AI in Real Estate: Key Use Cases, Solutions, and Challenges

Let's dive into what's really happening beneath all the AI buzzwords and elevator pitches.

The role of artificial intelligence in real estate

According to a recent survey of 750 CFOs, 14% of real estate firms are actively using AI technologies, with another 28% in early adoption stages and 30% conducting pilot programs. The numbers tell an interesting story about an industry in transition.

But what do these numbers really mean in practice?

For many real estate firms, "using AI" might simply mean that junior team members are writing social media posts with ChatGPT—hardly a technological revolution.

This points to a deeper issue: while basic AI applications are becoming commonplace, few understand how to leverage them effectively. The low-hanging fruit is obvious: ChatGPT and AI productivity tools can speed up daily activities at the ground level. Automated email responses, basic document summarization, and social media content generation are easy wins that can save hours each week.

This surface-level adoption is reflected in a telling statistic: while 82% of surveyed agents report using AI for property descriptions, 60% admit to a poor understanding of how the technology actually works. This knowledge gap creates a ceiling—while individual tasks might be automated, firms struggle to scale these solutions across their operations.

The real estate industry's adoption of AI solutions is significantly lower than in knowledge-intensive fields like finance, insurance, or IT. However, the sector is starting to recognize that workflows already widely adopted in these industries—such as AI for insurance document reviews or financial analysis—can also be applied to tasks like lease agreement processing and property listings.

And here's where the real opportunity lies. Scaling basic applications into enterprise-wide solutions presents a far greater challenge, but also offers exponentially greater returns. While it's one thing to use AI to draft a property description, it's quite another to implement an AI workflow that can reliably process thousands of legal documents or analyze complex market trends across multiple jurisdictions.

This is why the most promising applications are emerging in back-office operations, where AI is perfect for tasks that humans find tedious but crucial. AI contract search and analysis are a good example, but this can expand way beyond just documents. For instance, when analyzing tenant applications in commercial real estate, AI can now assess not just credit scores but broader patterns of business success in similar demographics and locations. Understanding whether a Pret A Manger or a local café would be more successful in a particular Brooklyn neighborhood requires analyzing vast amounts of hyperlocal data—exactly the kind of analysis where AI shines today.

So how are leading firms actually implementing these solutions?

How AI is used in real estate

AI in real estate covers diverse use cases, tackling challenges across the entire industry, from residential deals to commercial property management.

Even a cursory glance reveals a common thread running through all these use cases: the centrality of data and document processing. It’s not surprising, given real estate’s reliance on intricate contracts, statements of value, compliance requirements, and the sheer volume of paperwork required for transactions.

Before diving into specific applications, it's important to understand the key technologies driving this transformation. The landscape has evolved significantly from basic Optical Character Recognition (OCR) and Robotic Process Automation (RPA) to more sophisticated solutions:

• Intelligent Document Processing (IDP) combines machine learning with traditional OCR software to understand document context and structure

• Retrieval Augmented Generation (RAG) helps AI models access and analyze vast document repositories while maintaining accuracy

• Generative AI can create detailed reports, summaries, and analyses while drawing from verified data sources

Modern AI solutions combine all of these functionalities and AI models with proprietary technologies. Now, this can lead to a very diverse range of results and performance levels. A basic GPT wrapper with a nice UI would be at one end of the spectrum, and a tool that builds on top of these technologies and moves it to the next level with IP technologies on the other end.

For example basic RAG systems might struggle to locate specific items like termination clauses across thousands of agreements. A state-of-the-art proptech AI for document processing like V7 Go can not only find these clauses but also analyze their implications across entire portfolios. For property management firms handling thousands of leases, this capability alone can save hundreds of hours of analyst time.

Additionally, in real estate, there’s a lot of paperwork all over the place that isn’t centralized in software. This presents a clear opportunity to modernize processes and rethink some of them entirely. With these technological foundations (and constraints) in mind, let's examine how leading firms are applying AI to solve real commercial real estate challenges.

Document reviews

The complexity of commercial real estate documentation isn't just about volume—it's about interconnection. A single property transaction might involve lease agreements referencing multiple amendments, each affecting different clauses within master service agreements, all of which impact the property's NOI calculations and ultimately its valuation.

