If the history of the city shows anything, it is that the unintended consequences of new technology often overshadow the intended benefits. Take motorisation. The car offered a return to nature by freeing cities from the odorous piles of horse manure that clogged 19th century streets. Mission accomplished. But motorisation also led to urban sprawl, sedentary lifestyles and high carbon emissions.
Plastics, asbestos and high-rise public housing all promised to improve lives. Yet, the legacy of plastic is dangerous ‘forever chemicals’ that do not break down in the environment or the body. Asbestos, initially celebrated for its fire resistance, has caused severe health issues. High-rise public housing aimed to provide affordable urban living but instead often fostered crime, poverty and social isolation.
The critical need for AI adoption
AI, as Nino defines it, is a technology that allows computers and machines to think like humans. This broad definition encompasses everything from simple rule-based approaches to complex tasks like intelligent planning and problem-solving. Marcelo adds that AI involves methods taught to machines to perform tasks involving digital information such as text, numbers and even pixel understanding. With enough computational power, machines can be trained to perform specific tasks, creating a form of intelligence.
Nino emphasises that the potential benefits will be in automating processes and making better and quicker decisions. In this, the real estate industry lags behind the finance, healthcare, retail, manufacturing, telecommunications and automotive industries. The gap lies primarily in advanced data analytics, automation, customer experience enhancement and smart infrastructure, which is where opportunities exist for the real estate industry.
Practical applications of AI in real estate
AI’s potential in real estate will be transformative. Marcelo points out that a critical role that AI could play is addressing how the industry deals with imperfect and asymmetric information sets.
“If a company can train AI to understand the critical determinants of the business, such as time and pricing, it can significantly enhance the learning of the business,” he explains. “In effect, an AI system can serve as an additional employee who never sleeps, remembers everything and provides transparent advice on risks and opportunities.”
One other significant challenge in the industry is the lack of data standards. Real estate professionals often exchange data via documents, which is cumbersome and inefficient. AI can extract data from documents to develop smarter processes. This increased data quality and availability can unlock hidden potential in the industry.
Marcelo also mentions the use of satellite imagery to predict urban development. AI can analyse nighttime city lights to forecast growth areas, providing a new perspective on city development beyond traditional expert opinions, benefitting city planners, developers and investors.
A significant application is the creation of digital twins. This involves creating a virtual replica of real estate assets, allowing for real-time monitoring and management. “Imagine a control panel that receives a variety of data, documents and emails from everyone involved with the property,” Nino says. “The control panel automatically combines this information and enables managers to maintain a balance of key performance indicators (KPIs) across their portfolio. This level of integration could lead to a future where real estate management is much more dynamic and transparent.”
He also highlights the combination of business intelligence with generative AI. The technology behind tools like ChatGPT can analyse corporate data and generate comprehensive reports, allowing for more interactive and detailed data exploration. This capability extends to understanding documents and communications, providing deeper insights into asset performance and tenant interactions.
Framing tangible analysis against ‘intangible’ expert insights at PATRIZIA
PATRIZIA has been benefiting from the implementation of AI for a number of years, thanks to the work of Marcelo’s Data Intelligence team.
“We come from a different angle,” Marcelo explains. “We use several types of technologies and methods, including machine learning, that complement specific parts of the transaction process.
“For example, we have one flagship application called the Amenities Magnet report which tells us how attractive a location is. We collect relevant details on a local basis, which is important in real estate as each city is different, and we can test these on live deals using our algorithms.
“In our team, we have one of the largest real estate data lakes in Europe. As part of this, we love using open data. Downloading data that is outside of the real estate radar opens an important source of data for benchmarking assets. And my team is also involved when it comes to assessing pricing.
“Today, we can, at a very low level, provide insights on the drivers of rents. So, for example, the data tells us that building in kitchens in apartments in Munich will achieve higher rents. We know that in the UK, people avoid locations close to schools because of the noise and density of cars schools attract. And we know that, in Madrid, housing closer to cafes is culturally significant and can drive better rental performance. This is all based on tangible data.
“On the other hand, AI lacks intangible data. We had one example in a city in Europe in which the algorithm told us this is a very bad location from an amenities perspective. But when we spoke to the to the local expert, he told us: “no, what you’re buying here is not the access to amenities, but you’re buying prestige. This is a location in which people want to live because of prestige.” And this, of course, becomes a different transaction to most where the closeness to a bus station or kindergarten play a more important role.
“So the machine was right saying that the location is not well supplied. But there is an intangible aspect that our people on the ground identified and told us, and it’s this mix of tangible data and intangible insight from colleagues which shapes our approach at PATRIZIA.”
The nature of unintended consequences is that they can rarely be foreseen. If urban sprawl is the consequence of motorisation and health problems stem from plastics and asbestos, what could the unintended consequences of AI be?
Marcelo explains that one main fear is job displacement. Employees worry that AI might replace their roles. Nino stresses the importance of education and communication in addressing these fears. AI will not replace jobs but change them, automating repetitive tasks and allowing employees to focus on more complex and valuable work.
Another challenge is the misconception that AI can replace the intangible knowledge and gut feeling experienced professionals bring to real estate. Marcelo says: “AI should be viewed as a transparent, intelligent opinion that complements human expertise. It can provide data-driven insights, but the final decision should always consider human judgment and experience.”
A potential pitfall concerns privacy. AI-driven systems often require large amounts of data to function effectively. The collection and analysis of data from various sources, including property transactions, tenant behaviours and personal financial information, could lead to privacy breaches. Protecting sensitive information and ensuring data security will be paramount for real estate firms that do not want to suffer devastating reputational damage or financial penalties imposed by regulators.
Finally, AI’s ability to analyse vast amounts of data and predict market trends could increase market volatility. Automated trading and investment decisions based on AI models might cause sudden shifts in property prices, creating instability. Furthermore, if many market players rely on similar AI models, there could be a risk of synchronised behaviour, exacerbating market swings.
The future of AI in real estate
Yet, despite such fears, both experts believe AI in real estate will continue to evolve over the next decade. “In the short term, the focus will be on improving data quality and automating document interpretation,” predicts Nino. “This will lead to more efficient processes and better decision-making. In the longer term, AI could revolutionise how real estate is managed, moving towards a more integrated, real-time approach.”
Marcelo argues that emerging technologies like digital twins and advanced generative AI will drive change. “The ability to have all relevant data at your fingertips and interact with it dynamically will change the landscape of real estate management,” he says. “While predicting the exact future is challenging, the trend towards more intelligent, data-driven decision-making is clear.”
In the end, the pair say, the risk of falling behind is significant for companies that do not embrace AI. Waiting for a ‘killer application’ is not the solution; instead, integrating AI into daily operations across multiple areas will lead to impactful applications. Marcelo warns that slow adopters may face existential risks as the industry shifts towards more efficient, AI-driven processes. In other words: you snooze, your business loses.
