The global market for artificial intelligence applied to the property sector is set to grow from $301 billion in 2025 to $404 billion in 2026, representing an annual growth rate of 34.3 per cent. These figures are taken from the ‘AI in Real Estate Market 2026’ report by Research and Markets.
Behind these figures lies something that affects you too, if you’re looking for a home. Because the way people search for property has changed more in the last three years than in the previous twenty.
Until recently, property searches worked by process of elimination: you’d open a website, set a price range, select the number of rooms, filter by area, and get a list of properties for sale that matched your criteria. Today, you can message a chatbot saying, “I’m looking for a bright three-room flat near a primary school in Sesto Fiorentino, preferably with a terrace,” and receive a well-reasoned reply.
It’s a huge leap. But here’s the part that hardly anyone mentions: AI is brilliant at certain stages of the house-hunting process and completely unreliable at others. And the difference between the two can cost you a fair bit
The impact of AI on property searches: what’s really changing today
Traditional property portals are organised into structured categories: price, square metres, number of bathrooms and so on. They’re excellent if you already know exactly what you want. They’re terrible, however, if you’re still trying to work out what you’re looking for (which is the situation most people find themselves in when they start looking for a home).
Conversational tools turn the approach on its head. You can describe a situation (“I work at Careggi, I have two young children, a budget of 300,000 euros, and I don’t want to rely on a car”) and get advice on which areas make sense. It’s an orientation phase that used to require weeks of haphazard research or a conversation with someone in the know.
The sector is adapting accordingly. Agencies are adopting systems for automated valuation, automated follow-up management, and the generation of property listings and floor plans. The stated aim of proptech operators (i.e. companies, start-ups or professionals who use digital technology to modernise and improve the property sector) is to free up time from repetitive tasks so they can focus on consultancy, the part of the job that no software can do.
From portal highlights to conversations with AI
Let’s look at a practical example of the difference between these two methods.
Traditional search: filters “Prato, 2 bedrooms, €150,000–200,000, with terrace”. You get 47 listings. You open them one by one. Thirty-five of them don’t interest you for reasons the filter couldn’t pick up on: on the ground floor overlooking a busy road, on the fourth floor with no lift, in an area you weren’t familiar with and which isn’t right for you.
Conversational search: explain that you’re looking for a home in Prato for a family with a four-year-old child, that you work in Florence and take the train every day, that you want an outdoor space, and that your budget is between €150,000 and €200,000. The assistant explains which areas of Prato are close to the stations, which have good nursery schools, where you can find properties with private gardens within your price range, and so on.
It doesn’t provide you with a list of available properties, that’s what the portal or estate agent does. It gives you the framework to search for them more effectively. It’s a matter of putting things into context, and AI is extremely useful at this stage.
Algorithmic valuations and predictive value analysis
The other major area is automated valuation estimates, known in the trade as AVMs (Automated Valuation Models).
Here’s how it works: the system cross-references asking prices in the area, historical price data and the property’s stated features, and returns a price range in a matter of seconds. It’s free, instant and requires no appointments.
These are reliable tools and serve a specific purpose: to give you a rough idea of the price range. If you’re trying to work out whether a property listed for sale at 280,000 euros is above or below market value, an automated valuation can help you work this out in no time at all.
Be aware, however, of the structural limitation, which is not a flaw that can be resolved with more data: the algorithm does not enter the house. It cannot see the damp on the north-facing wall, it does not know that the electrical system dates from 1978, it is unaware that the block of flats is currently embroiled in a dispute over the roof renovation, and it does not know that the building only gets sunlight for two hours a day.
To provide an overview of the broader context, our 2026 property market forecasts set out the trends that no single estimate can capture on its own.
Using ChatGPT to look for a home: real opportunities and risks to avoid
We need to be precise here, because the most common confusion centres on what a generic language model knows and what it does not know.
A general-purpose conversational assistant (also known as a chatbot or virtual agent) is trained on text: it can explain what a flat-rate tax is, tell you the difference between bare ownership and usufruct, and help you structure your reasoning. It’s very good at this.
What it lacks is real-time access to property databases. It does not know about the listings published this morning, it cannot see the properties that estate agents have in their portfolio but have not yet listed, and it does not have access to the actual closing prices of transactions (which are confidential and consistently differ from the asking prices).
In this regard, there are vertical integrations capable of bridging part of this gap: some portals have developed applications linked to their own databases, which update the data continuously. These tools are therefore distinct from a generic model.
The rule of thumb. If you ask, ‘Explain to me how a preliminary contract works’, you’re using AI for what it’s designed to do. If you ask, ‘How much is a flat on Via Tal dei Tali in Florence worth?’, you’re asking something it cannot know, and the answer you’ll receive will look like a precise figure without actually being one.
What questions to ask the AI before starting your research
Here are four examples of prompts that you can copy and adapt before and whilst searching for your next home using AI. These prompts are designed to elicit reasoning, not data that the model does not possess.
Advice on neighbourhoods: “I work in [neighbourhood/company] and commute by [car/train/bike]. I have a budget of [amount] and am looking for [type of property]. What criteria should I use to decide which neighbourhood in [city] to look for a home in? List the factors to consider, not specific properties.”
