In real estate, artificial intelligence-driven models enhance property valuations and trend analysis, while predictive maintenance and blockchain technologies introduce new efficiencies. However, concerns about data accuracy and regulatory compliance persist. Léna Le Gal, a partner at EY Luxembourg in accounting compliance and reporting who also specialises in the real estate sector, shared these insights in an interview with Delano.
Kangkan Halder: Can you provide insights into how artificial intelligence performs market trend analysis and residential property valuations? Are clients actively seeking these analyses or handling them in-house?
Léna Le Gal: We typically find that AI is being used for several activities in real estate. Additionally, our clients are increasingly seeking AI-driven market trend analyses and valuations. Larger organisations with the resources to develop and maintain AI systems are aiming to develop these capabilities in-house, while smaller real estate organisations are looking to leverage AI through various service providers.
AI is utilised for pattern recognition, where machine learning algorithms identify patterns and trends in data that might not be immediately clear to human analysts, such as changes in market demand, pricing trends, and the popularity of certain property features. AI tools are also used for sentiment analysis, which involves analysing news articles, social media and other textual data to gauge public sentiment towards the real estate market, potentially serving as a leading indicator of market trends. Additionally, AI employs image recognition technology to assess property conditions and features. This technology analyses photographs to identify renovations, landscaping and other factors that might affect a property’s value.
Are real estate fund managers actively seeking these analyses? What are the three most common trends or data they look for?
Like most industries, the real estate sector is experiencing rapid changes due to AI development. While the future appears promising, real estate professionals remain cautious about the use of AI due to the sudden shift in the decision-making process and the reliability of data sources. These concerns are largely attributed to the AI lifecycle; it will take some time for the market to fully integrate AI, making it a gradual process.
Real estate fund managers commonly seek three main types of data. Predictive information to forecast market changes is crucial; accurate and reliable data for property valuations is essential, and detailed property features and personalisation, such as using data to better address choices and target clients based on individual property features, are also highly sought after.
AI can analyse past maintenance data and sensor readings from smart devices within the property to predict potential equipment failures before they occur.
Do AI-driven models offer more accuracy and reliability compared to traditional property valuation methods? If so, why?
AI enhances the valuation process by using machine learning algorithms to analyze vast amounts of data, including historical price data, neighborhood trends, current market conditions and comparable, with higher accuracy and efficiency. This results in more precise valuations, real-time updates reflecting market conditions and robust analytics for informed investment decisions.
At the same time, there are still concerns among real estate players about the accuracy and reliability of the underlying data being used in these valuation methods, and it will be some time before the market has complete confidence in AI-driven models. Even so, we are seeing significant advancements in AI, with systems benefiting from continuous learning--we expect over time that algorithms will be able to collect more relevant data and provide more accurate valuations and trend predictions over time.
Can AI be used for predictive maintenance in property management? How does it work, and what are the benefits?
AI can be used for predictive maintenance in property management. For example, AI can analyse past maintenance data and sensor readings from smart devices within the property to predict potential equipment failures before they occur. This proactive approach allows for preventive maintenance, avoiding costly repairs and minimising downtime for tenants. Additionally, by connecting AI to live data, it can optimise the use of heating, ventilation and air conditioning in buildings.
There is a shortage of skilled AI professionals which is acting as a barrier to transform the industry.
What trends are you observing in AI adoption within the real estate sector? What are the key focus areas?
AI facilitates faster decision-making with fewer resources. It enables real estate asset managers to efficiently filter properties worthy of further investigation without significant investment. AI can quickly research and summarise the value of top properties in a sector or track industry trends, enhancing asset managers’ ability to access more information.
AI also improves communication with tenants by assisting professionals in preparing negotiation scripts and improving sales skills. It helps tenants easily reach the appropriate individual or department through AI-powered chatbots that can direct inquiries to the correct contact.
Generative AI summarises key information from various contracts, such as expected monthly rent and the correlation between rent price and economic conditions, providing stakeholders with effective and concise data.
Have there been any limitations or challenges for the Luxembourg real estate sector with the new AI Act? What should companies do to ensure compliance?
Depending on how quick firms are to grasp the influence of the AI Act--and formulate an appropriate reaction--it could impact the speed of adoption of AI within the EU, and by extension in Luxembourg. The AI Act applies a risk-based approach, dividing AI systems into different risk levels: unacceptable, high, limited and minimal risk.
