucial aspect of property transactions is ownership documentation, and blockchain offers a secure and transparent way to record property rights.  

By leveraging blockchain for property documentation, the industry can streamline processes, reduce fraud and enhance trust among stakeholders. 

Further research and development are needed to unlock the full potential of technologies like blockchain and AI.

However, it is evident that these technologies have the potential to revolutionise the landscape in the coming years. This could fundamentally alter how we buy, sell, and rent properties.

Further reading

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Algorithmic inertia: The flaw that made Moody's blind to the financial crash 

What does DeFi need to go mainstream?

How AI will accelerative financial democratisation

Ram Gopal is Professor of Information Systems Management and Director of the Gillmore Centre for Financial Technology. He lectures on Digital Finance, Blockchain & Cryptocurrencies on the MSc Management of Information Systems & Digital Innovation and Text Analysis on MSc Business Analytics.

Learn more about AI and digital innovation Business Impacts of Artificial Intelligence on a four-day Executive Education course at WBS London at The Shard.

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