How AI is transforming the real estate market - from illusions to engineering tools

What AI is called in proptech, and why it is often not about AI

One of the main barriers in proptech is confusion in terms. Everything often falls under AI: chatbots, visualizations, scripts and interfaces. Even solutions without intelligence get the label "AI products."

Chat bots are not trained on scripts, BI systems only render data according to the rules, recommendation blocks sort objects by filters. Simple formulas are often hidden under "machine learning."

This creates false expectations: AI seems to have already been implemented. Therefore, when it is really needed - to predict failures or personalization - they are in no hurry to invest.

The real AI today is big language models. They need data, feedback and training. Without architecture, teams and goals, they don't work. Distinguishing automation from LLM is important not for the sake of accuracy, but for the sake of result. Otherwise, digital transformation will remain a facade.

Where AI really has a business effect in real estate

Proptech already has directions where artificial intelligence it gives not an illusory, but a verifiable result. The most mature cases are in building management, personalization marketing and technical diagnostics.

Building Management Predictive Analytics and Cost Optimization

According to the ArchiSense system, analysis of historical data using AI can reduce up to 50% of sudden equipment failures. The predictive maintenance strategy reduces operating costs by 25-30% and reduces the share of emergency failures by up to 70% compared to traditional regulations. This makes AI not just an assistant, but a driver of economy and reliability.

Technical Audit: CV

CV algorithms automate the inspection of facades and roofs - they record cracks, corrosion, defects, classifying them according to the degree of risk. So, in Dubai , the FEDS team using drones examined 33 buildings up to 280 meters. Collection and processing took 6 days, after another 6 - the report is ready. This approach replaces months of manual verification and speeds up decision-making.

Construction control and occupational safety: AI in action at the site

At the construction site, AI monitors compliance with the project. Samolet uses drones and robot dogs to create 3D models and compare with a BIM plan, identifying deviations and reducing costs. AI also increases security: video analytics (Smartvid.io, VizorLabs) records violations - the absence of helmets, vests, signal form. This reduces the number of incidents by up to 30% and increases discipline.

All these cases are united by one thing: AI does not work on its own, but as part of the infrastructure - within processes, data and goals. If this is not the case, the AI turns into a showcase, and if there is, it gives a measurable result.

Where AI is not yet working, and how it can be fixed

It is important to understand: AI is a mature technology, but its effectiveness depends not on the algorithm, but on the context. In proptech, AI often produces a weak result not because of its limitations, but because of the lack of conditions for training - unstructured data, non-standardized processes and weak feedback.

Customer Relations and Contact Centers

AI assistants are often lost in difficult or emotional scenarios - for example, when assigning rights or technical complaints. A hybrid approach is more effective, where AI helps the operator: classifies the request, offers answers, or forms a draft. Thus, the RE-GrievanceAssist system reduced manual processing of complaints on the real-estate platform by 40%, reducing costs and speeding up the reaction.

Generative Design and Design

AI quickly creates architectural concepts, but does not take into account the norms (SNiP, SP), the geography of the site and engineering restrictions. He also does not perceive the aesthetics and requirements of the customer. As a result, solutions look spectacular, but rarely feasible. AI is useful as a tool for preliminary study - generating options, which are then finalized by the architect. This requires integration with CAD, regulatory framework and close interaction with the project team.

Assessment of the technical condition of the facilities, especially the old fund

AI models are difficult to work with objects built 20-40 years ago. Such buildings do not have a digital history: there is no data on repairs, defects, loads. In addition, the objects themselves are extremely heterogeneous - designed according to different standards, often not digitized.

Why hype around AI is dangerous: not technology, but expectations

AI in proptech is not only a growth point, but also a risk area. The problem is not the algorithms themselves, but the management of expectations. Under the pressure of fashion, projects are launched "for show," without tasks, data and metrics. The result is not an effect, but disappointment.

One of the common mistakes is high expectations. AI is expected to replace the analytics department or boost sales. But without access to high-quality data, it simply does not work. Thus, the recommendation system, which did not take into account user preferences, led to a drop in conversion and was disabled. The other trap is the lack of a target. Implementation for the sake of technology turns AI into an extra interface. For example, a chat bot with ML on a low-traffic site solves problems that are easier to close manually.

After failure, the technology often disappears from strategy forever. The pilot is frozen, experience depreciates and competitors move on. At the same time, the main losses are not in the budget, but in lost opportunities and loss of trust. AI is not a showcase for investors, but an engineering tool. Without architecture, data and a distinct task, it does not work. And no hype compensates for this.

What distinguishes successful AI cases in proptech

Effective AI projects in real estate are not luck or a successful algorithm. This is always a consequence of engineering discipline, where the technology is built into a real business task and works in the right environment. There are several factors that are almost always present in successful cases.

First, it starts with a clearly articulated goal, not a desire to "do something about GPT." Mature teams come not from technology, but from pain: reduce the number of accidents, speed up the cycle of processing contracts, increase conversion. Without a metric to measure, AI doesn't know what to target and can't prove its worth.

Secondly, the model is impossible without data - and it is they who most often become a narrow neck. Successful teams pre-establish the collection, cleaning and marking of information from CRM, BMS, ERP, IoT. The data should not only be available, but also relevant, standardized and reproducible. For example, the model for predicting facade defects was able to learn only through a sample of more than 7,000 photographs manually marked by engineers.

The third critical element is the correct logic of the pilot. It should not be a showcase for an investor, but rather a tested hypothesis. There is a control group, understandable metrics (accuracy, economy, speed), and it is predetermined what is considered a success. If AI consistently predicts heating overload with an accuracy of more than 85% for six weeks, the solution is scaled. If not, they draw conclusions.

And finally, the key to a sustainable result is the team. Not one ML engineer, but a bunch of people who simultaneously understand both business and data. The product - formulates the problem, the analyst - translates it into the data language, and Data Scientist - collects the model, taking into account the specifics of the industry. Only in this configuration does AI become not an experiment, but part of the operational process.

Truly working AI is not what looks smart, but what is built into the contour of tasks, solutions and metrics.

What will happen tomorrow: where AI in proptech will definitely go next

AI in real estate is gradually moving from demonstrations to infrastructure, becoming part of everyday processes - from building management to investment decisions. Here are the key areas where it already works:

Adaptive building management.

BMS systems move away from harsh scenarios: based on operational data, complaints and weather, they learn to automatically regulate ventilation, heating and light. The massive emergence of AI-first BMS is a matter of the coming years.

Private LLMs from the developer.

Large companies are beginning to develop local models trained on internal data. This reduces cloud dependency, accelerates implementation, and minimizes the risk of leaks.

How developers and management companies approach AI without illusions, but with benefit

AI is not magic, but an engineering tool. It is not "embedded," but designed, trained and configured for a specific purpose. To get an effect, and not lose money and trust, it is important:

Automation first, then AI. LLMs work where there are repetitive processes and data: accidents, audits, long sales. First, automate the database - or immediately build the process with models in mind. The main thing is to solve the problem, and not "implement GPT."

Build infrastructure. Without stable data, integration into processes and teams that understand both business and AI, no model will work. First - the base, then - the algorithm.

Count the effect from day one. Any system should have KPIs that are understandable to business: savings, increased conversion, reduced time. Without this, the project is an experiment without a goal.

Check, not take a word for it. Instead of loud promises, ask for facts: on what data they trained, where they already work, what metrics. A lot of what is sold as AI is just automation with an interface.

AI in real estate does not solve everything, but it already decides a lot. Where there is data, processes and purpose, it gives a real result.