Traditional document management systems fail here because they treat each document as a separate entity. Modern generative AI tools for real estate approach the problem differently. When analyzing a portfolio acquisition, for instance, the system builds a semantic network of relationships between documents. A change in a lease amendment automatically triggers analysis of its impact on service agreements, operational costs, and compliance requirements.

This capability becomes particularly crucial in mid-market acquisitions. Unlike deals involving large REITs with standardized documentation, mid-market transactions often involve family-owned properties with decades of inconsistent record-keeping. We recently saw a case where an AI system analyzing a seemingly standard retail property portfolio identified a pattern of undocumented sublease arrangements that materially affected the property's value—something that might have been missed in a traditional review process. Artificial intelligence is becoming an essential tool for tasks like analyzing technical documentation and performing AI legal contract reviews.

Copilots and agentic search

Everyone's talking about ChatGPT integrations, but the real innovation in commercial real estate isn't about chatbots answering customer queries—it's about autonomous AI agents that can actively search, understand, and synthesize information from across your organization's data.

What does this look like in practice? Real estate agents often need to ask their managers about property details—lease terms, pricing, legal requirements—and these managers then have to hunt through internal repositories for answers. AI platforms like V7 Go let you directly query this information across multiple sources: Salesforce, Hubspot, emails, and internal documents. It's not "ChatGPT for real estate"—it's more like Perplexity for your entire organization's knowledge base, with every answer linked back to its source.

On top of that, unlike traditional search systems that simply match keywords or even standard RAG implementations that find relevant documents, agentic search can follow complex reasoning chains. When asked about a specific lease term, it doesn't just locate the relevant clause—it actively explores related documents to understand implications, checks compliance requirements, identifies similar clauses in other agreements, and synthesizes this information into a comprehensive answer.

The difference becomes clear when handling complex queries that require understanding context and relationships. A traditional system might help you find a document containing specific terms. But agentic search can actively reason about the query and provide an accurate answer complete with citations.

Property valuation and due diligence

The real value of AI emerges when dealing with complex commercial property portfolios, where accurate valuation depends heavily on thorough due diligence. Traditional valuation methods often miss critical details hidden in vast document repositories. As one example from the field shows, just reading through a typical commercial property's documentation could take a human 4.6 years at an average pace—and that's before any actual analysis begins.

The challenge isn't just the volume of documents—it's understanding how different factors impact valuation. A single lease amendment might affect not just rental income projections, but also operating costs, compliance requirements, and ultimately, the property's NOI. In traditional approaches, these connections often get missed because nobody can manually cross-reference thousands of pages of documentation.

This becomes particularly crucial in mid-market acquisitions. At the upper end of the market, you're dealing with established firms and professional documentation that follows standardized formats. But try valuing a portfolio of properties owned by a company doing $15-20 million in revenue—you'll likely get a jumble of documents without clear organization. Nobody hands you a neat folder labeled "HR documents" or "supplier contracts," let alone a clear record of how various agreements affect the property's value. It's no surprise that automated due diligence powered by AI is becoming popular.

Computer vision and inventory documentation

OK, let’s address the elephant in the room—yes, AI can do things like this:

As cool as it looks, this isn’t really the best use of AI and computer vision.

In real estate, the true value of computer vision lies in practical, behind-the-scenes applications rather than flashy gimmicks like virtual staging or AI-enhanced visualizations. One of its primary roles is verifying that property listings accurately reflect reality. For example, if a rental listing claims there’s a shower but the uploaded bathroom photos only show a bathtub, AI can automatically detect and flag this inconsistency.

Unlike generative AI, which focuses on enhancing or creating images, computer vision leans on tasks like semantic segmentation and object tracking in videos. These capabilities help tenants visualize space utilization options, assist property managers in monitoring inventory, and support developers in tracking progress on construction sites.

Computer vision also plays a critical role in compliance and maintenance tracking. When property listings are submitted to rental platforms, AI can ensure the images meet platform policies, exclude unauthorized content like copyrighted materials or personal photos, and confirm they align with the property description. By automating these processes, computer vision enhances accuracy, reduces errors, and streamlines operations across the real estate industry.

While understanding the capabilities of AI in real estate is crucial, equally important is knowing which solutions can deliver on these promises. Let's examine the key players in the market and what sets them apart.

