{"id":57844,"date":"2026-06-18T08:30:09","date_gmt":"2026-06-18T15:30:09","guid":{"rendered":"https:\/\/www.yardi.com\/blog\/?p=57844"},"modified":"2026-06-18T08:30:57","modified_gmt":"2026-06-18T15:30:57","slug":"the-transformation-of-the-real-estate-sector-by-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.yardi.com\/blog\/en-gb\/the-transformation-of-the-real-estate-sector-by-artificial-intelligence\/","title":{"rendered":"The Transformation of the Real Estate Sector by Artificial Intelligence"},"content":{"rendered":"\n
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\u201cMake sure the foundation is solid before you start building with AI\u201d<\/em><\/strong><\/p>\n<\/blockquote>\n\n\n\n

One of the most challenging topics currently facing the real estate sector is artificial intelligence. What can AI mean for the sector? At Provada, Vastgoedmarkt spoke with Neal Gemassmer, vice president and GM of International at Yardi.<\/p>\n\n\n\n

Yardi develops industry-leading software for all types and sizes of real estate companies across the world. The family-owned business employs more than 10,500 professionals and has 40 offices worldwide. Through the Yardi platform<\/a>, clients can streamline the management of various types of real estate, such as commercial and residential, by bringing the entire real estate lifecycle, including administration, leasing, and finance, together in one centralised platform.<\/p>\n\n\n\n

For Gemassmer, there is no doubt that AI will transform both the company and the sector. The only question, he says, is how. \u201cThere are so many ways AI can be deployed. The question is simply how best to apply this new technology within the real estate sector. There is also a distinction between how we use it internally and how we enable our clients to use AI to further improve their processes and diversify their activities,\u201d he explains.  <\/p>\n\n\n\n

Empowering clients<\/h2>\n\n\n\n

The impact on Yardi itself does not lie in reducing team sizes, Gemassmer emphasises. \u201cMany large companies in the IT sector are laying off people because of AI. We have no plans to do that. We focus on our clients and embrace AI, because we believe it helps us serve our clients better. Our starting point is to empower our people to use AI and development tools effectively. If we can develop and enhance products faster, clients benefit directly. The biggest impact of AI we see right now is with our development team; the speed at which they can build using tools like Cursor, Claude, GitHub, MCP (Model Context Protocol), Figma, and other prototyping tools is tremendous. In addition, we are looking at how to embed AI into and integrate it with the core platform to strengthen existing features, as well as by enabling integrations with language models and AI providers such as Anthropic, Google Gemini, Microsoft Copilot, or OpenAI, so that our clients can use any AI engine within the Yardi platform.\u201d<\/p>\n\n\n

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According to Gemassmer, deploying AI is particularly interesting for the real estate sector because it deals with a great deal of fragmented data from various sources. Without a solid foundation, however, he believes there is little point in deploying AI. \u201cAI feeds on data \u2013 it does not create data, nor does it automatically clean it up. AI is an enabler;<\/em> it helps you become more productive and automate business processes. But when data is spread across multiple platforms, it is difficult to form a coherent data structure. Our vision is: start with a strong foundation. Just like with a building: make sure the foundation is solid before you start building.\u201d<\/p>\n\n\n\n

Data, terminology, and a strong data foundation<\/h2>\n\n\n\n

In concrete terms, this means that companies need to strive for a well-structured database in which all information is consolidated. Gemassmer: \u201cAcross different platforms, the definition of a \u2018property\u2019, a \u2018unit\u2019, or a \u2018tenant\u2019 can be completely different. How do you combine that data? And how do you expect AI to be able to read those different data definitions? With a strong foundation, meaning good data governance around terminology and technology aligned to the various sub-sectors of real estate, you can deploy AI more productively.\u201d<\/p>\n\n\n\n

Gemassmer cites statistics showing that 95 per cent of AI projects fail. \u201cI think that is because people try things out, but a clear definition of the goal is missing. Many pilot projects didn\u2019t have a clearly defined objective, or the data foundation was not in order. As a result, it was difficult to measure outcomes and make them repeatable. Bain Consulting has researched this and concludes that most failures are attributable to inconsistent datasets, which goes back to the foundation.\u201d<\/p>\n\n\n\n

Most organisations struggle with this. \u201cThe average company uses 20 to 40 different IT platforms. If they all have different definitions and data structures, it is difficult to pursue a coherent AI strategy. Real estate is also a unique field: we sometimes make things hard for ourselves with different measurement standards, KPIs, and definitions. The sector needs to look internally at how we can help ourselves by standardising more. AI also makes it possible to test ideas faster. But we should not become overly optimistic and think that AI will solve all our problems.\u201d<\/p>\n\n\n\n

