How U.S. Real Estate Is Using AI to Transform Property Data

Over the past decade, artificial intelligence has moved from a niche experiment to a driving force in the real estate industry. While many companies are only now testing AI’s potential, a small group of early adopters has spent years building solutions that shape how property data is used and understood.

Restb.ai is one such company. Founded in Barcelona more than 10 years ago, Restb.ai began developing computer vision technology for real estate before the field entered the mainstream. “We got into convolutional neural networks before they were even widely recognized,” says Dominik Pogorzelski, President of Restb.ai’s MLS business unit. The co-founders began experimenting long before most real estate professionals had heard of AI.

Early adoption came with significant challenges. Pogorzelski recalls that outreach efforts were often met with confusion. “I remember sending out emails asking if people wanted to talk about AI, and some would respond asking who ‘Al’ was,” he says. The lack of industry awareness meant Restb.ai’s early years were spent convincing potential clients that AI had practical value in real estate.

From Photos to Property Intelligence

What began as a narrow focus on image recognition has grown into a company centered on “property intelligence.” Restb.ai’s evolution mirrors AI’s broader role in real estate, moving from basic automation to complex data insights that affect how homes are marketed, valued, and regulated.

A core problem Restb.ai set out to solve was the untapped potential of property photos. Listing images contain vast amounts of information about a home’s features, layout, and condition, but most of that data has historically been ignored. “The information inside photos wasn’t being leveraged,” Pogorzelski says. As a result, agents and buyers often missed valuable details that could influence a property’s appeal and price.

Today, Restb.ai’s technology is embedded in the operations of nearly 100 Multiple Listing Services (MLSs) across the U.S. and Canada. Their AI acts as the unseen engine for many tools that agents use when creating or managing listings, analyzing photos to extract features, flagging compliance issues, and improving searchability.

AI Automates Agent Operations

Restb.ai’s tools automate the identification of property features directly from listing photos. The system can detect amenities such as pools, fireplaces, and vaulted ceilings, ensuring listings accurately reflect what a home offers.

This automation corrects a common market inefficiency. Pogorzelski notes that about one in three homes with a water view fail to mention it in listing features, even though the detail appears in the photos, according to Restb.ai’s internal data. Since water views are highly sought after and can command price premiums, missing this information means lost opportunities for sellers and agents.

MLS staff also benefit from automation. Previously, compliance teams would manually review every photo for violations, such as visible faces, signposts, or inappropriate content. “We had customers with staff who clicked through every single photo,” Pogorzelski says. “Now AI can do that initial scan, making the process far more efficient.”

The scale is significant: Restb.ai processes over 1.5 billion images monthly, while continuously refining its AI models through ongoing research and development.

Visual Search for Homebuyers

One of Restb.ai’s most innovative applications is visual search, allowing buyers to use images rather than keywords to find properties. If a consumer sees a kitchen they love, whether in a friend’s home or a magazine, they can upload a photo to a platform using Restb.ai’s technology and instantly find listings with similar kitchens.

This approach offers a “Pinterest-like experience,” making it easier for buyers to find homes that match their tastes without relying on imperfect search terms.

AI Adoption Gains Momentum

The real estate industry has traditionally been slow to adopt new technologies, but adoption is accelerating. Pogorzelski observes a sharp increase in AI adoption, driven by agents recognizing the need for efficiency and better data. Still, some resistance remains. “There are agents who are still afraid of AI and reluctant to try even the most basic features,” he says.

Despite this, Restb.ai’s integrations now reach almost a million agents in North America, indicating mainstream adoption is well underway.

The Risk of AI Images

As AI’s capabilities have grown, so have new risks. One emerging concern is the use of AI-generated images in listings, which can misrepresent a property’s true condition or features. “AI-generated media, especially images, is a growing issue in real estate,” Pogorzelski explains.

To address this, California passed legislation requiring brokers to disclose when listing photos have been digitally altered and to provide access to the original images. Other states are considering similar regulations. Restb.ai is developing tools to help MLSs detect AI-generated images and maintain compliance with new and pending regulations. The rapid evolution of image manipulation technology means companies must stay ahead of both technical and regulatory changes.

Improving Property Valuation

Beyond enhancing listings, Restb.ai’s technology is also being used by appraisal management companies and lenders to improve property valuation. Traditional valuations rely heavily on human judgment, which can introduce bias and inconsistency. “There’s a big blind spot in how appraisers and lenders assess condition and quality — they depend on subjective opinions,” Pogorzelski says.

By analyzing millions of property images, AI can provide a more objective and consistent assessment, reducing risk and improving accuracy for lenders and investors. Restb.ai’s valuation tools have revealed significant lending risks tied to inaccurate or biased appraisals, underscoring the need for more data-driven approaches in property finance.

A Decade of AI Experience

As more companies enter the AI space, Restb.ai’s decade of focused development provides a significant advantage. Pogorzelski points out that building a proof-of-concept AI differs greatly from deploying a system that works reliably at a national scale. The company’s experience with diverse property types and local market nuances, from rural Montana to urban New York, allows Restb.ai to handle edge cases that generic AI often misses.

“We have over 80 people working on this every day, building technology that is accurate, fast, and reliable,” Pogorzelski says. This depth of experience sets Restb.ai apart from newer entrants that may lack the data or expertise to handle real-world complexity.

The Push to Digitize Property

Looking to the future, Pogorzelski sees the biggest opportunity in fully digitizing property information. By extracting as much data as possible from images and other sources, AI can create highly accurate digital representations of homes. This will enable smarter, faster decisions for agents, buyers, lenders, and regulators alike.

The pace of digital change in real estate is accelerating, driven by companies that invested in AI long before it was widely discussed. As the technology matures and regulation catches up, the conversation has shifted from whether to use AI to how to do so responsibly. Those who engage now stand to benefit from more efficient operations, better data, and a clearer view of the market. Those who delay risk falling behind as the industry’s digital future takes shape.

About the Expert: Dominik Pogorzelski is President of the MLS business unit at Restb.ai. This Barcelona-based property intelligence company has been developing AI-powered computer vision technology for real estate for more than a decade. Restb.ai’s solutions are embedded in nearly 100 Multiple Listing Services across the U.S. and Canada, reaching almost a million agents in North America.

This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.

This article was originally published on the KeyCrew Journal here and is republished on The Platform Report with permission.