Preparing for the viewing ‘I’m about to view a ground-floor flat from the 1970s with a garden. Could you give me a checklist of what to look out for and what questions to ask the seller or estate agent?’
Understanding documents: “Please explain to me in simple terms what this clause in a preliminary contract means: [paste the text]. What are the risks for me as the buyer?”
Cost breakdown “List all the expenses a buyer incurs in addition to the price of the property in Italy when buying a first home. Explain which ones are fixed and which are variable.”
Note what they have in common: they require a method, not specific figures relating to your property. That’s where AI really comes into its own.
Why general-purpose AI doesn’t know the real price of a house
The price of a property exists in three different forms, and only one of them is publicly available.
There is the asking price – the one you see on property websites. It is a statement of intent by the seller, often overestimated by 10–15 per cent in anticipation of negotiations.
There is the OMI valuation system used by the Italian Revenue Agency, which sets a range of values for each homogeneous zone. It is a useful institutional benchmark, but it operates at a macro-level: within the same OMI zone, you might find both a renovated block of flats and a dilapidated building.
Finally, there is the actual closing price – the one stated in the notarial deed. This is the figure that really matters, and it is one that no language model can know, because it is not published in an aggregated and accessible form.
Added to this is everything the algorithm cannot detect: terraces not registered with the land registry, mezzanines built without planning permission, restrictions imposed by the Heritage Authority on properties in historic centres, and discrepancies between the actual condition of a property and the land registry plan. All these factors can be worth tens of thousands of euros or prevent a property sale from going through, yet they do not appear in any searchable database.
AI as a tool for bureaucratic and legal literacy
There is, however, one area in which artificial intelligence is doing something truly valuable for homebuyers, and one that is not discussed nearly enough: reducing information asymmetry.
The property sector is full of jargon: provisional agreement, preliminary contract, deposit to confirm the sale, penalty deposit, commission, cadastral compliance, title deed, remaining mortgage. For those who work in the sector, these terms are self-explanatory. For those who buy a house once or twice in their lifetime, they are a complete mystery.
Historically, this imbalance has had one specific consequence: people would sign documents they did not fully understand, simply on trust. Sometimes it went well, sometimes it didn’t.
Nowadays, you can turn up for an appointment already knowing exactly what you’re about to sign. And here, AI is a tool for empowerment, not replacement: it doesn’t eliminate the need for a professional; it simply enables you to ask them the right questions.
Translating settlement agreements, land registry extracts and notarial deeds into plain Italian
The most effective way to use this is as follows: take the clause you don’t understand, paste it into an AI chat, ask for an explanation in plain language and, above all, ask what risks it entails for you.
It works particularly well on three documents.
- The preliminary contract, in which deadlines, penalties and conditions precedent are set out. Understanding in advance what happens if the mortgage is not granted is more important than any price negotiations.
- The land registry extract, which contains the assessed value, category and identifying details. Knowing how to read it allows you to check that what you are buying matches what is stated.
- The title deed, which sets out the history of the property and may contain some surprises: donations, unresolved inheritances, easements.
An important point to note: use AI to understand, never to make decisions. The explanation prepares you for your discussion with the solicitor or adviser; it does not replace it. And before pasting in any documents, remember to check carefully whether they contain your personal data or that of others.
If you’d like an overview of the checks you need to carry out, you can start with our guide on what to know before buying a house.
Understanding the Green Homes Directive and energy performance
Another area where AI provides tangible support is the energy sector, which has taken centre stage following the European directive on the energy performance of buildings.
If you’re considering a property in energy efficiency class F or G, the real question isn’t ‘how much does it cost today?’ but ‘how much will it cost me to bring it up to standard?’. You can ask a chatbot to list the typical measures required to move up a class, to explain the difference between internal and external insulation, and to help you prioritise the work based on the cost-benefit ratio.
These are rough estimates, not quotes. But they do give you an idea of what you’re asking for when you meet with the technician. This topic is explored in more detail in our analysis of the The Green Homes Directive and what it means for Italian homeowners.
The limitations of artificial intelligence and the value of the human adviser
Now we come to the uncomfortable part. Where the algorithm gets it wrong, and how to recognize it.
Hallucinations, Algorithmic Bias, and Deceptive AI Photos
Hallucinations. Language models, when they don’t know an answer, tend to produce a plausible one instead of admitting the gap. In real estate, this translates into incorrect tax rates, nonexistent regulatory references, and price-per-square-meter figures invented with three decimal places of apparent precision. The problem isn’t the error itself: it’s that the error has the same confident tone as a correct answer.
Outdated data. A model trained up to a certain date doesn’t know about subsequent regulatory changes. Rules on building incentives, tax rates, and first-home benefits change frequently: an answer that was correct two years ago may be wrong today.
Deceptive virtual staging. This is the most concrete problem for home buyers. AI editing tools allow digitally furnishing empty rooms, removing cracks and moisture stains, and correcting lighting and colors. Used transparently, they are a legitimate enhancement tool (our guide to home staging for selling your home explains how it works when done properly). Used without disclosure, they become false advertising.
