Artificial intelligence in real estate
Key Points at a Glance
- AI is an integral part of the real estate industry: 93 percent of German real estate companies are already using it or plan to do so (JLL, 2025).
- A wide range of applications: from asset and portfolio management to property and facility management and tenant communication via chatbot, all the way to robotics and energy management/ESG.
- The key benefits: analysis of large volumes of data, time savings, greater efficiency at lower costs, and stronger customer loyalty.
- Unresolved Challenges: Data protection, data quality, and employee acceptance are considered the biggest hurdles—with a lack of personnel (79 percent) and poor data quality (68 percent) ranking at the top of the list (ZIA/EY, 2025).
- The Outlook: AI is becoming a key to success and a competitive advantage. It speeds up processes and enables data-driven decisions, but it does not replace humans.
The development of ChatGPT has brought the topic of artificial intelligence (AI) into the public eye, but research in this field has been ongoing for decades. AI has been used in certain areas for quite some time, such as in robotics and the automotive industry. Strictly speaking, AI (Artificial Intelligence) refers to learning systems characterized by their ability to adapt to their environment: They use their interfaces with the outside world to optimize themselves, independently perform complex tasks, and continue learning as they operate.
AI has also been making inroads in the real estate industry for some time now. According to JLL’s Global Real Estate Technology Survey 2025, 93 percent of German real estate companies are already using AI or planning to do so. AI is not only taking over operational tasks but is also transforming decision-making processes. “AI is no longer a vision of the future in the real estate industry—it’s part of day-to-day business,” says Claudia Boymanns, Exhibition Director of EXPO REAL. “Those who use data intelligently are able to make faster and more informed decisions today.”
Wie wird KI in der Immobilienwirtschaft eingesetzt?
Areas of application for AI include asset and portfolio management, property and facility management, market forecasting, customer interaction, and building automation (see Smart Buildings). The technologies used include machine learning, natural language processing, and computer vision.
Asset and Portfolio Management
AI analyzes market data and portfolio metrics at a speed and depth that manual processes cannot match. Decisions regarding strategic direction are thus no longer made subjectively but are based on a robust data foundation. AI-powered predictive models make it possible to identify market movements early on and make data-driven portfolio decisions. In addition to market data, location-specific factors—such as infrastructure, micro-location, and demographic trends—are also incorporated into AI-powered real estate valuation.
Specific use cases:
- More Accurate Real Estate Appraisals Based on Extensive Market Data
- Shorter turnaround times for due diligence reviews, including automatic translation of foreign-language documents
- Preparation and review of property listings to ensure consistent quality
- Contract and Accounts Receivable Management in the Rental Sector
- Risk and potential analyses for individual assets and for the overall portfolio
Property and Facility Management
Especially in maintenance and repair management, AI can identify early on when specific measures are needed. This allows for proactive action rather than rigidly following a schedule. One important use case is predictive maintenance: AI analyzes sensor data from building systems and equipment, identifies impending failures early on, and initiates maintenance before damage occurs. This reduces downtime and lowers maintenance costs.
Another example is cleaning. Until now, cleaning has usually been done on a set schedule and according to a set checklist, regardless of whether it is necessary.
Short-term spikes in demand are not taken into account, nor are unnecessary cleaning cycles. Using sensor systems, AI manages cleaning based on actual demand, ensuring cleaner surfaces while making more efficient use of resources.
Other use cases:
- Predictive Maintenance Based on Sensor and Equipment Data
- Cleaning based on actual needs via sensor control rather than a fixed schedule
- Optimized space management through analysis of actual usage
- More Efficient Management of Catering and Other Services
Robotics and Automation in the Real Estate Industry
Many repetitive or physically demanding tasks tie up staff who are needed elsewhere—from building inspections to data entry. Robotics and automation handle such tasks reliably, more quickly, and often in places where access is difficult or dangerous for humans. This reduces the workload on employees, lowers costs, and improves accuracy.
Specific use cases:
- Drones are used to inspect buildings and facades, even in hard-to-reach areas such as roofs or high-rise facades
- Autonomous cleaning robots operate in commercial properties and maintain large areas on their own
- Robotic Process Automation (RPA) automates repetitive administrative tasks such as data entry, invoice verification, and master data maintenance
Energy Management and ESG
AI can control heating, cooling, and lighting in real time based on actual demand, thereby reducing energy consumption. At the same time, it can provide the consumption data needed for ESG reporting and carbon footprint calculations. In this way, AI becomes a tool that not only documents sustainability goals but also actively helps achieve them.
AI-Driven Customer Interaction
Real estate agents and property managers often receive a flood of customer inquiries outside of office hours. They then lack the capacity to respond quickly. AI-based communication answers recurring inquiries in real time, 24 hours a day, seven days a week. AI also uses data analysis to develop customer profiles, enabling personalized communication and targeted support.
Specific use cases:
- Responding to inquiries from tenants and prospective tenants around the clock
- Automated appointment scheduling and routing of complex inquiries to the appropriate contacts
- Personalized communication based on data-driven customer profiles
- Reduced workload in complaint management, freeing up time for more intensive support in other areas
- AI chatbots handle initial communication with tenants and prospective tenants and forward only complex issues to the appropriate staff members
What advantages does AI offer in the real estate industry?
The ability to analyze large amounts of data and recognize complex data patterns is one of the benefits of AI (not only) in the real estate industry. It can relieve humans of many time-consuming processes, increase efficiency, and contribute to clear, data-driven decisions.
Analysis of large amounts of data
While the human brain reaches its limits when processing large amounts of data, AI does not because it recognizes complex patterns and, as a learning system, quickly adapts its analysis as data patterns change. In addition, AI can quickly compare data—such as market data in portfolio analysis—allowing for a much more accurate assessment of potential and risk.