Companies must take time to chart out and classify the risk profiles of their AI technologies according to the criteria set by the AI Act. Should any of their AI solutions be identified as having limited, high or unacceptable risk, they must evaluate how the AI Act will affect their operations.
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Is the shortage of skilled AI professionals influencing the adoption of AI in real estate analytics? What can be done to address this issue?
There is a shortage of skilled AI professionals which is acting as a barrier to transform the industry. Collaborations with academic institutions will help companies comprehend the latest advances in the technology could definitely help churn out talent with the appropriate skills. Other ways to address this shortage include cultivating a culture of continuous learning to help employees see learning AI as a personal investment, and developing in-house training programs which will allow workers to learn about AI without having to invest significant resources. The decision on how and what to train hinges on whether asset managers plan to acquire tools or create their own models. It could end up being a blend of both, depending on how crucial the issue is strategically.
Are there potential roles for distributed ledger technology (DLT) and blockchain in the real estate sector? What are some possible use cases?
Possible use cases of DLT and blockchain in real estate include tokenisation, which converts real estate rights into digital tokens on a blockchain, simplifying and speeding up investments while making traditionally illiquid assets more tradeable. Smart contracts automate transactions like lease signings and property sales, reducing time, cost and fraud. Blockchain technology can enhance transparency and efficiency in financing, potentially introducing new models like crowdfunding and attracting international investors.
Tokenisation offers several advantages in real estate. It enhances transaction efficiency and liquidity by enabling the trading of asset parts and introducing new financing options like crowdfunding. It also improves accessibility, allowing smaller investors to enter the market with lower initial investments through asset fragmentation.
From EY’s perspective, the introduces a regulatory framework for crypto-assets across Europe, including licensing and transparency requirements. While this may pose entry barriers for traditional real estate actors, it could also present opportunities for real estate-related tokens, increasing market trust. However, the use of such technologies is still emerging, with further developments expected in the coming years.
Automated valuation models use AI to analyse various data points and deliver precise property valuations, reducing both time and cost compared to traditional appraisals.
Is AI impacting negotiation and acquisition processes in real estate investments? Are there emerging tools or methods?
AI is impacting negotiation and acquisition processes in real estate investments in a number of ways. For one, it is leading to data-driven negotiation Insights: AI systems analyse vast amounts of market data, providing real estate investors with actionable insights during negotiations. It also supports with predictive contract analytics: AI tools analyse past contracts and negotiation outcomes to predict the success of different negotiation strategies. These AI-driven tools and methods are transforming the negotiation and acquisition processes in real estate investments, making them more efficient, data-driven and strategic.
Can AI assist real estate professionals in assessing and mitigating environmental and social governance risks?
AI helps identify and evaluate environmental risks associated with real estate properties. Climate risk analysis uses climate data to predict potential risks like flooding, hurricanes, or wildfires. Platforms like Jupiter Intelligence provide detailed climate risk assessments for properties. AI can also analyse energy efficiency by assessing a building’s energy consumption patterns and suggesting improvements for energy efficiency, helping properties meet environmental standards and reduce carbon footprints.
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AI also supports social governance by ensuring properties comply with social standards and regulations. For one, community impact analysis looks at the potential social impact of real estate developments on local communities, including effects on housing affordability, local employment, and infrastructure. AI-driven platforms can also collect and analyse tenant feedback, helping property managers address social issues, improve living conditions and enhance community relations.
Are there examples of AI-driven innovations that have significantly changed how real estate funds approach market research and due diligence?
Yes, several AI-driven innovations are currently transforming the real estate industry. For example, automated valuation models use AI to analyse various data points and deliver precise property valuations, reducing both time and cost compared to traditional appraisals. AI-enhanced due diligence platforms aggregate and analyse commercial real estate data, streamlining the due diligence process by offering comprehensive property reports, comparable sales data and market analytics. And real-time market analysis tools use AI to continuously monitor market conditions and provide up-to-date insights, enabling real estate funds to swiftly adjust their investment strategies based on current market trends.
Have you come across any potential ethical concerns with AI in real estate management, and how should firms address them?
AI poses data privacy and cybersecurity concerns, with risks of over-reliance on data leading to potential errors. Adhering to regulations and maintaining a human-centric approach can mitigate these risks, ensuring AI serves as a supportive tool for enhancing well-being rather than an end in itself.