How AI and Big Data are Revolutionizing Real Estate

How AI and Big Data are Revolutionizing Real Estate
The future of commercial real estate is data-driven, with AI and big data paving the way for smarter investments and better outcomes.
In recent years, AI and big data have emerged as transformative forces across various industries, including commercial real estate (CRE). These technologies are reshaping the way industry professionals conduct market analysis, manage properties, and make investment decisions. As the adoption of AI accelerates, it is becoming clear that those who embrace these tools can gain a competitive edge in the rapidly evolving commercial real estate landscape.
Traditionally, commercial real estate has relied on a combination of intuition and historical data to make market predictions. AI, coupled with big data, has drastically improved the accuracy and scope of market analysis. Platforms leveraging AI can process massive amounts of real-time data, including property prices, economic indicators, demographic shifts, and local amenities. By aggregating and analysing this data, AI systems can offer various types of predictive insights that can help real estate professionals and investors identify emerging trends. Such as:

• Data-Driven Market Insights, including predictive analytics and automated valuation models (AVMs) are used for forecasting market trends or property valuations by analysing comparable sales data, property characteristics, and market conditions.
• Enhanced Property Management: AI-powered platforms capable of automating many day-to-day operations, from predictive maintenance to tenant relations, these AI systems can contribute to the efficiency of managing large commercial properties. These platforms can offer services such as predictive maintenance, smart building technologies and automated tenant communication.
• Personalized Marketing and Leasing: AI and big data are also transforming how brokers and property managers market and lease commercial spaces, by helping create targeted marketing campaigns and enhance leasing optimization process.
• Streamlining Due Diligence and Transactions: One of the most time-consuming aspects of commercial real estate transactions is the due diligence process. AI can streamline this by automating data collection, analysis, and document review.
• Optimizing Space Utilization: AI’s ability to analyse how spaces are used is helping companies optimize their office layouts and space utilization.
• Investment and Portfolio Management: for investors managing large commercial real estate portfolios, AI provides a critical advantage in identifying trends and optimizing asset performance by portfolio optimization and Dynamic Pricing Models, which is especially useful in sectors such as hospitality or retail where pricing flexibility is key to maximizing profitability.

AI and big data are revolutionising the commercial real estate industry by providing deeper insights, improving operational efficiencies, and enhancing decision-making processes. As technology continues to advance, industry professionals who harness the power of AI will be better equipped to navigate the ever-changing real estate landscape and drive long-term success. Whether it’s through predictive analytics, smart building technologies, or automated property management, the future of CRE is undoubtedly data-driven.
Several platforms stand out for their capabilities in property management, investment analysis, market research, and data analytics. Some of the best platforms commonly used in the industry are:

• CoStar, a leading commercial real estate information and analytics provider.
• LoopNet, an online marketplace for commercial properties. It’s a key resource for brokers and investors looking to buy, sell, or lease properties.
• Real Capital Analytics (RCA), specialised in commercial real estate transaction data, offering insights into property sales and investment trends.
• Argus Enterprise, a widely used software for property valuation, financial modelling, and asset management in commercial real estate.
• Yardi, a property management software that covers residential and commercial real estate management.

Global commercial real estate companies are increasingly using big data platforms to improve their business and decision-making processes, some of them have even developed their own data and insight platform. Others, such as Cushman & Wakefield, for example, are actively integrating big data and AI into their operations by collaborating with Microsoft to utilize the Azure OpenAI Service and other advanced technologies to improve client service and operational efficiency.

The Adoption of AI and BIG DATA in Serbia

The adoption of AI and big data in Serbia’s real estate sector, like in many emerging markets, has been relatively slower compared to Western European countries or the US. Serbia ranks 57th out of 193 countries in the AI Readiness Index (Oxford Insights, 2023), making it a leading country in the field of AI within the Western Balkans. The country is actively investing in AI infrastructure and aligning with global standards, as demonstrated by its membership in the Global Partnership for Artificial Intelligence (GPAI). Additionally, the Sebian government is developing a new AI development strategy for 2025–2030, followed by an action plan, with a focus on responsible and sustainable implementation in various sectors, including real estate. In 2023, Serbia introduced Ethical Guidelines for the Development, Application and Use of Responsible AI. The establishment of the Council for Artificial Intelligence further supports the coordination of activities to implement this strategic framework. Serbia fosters AI innovation through grants, tax incentives and start-up support. Companies involved in R&D benefit from salary tax exemptions and corporate tax incentives, making Serbia an attractive destination for AI-related investments.
When it comes to specific commercial real estate applications, the adoption of AI is not yet widespread. Early movers in Serbia’s private sector are experimenting with AI in property management, market analysis, and predictive analytics. Public institutions and larger corporations are leveraging AI technologies, supported by the National AI Platform and collaboration between academia and industry.
The biggest impact of AI in the Serbian commercial real estate market might first be seen in property management. AI-driven predictive maintenance, energy management, and tenant communication tools can help cut costs and improve tenant satisfaction. However, these tools are typically adopted by larger, more institutional landlords or property management companies that have the resources to invest in such technologies.
In sectors like retail and logistics, where real estate needs to adapt quickly to shifting demand, AI could play a larger role. As Serbia’s logistics sector grows (driven by e-commerce), companies may start adopting AI to optimize warehouse operations, manage supply chain logistics, and predict demand for new facilities. Retail parks and shopping centres might also begin using AI for footfall analysis, customer behaviour prediction, and optimizing space utilization. These are areas where big data and AI have already shown success in international markets and could find their way into Serbia in the coming years.
While the potential is there, several challenges remain such as Lack of Awareness as many firms, especially smaller local players, may not fully understand the potential of AI or how it can be applied to improve their operations. Also, AI solutions often come with high upfront costs for software and infrastructure. This can be a barrier for companies without large budgets for technology investment. Last but not least, the effectiveness of AI is dependent on high-quality, consistent data. A lack of centralized data or market transparency may limit the potential of AI-driven tools.
In the near future, AI’s role in real estate could evolve significantly, particularly in areas like market forecasting, optimizing property utilization, and streamlining operational efficiencies. For the sector to catch up, greater education, investment, and strategic adoption will be crucial.