AI in the real estate market

The origins of artificial intelligence date back to the 1950s – to the work of Alan Turing and the Dartmouth Conference (1956), during which the very concept of AI (Artificial Intelligence) was defined. However, it is only in the past several years, with the growth of computing power, machine learning methods and most importantly, access to vast datasets, that AI has reached a level enabling it to significantly influence reality. Today, AI is present in many areas of our lives: in medicine it supports diagnostics and treatment, in the automotive industry it powers autonomous driving systems, in finance it manages investment portfolios and detects fraud attempts, in logistics it is used to optimize deliveries, in the creative sector it can generate texts, images and music, in education it enables personalized learning paths, in retail it provides precise customer targeting. AI is thus already a tangible tool reshaping the world’s functioning and its potential for future transformation appears inexhaustible. The AI revolution will not bypass the real estate market, a sector often regarded as slow to adopt technological change. In the coming years, artificial intelligence may revolutionize virtually every element of this market – from the valuation process to property management, brokerage and even the development process.

One of areas within the real estate sector, where AI is already being increasingly applied, is the automation of property valuation. AI enables quick estimation of property value using large datasets and advanced machine learning algorithms. The most commonly used models are so-called Automated Valuation Models (AVMs), which rely on analysing property attributes such as location, area, number of rooms, year of construction and so on. Methods employed include linear regression, decision trees, random forests and neural networks, capable of including complex and non-linear relationships between variables. The advantages of using AI in property valuation lie primarily in speed, scalability and the ability to analyse large, complex datasets, which helps to reduce subjectivity and human error. In the future, valuations may also incorporate satellite images, drone data, IoT (Internet of Things) sensors monitoring building conditions, or even weather data to reflect the impact of climate on property values. Naturally, for various reasons (including regulatory ones), these models will not fully replace expert appraisals, particularly in case of properties with atypical characteristics or in unique locations.

Property management, particularly of commercial properties, is becoming increasingly complex due to the growing number of facilities, the intricacy of infrastructure and the high expectations of both tenants and owners. AI-based solutions are increasingly being deployed to streamline and optimize management processes, minimize costs and maintain or improve service quality. AI is primarily used here to automate routine tasks – machine learning systems can automatically monitor the technical condition of buildings, analyse data from IoT sensors and devices, detect malfunctions and even predict them. Another significant application of AI in property management is energy consumption optimization. Intelligent Building Management Systems (BMS) using AI algorithms can analyse energy usage, regulate lighting, heating and air conditioning in real time, adapting them to users’ current needs. This results in substantial energy savings and improved comfort for building occupants. AI is also applied in lease management and tenant services. Chatbots and automated customer service systems can respond to tenant inquiries 24/7, log and track service requests and schedule maintenance visits. Moreover, these systems can analyse tenants’ preferences and behaviours, allowing better tailoring of additional services, thereby improving customer satisfaction. An important element is also the analysis of market and financial data. AI enables fast processing of information on property values, market trends, rent payments or operating costs. As a result, property managers can make more informed decisions regarding investments, leasing strategies or budget optimization.

Brokerage in real estate transactions also holds significant potential for AI-driven transformation. With substantial improvement in access to information (both in ease and speed), the role of brokers is likely to shift more toward advisory services and transaction facilitation. The future will belong to those, who can leverage modern technologies, such as property presentation and showcasing its potential. One of the most important applications of AI in brokerage will be the personalization of property listings. Advanced algorithms will be able to analyse customer preferences, past behaviours and purchasing profiles to propose properties that best match their expectations. This will make the property search process more efficient and less time-consuming. Brokers will gain the ability to present clients with curated offers more quickly, increasing the chances of successful transactions. Additionally, generative AI is already able to create realistic visualizations of interiors or entire buildings even from floor plans. In the future, “virtual staging” may become the standard, enabling clients to view properties in multiple design or lighting variants. As technology develops and data accessibility increases, analytical tools supporting real estate agents’ work will gain in importance. AI, through machine learning algorithms and large-scale data processing, will enable automatic analysis of listings, verification of their value and forecasting of market trends. This will allow for more accurate property selection and recommendations of the most suitable offers to clients. Faster access to high-quality information may in practice mean that the concept of a “market opportunity” will fade, as the best offers will disappear almost instantly. This could significantly reduce the ability of so-called “opportunity hunters” and flippers, for whom the reaction speed has so far been a key advantage. AI may also support brokers by automating marketing activities. AI-based systems can generate property descriptions, optimize social media advertising campaigns and manage client communications. Such solutions will not only improve customer service quality, but also reduce the workload of agents, enabling them to focus on more complex and demanding aspects of their work.

Transformation can also be expected not only in sales, but also in the rental housing market. Thanks to AI, landlords and brokers will be able to set rents in real time based on current demand, seasonality or events in a given city. This may lead to greater price volatility and reduced predictability for tenants, while at the same time enabling landlords to maximize profits. AI can also support the tenant verification process, contract signing and payment monitoring, which increases security, but also brings greater automation into the landlord – tenant relationship.

These are, of course, only the fundamental aspects of AI implementation in the real estate market. Beyond market participants, such as appraisers, brokers or managers, AI’s impact will certainly extend to all related professions and industries – developers, who will be able to more accurately forecast demand and design investments suited to client preferences, while also facing rising competition and likely margin reductions, architects, for whom AI brings the potential for a true revolution, as well as banks, insurers and the construction sector. Over the next 5 – 10 years, AI will become an integral part of the real estate market, influencing every segment – from investment planning, through sales and rental, to property management. Companies that implement these technologies early may gain an advantage, but success will require combining technological innovation with an ethical and transparent approach to clients. These areas hold the greatest challenges of AI adoption. Data monopolization, flawed data, algorithmic errors excluding or disadvantaging certain market participants, or privacy issues are just some of them. Therefore, AI is – and should remain in the future – merely a tool to enable more effective work for professionals operating on this market, and additionally subject to critical oversight. After all, property valuation or sales are not only about hard data, but also about soft factors – emotions and subjective perceptions.

Jerzy Ptaszyński
Research and Market Service Director