Negotiation and Urban Planning Compliance Cannot Be Delegated to an Algorithm
There are three things no software can do today in place of you or a professional.
Verify urban planning and cadastral compliance. To do this, you need to access municipal archives, compare building permits with the actual state, and assess whether a discrepancy can be remedied and at what cost. It’s technical work on documents that are often not digitized. An algorithm doesn’t know that veranda was enclosed in 1994 without a permit.
Conduct a negotiation. The final price depends on how urgently the seller needs to sell, how long the property has been on the market, what other offers exist, and how the relationship is structured. This is information that circulates between people.
Guarantee legal protections at closing. Mortgage checks, condominium status, system compliance, presence of restrictions. If something goes wrong, a professional is liable through their insurance. A chatbot is not.
| Artificial Intelligence | Real estate consultant | |
| Analysis Speed | Immediate | Takes time |
| Area Guidance | Good at general level | Excellent, with direct knowledge |
| Actual Closing Prices | No access | Knows from direct experience |
| Actual Property Condition | Cannot assess | Verifies in person |
| Urban Planning Compliance | Cannot access archives | Verifies documents and permits |
| Negotiation | Not applicable | Core of the work |
| Legal Liability | None | Insured and regulated |
| Availability | 24/7 | By appointment |
On this point, a clarification is warranted that also applies to valuations: an algorithmic valuation does not replace a real estate appraisal, which has technical value and, in certain contexts, legal value.
How to Choose a Real Estate Agency in the Age of Artificial Intelligence
If AI automates part of the work, what distinguishes a good agency today?
The most honest answer is: what it does with the time automation frees up.
An agency that uses technology well reduces downtime (data entry, listing generation, appointment management) and reinvests that time in what matters: seeing properties in person, verifying documents before putting them on the market, managing a negotiation. An agency that uses technology poorly produces more listings, faster, and more superficial.
Here’s what to look for, concretely.
- How listings are written. Generic, interchangeable descriptions are a sign of mass-produced content. A listing written by someone who has seen the home contains details no model can invent: actual exposure, what you hear from the windows, what the building is like.
- Whether they disclose virtual staging. A reputable agency specifies when images are digitally edited. It’s a reliable indicator of overall transparency.
- What they verify before publishing. Ask directly: cadastral compliance, building permits, mortgage status. Those who work well have already done these checks.
- How well they know the area. This is where the gap with any automated tool remains greatest. Knowing that values have moved on a certain street, that a public project will change a neighborhood, that a building has approved special assessments: this is information accumulated by being on the ground.
A deeper look at these criteria is in our guide on how to choose the right real estate agent, along with the advanced real estate marketing strategies that established agencies adopt to enhance property value.
Summary: How to Integrate AI into Your Search Without Taking Risks
The right way to use artificial intelligence in home search is as an accelerator for the initial phase, never as a substitute for final verification.
Use it to orient yourself among neighborhoods, to understand the jargon, to prepare for viewings, to decipher documents before signing them, to get a sense of the orders of magnitude. In these phases, it saves you weeks and puts you in a position to speak as an equal with professionals.
Don’t use it to determine what a property is actually worth, to assess its condition, to verify urban planning compliance, to calculate taxes and installments on which you’ll base a decision, or to replace an in-person visit.
And adopt a three-step verification method every time AI gives you a number: ask for the source, compare with official OMI data, have a local professional confirm it. If the first step doesn’t produce a verifiable source, the other two become mandatory.
Frequently Asked Questions About Artificial Intelligence and Real Estate
Can I ask ChatGPT to find me a home in Florence or Sesto Fiorentino?
It can suggest neighborhoods, help you define search criteria, and analyze listings you submit. However, it doesn’t have real-time access to all available offers, nor to properties agencies have in their portfolio before publication. It’s an orientation tool, not an updated real estate search engine.
Does artificial intelligence know the actual prices of the real estate market?
It processes published averages and historical data, so it returns reliable orders of magnitude. However, it doesn’t know the actual closing prices of negotiations, which are confidential and on average lower than asking prices, nor the maintenance condition or compliance of a specific property.
How can I tell if an AI response about real estate is reliable?
Follow three steps. First: always ask for the source of the data and verify it actually exists. Second: compare values with official OMI quotations from the Revenue Agency. Third: have a technician or consultant who knows that market confirm the information. If the data concerns regulations or tax rates, verify it’s updated to the current year.
Can AI replace the real estate agent in a transaction?
No. AI speeds up initial selection and data analysis, but negotiation, urban planning and cadastral checks, and legal protection at closing require the involvement of a professional who is accountable for their assessments. These are activities that involve liability, and liability cannot be delegated to software.
Is it safe to rely solely on AI to calculate taxes or mortgage payments?
No. Generalist models can confuse first-home tax rates, ignore the specific cadastral income of the property, or rely on outdated banking conditions. Use AI to understand what expense items exist, then have the calculations done by a notary, accountant, or lending institution.
Want to verify whether the quotations suggested by AI for your area in Florence, Prato, or Sesto Fiorentino match actual transaction prices? Request a transparent market report from our consultants.