Solving challenges with AI <\/h2>\n\n\n\n

That is also why it is important not to chase every new AI feature and instead choose to deploy AI only to create value from a strong foundation. \u201cOver the past four years, our goal has been to map out which processes or activities in the real estate sector create a lot of friction, such as difficult communication with an applicant, tenant, supplier, or investor, and to digitise those processes. What matters most is first determining which problem you want to solve. Blockchain was the <\/em>big topic at this conference ten years ago. However, blockchain remained a technology in search of a problem. Outside of crypto, it never really took off. With AI, things will be different \u2013 but AI only solves a problem if you can define what the problem is and then link it to a business process.\u201d<\/p>\n\n\n\n

Once the problem is clear, an application can also create immediate value. Gemassmer illustrates this with an example from the housing market. \u201cMost people search for a house or apartment outside of office hours. You want to personalise that process for the customer, so they do not simply leave their name on Funda and then have to wait four days to be called back. Now we can make it immediately personal: before the rental team arrives the next morning or on Monday morning, a viewing has already been scheduled, the lease may already be signed, payment made, and a reference check carried out. That is the biggest consumer-facing AI application to date,\u201d he explains.<\/p>\n\n\n\n

AI Chatbot Assistance<\/h2>\n\n\n\n

The next phase where AI can help is tackling internal business processes. Gemassmer: \u201cThink of automatically summarising lease agreements or accelerating the monthly financial close. If you automate those processes while retaining human validation, agentic AI orchestration can have a major impact.<\/p>\n\n\n\n

From a consumer perspective, people have also become more willing to accept help from a chatbot. They now know that AI is much smarter than it was a few years ago. In the past, chatbots were machine-learning systems with pre-programmed answers to specific questions \u2013 so-called intents. AI takes that to a higher level: it can respond much more personally to each individual question. If someone enquires about an apartment and mentions that their dog is a golden retriever named Pixie, the bot can take in that information and ask how much Pixie weighs and continue through the application process from there.\u201d<\/p>\n\n\n

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Data privacy and GDPR are crucial in this context. Gemassmer: \u201cEurope leads the way when it comes to privacy legislation. I see two sides: data privacy and the risk of disinformation from AI models \u2013 so-called hallucinations, where an AI engine gives incorrect answers or fabricates information. The responsibility lies with technology companies like Yardi, with the clients who use the platforms, and with the AI models themselves. Our approach: the source of record remains in a protected environment. With the right enterprise model, that information is not shared in the broader AI environment and no new information is created. Furthermore, risks remain such as data breaches, incorrect information, or offensive outputs, but we learn continuously. Everything we know now will look different in twelve months.\u201d<\/p>\n\n\n\n

The Human Element <\/h2>\n\n\n\n

AI does not replace people, and the goal of deploying AI is not simply to make people do more. According to Gemassmer, it must always be a complement. \u201cSpotify announced a 50 per cent workforce reduction a few years ago because of AI. Six months later, they rehired almost everyone because they could not do without the service model and the human element. I do expect a different kind of impact, though. Seven years ago, most organisations had no broad remote working policy. Now, very few organisations still require people to come into the office five days a week. The role of the office is already changing. It will be interesting to see how AI influences that further.\u201d<\/p>\n\n\n\n

AI offers opportunities, but you need to keep it personal. \u201cYou can ask Copilot or Claude to optimise your calendar for the next two weeks, or have an agent respond to emails. But do you want the agent to do all those things, or do you want to preserve that personal element? It is about \u2018learning by doing\u2019. The only certainty we have is that if we sit here again in twelve months, we will be having a different conversation. Not a worse one, but we will have learned a great deal.\u201d<\/p>\n\n\n\n

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AI is only as good as the data underneath it. Yardi connects your portfolio, properties, and teams into one source of truth – so AI can finally work for commercial real estate.<\/p>\n\n\n\n

One platform. One dataset. Smarter decisions.<\/p>\n\n\n\n

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Learn More<\/mark><\/a><\/div>\n<\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

\u201cMake sure the foundation is solid before you start building with AI\u201d One of the most challenging topics currently facing the real estate sector is artificial intelligence. What can AI mean for the sector? At Provada, Vastgoedmarkt spoke with Neal Gemassmer, vice president and GM of International at Yardi. Yardi develops industry-leading software for all types and sizes of real estate […]<\/p>\n","protected":false},"author":897,"featured_media":57845,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_s2mail":"yes","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3387,3501,3479],"tags":[],"class_list":["post-57844","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-commercial","category-events","category-mixed-portfolios"],"acf":[],"yoast_head":"\nYardi Blog<\/title>\n<meta name=\"description\" content=\"Neal Gemassmer sits down with Vastgoedmart at Provada 2026 to discuss the transformation of the real estate sector by AI.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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