Time saving
Creating and reviewing contracts takes a lot of time. AI can take on many tasks here, from checking to suggesting improvements. The same applies to reviewing documents from the data room during due diligence, where AI can also provide a translation, for example.
Similarly, AI technology can handle most of the tasks associated with monitoring incoming payments and claims, which not only improves the dunning process and reduces administrative costs, but also makes it easier to keep track of claims that could potentially become a risk.
Efficiency
When it comes to processing and analyzing large amounts of data, AI increases efficiency. It can handle many administrative and/or time-consuming tasks better and faster and create more accurate analyses based on the data available. Increased efficiency always translates into cost savings.
Customer loyalty
Another aspect of efficiency is improved customer retention because of highly personalized interactions. With AI taking over many of the customer interaction and complaint management tasks, there is time to provide more intensive customer support in other areas. Real estate agents and managers have been aware of the potential of AI for some time and are using the appropriate tools.
What risks and challenges does AI pose for the real estate industry?
The biggest challenges in using AI in the real estate industry are growing mistrust, the potential for misuse, and high energy consumption.Low acceptance and a lack of trust
Since ChatGPT and chatbots have become part of everyday life, many people have had mixed—and at times frustrating—experiences with AI, which has not increased acceptance everywhere. At the same time, there is an ongoing public debate about the risks of the technology and its societal implications. The best way to build trust is through transparency in the use of AI and consistently high-quality results.
Potential for Abuse
Whether AI is used sensibly and effectively or abused is still ultimately up to humans. The technology itself is a tool—its application is determined by those who use it. Clear rules and responsible use are therefore crucial.
High Energy Consumption and Carbon Footprint
However, AI consumes a great deal of energy. Data processing takes place in data centers and requires high computing power, making it energy-intensive. This is particularly true during AI’s “learning phase,” but also applies to a large extent during its application. Energy consumption also results in a high carbon footprint unless only renewable energy sources are used.
AI in the Real Estate Industry: A Look into the Future
AI will further automate many processes in the real estate industry, particularly in operations. Through more accurate analyses and greater efficiency, AI will be decisive for the future success of real estate companies. In the real estate market, the speed of data analysis is increasingly the key to competitive advantage. AI can take on tasks and accelerate processes, but it cannot replace people. The use of AI-based solutions represents, so to speak, a step up to the next level of the digital age.
The 2025 Digitalization Study by ZIA and EY shows that the industry recognizes this potential: 90 percent of the companies surveyed consider AI to be a key technology for the next five years. At the same time, the same study identifies the obstacles that still stand in the way.
Key Challenges in the Use of AI:
- Data Protection: GDPR-compliant processing of personal data, particularly with regard to tenant and property data
- Employee Training and Change Management: Building AI Competence and Supporting Change in Everyday Work Life
- Data Security: Protecting Sensitive Company and Customer Data from Attacks and Misuse
- Data Quality: Reliable, Up-to-Date, and Structured Data as the Foundation of Every AI Application
- Acceptance: Employee trust that grows along with the reliability of AI
The figures demonstrate just how much implementation depends on these factors: The biggest obstacles are considered to be a lack of human resources (79 percent) and poor data quality (68 percent). For this transformation to succeed, therefore, it all comes down to people and their data: AI solutions only realize their full potential when employees are confident in using the tools. Appropriate training and the development of AI expertise are therefore becoming a key priority. The more reliably AI works, the more naturally it will be accepted in everyday work.
Data protection remains a key issue for all stakeholders, especially in areas where personal data is processed. And as with digitalization in general, data security is a key issue. Here, however, AI can also help on its own. For example, AI-based data discovery solutions can scan all storage locations and automatically classify the data. And because AI quickly and reliably detects patterns and anomalies in large volumes of data, it is ideally suited for identifying threats. It can monitor network traffic in real time and immediately alert the staff responsible for data security in the event of suspicious activity. In doing so, AI does not rely on predefined threat patterns but learns over time what is normal for a given company’s network and detects any notable deviations from that.
FAQ
The key benefits include the analysis of large volumes of data, significant time savings, greater efficiency at lower costs, and stronger customer loyalty. AI identifies complex patterns in large datasets, relieves humans of time-consuming routine tasks, and thus lays the foundation for faster, data-driven decisions.
AI is used throughout the entire value chain: in asset and portfolio management (valuation, property profiles, contract analysis), in property and facility management (predictive maintenance, demand-based cleaning), in tenant communication via chatbots, in robotics (drones, cleaning robots, RPA), as well as in energy management and ESG reporting.
Machine learning for forecasting and automated valuation models (AVM), computer vision for drone and building inspections, and robotic process automation (RPA) for recurring administrative tasks.
Very far: According to a survey by ZIA and EY, 90 percent of the companies surveyed consider AI to be a key technology for the real estate industry over the next five years.
Among other things, the following tasks can be automated: real estate appraisals, preparing property listings, responding to tenant inquiries, analyzing lease agreements, identifying maintenance needs, monitoring incoming payments, and recurring administrative tasks such as data entry and invoice verification.
The biggest challenges are mistrust, the potential for misuse, and high energy consumption—and the resulting carbon footprint. Added to these are data privacy and data quality. A lack of personnel and poor data quality are considered the biggest hurdles.
AI will continue to automate many processes and will increasingly be a deciding factor in competitive advantage—primarily due to the speed of data analysis. It accelerates workflows and enables data-driven decisions, but it does not replace people. It will be crucial for companies to train their employees and build AI expertise.