AI solutions for real estate

The following platforms stand out for their proven ability to address specific real estate industry challenges with sophisticated technology. Each offers distinct capabilities and pricing models suited to different segments of the market, from buyers, to individual agents, to large commercial operations. Let's examine their key features, use cases, and pricing structures to understand which solutions might best fit your needs.

V7 Go

At its core, V7 Go is a document intelligence platform that goes far beyond standard document processing. It combines advanced technologies like chain-of-thought reasoning and multimodal processing to tackle complex commercial real estate workflows that traditional document systems can't handle.

The platform stands out for its ability to not just extract information, but understand context and relationships across documents. For instance, when analyzing lease agreements, it can follow complex reasoning chains to understand how amendments affect master agreements, calculate financial implications, and verify compliance requirements—all while providing clear citations back to source documents.

Best suited for large commercial real estate operations and mid-market acquisitions where document complexity and volume demand sophisticated analysis capabilities. The platform particularly excels at portfolio-level analysis where understanding document relationships is crucial for accurate valuation and risk assessment.

Pricing:

• Free: 250 fields, limited credits, 10 seats

• Self-Serve: $249/month for 25,000 fields

• Enterprise: Custom pricing with dedicated support

Zillow AI

Zillow's AI technology started with the famous Zestimate but has evolved into something far more sophisticated. Their machine learning systems now power everything from property valuations to natural language search, processing vast amounts of data from public records, user submissions, and market activity.

The platform's real innovation lies in its predictive capabilities. By analyzing user behavior patterns and property data, Zillow's AI can identify market trends and buyer preferences before they become obvious. Their natural language search allows users to specify complex criteria about layout, location, commute times, and amenities in everyday language, while their recommendation engine delivers surprisingly accurate property suggestions.

While the platform serves both consumers and professionals, its most powerful features target real estate professionals who need deep market insights and advanced property analysis tools. The system is particularly valuable for agents and brokers who want to leverage data-driven insights for better client service.

Pricing:

• Core features free for users

• Professional services: Custom pricing

• Premium features available for agents and brokers (pricing on request)

HouseCanary

HouseCanary has built a comprehensive analytics platform that covers over 136 million single-family properties across the United States. Its AI engine processes vast amounts of property data, market trends, and historical information to provide detailed valuations and market forecasts.

Beyond basic property data, HouseCanary's AI analyzes complex market patterns to predict future trends and property values. The system generates customizable reports and portfolio analyses that help investors and lenders make data-driven decisions about property investments and market opportunities.

The platform is particularly valuable for investors, lenders, and large-scale property managers who need accurate valuations and market forecasts. Its portfolio management capabilities make it especially useful for firms managing multiple properties or evaluating numerous investment opportunities simultaneously.

Pricing:

• Basic: $190/year (2 reports/month)

• Pro: $790/year (15 reports/month + CanaryAI)

• Teams: $1,990/year (40 reports/month, 10 users)

• Enterprise: Custom pricing

RealScout

RealScout approaches real estate technology from a collaboration perspective, focusing on enhancing how agents and clients work together during property searches. Their platform combines sophisticated property matching with client behavior analysis to create a more intelligent search experience.

Unlike standard property search tools, RealScout's AI learns from client interactions to understand unstated preferences and requirements. The system monitors market changes in real-time, automatically alerting users to relevant new listings or market shifts that match their refined criteria.

The platform is designed for real estate agencies and brokerages that want to enhance their client relationships with data-driven insights. It's particularly effective for teams that value a more personalized, collaborative approach to property matching.

Pricing:

• Agent: $99/month (100 contacts)

• Team: $249/month (1,000 contacts, 2 licenses)

• Brokerage: From $399/month

• Additional contacts: $9.99/month per 500

Surface AI

Surface AI is an AI-powered workflow software designed to enhance operations in the multifamily housing sector. By integrating with existing systems, it prioritizes tasks, highlights risks, and streamlines processes to improve efficiency and resident satisfaction.

The platform offers features such as summarizing resident information, drafting policy-compliant communications, and generating insights swiftly. These capabilities aim to boost operational consistency, quality, and speed, leading to increased Net Operating Income (NOI) and more satisfied staff and residents.

Surface AI is particularly beneficial for property management teams seeking to optimize workflows and enhance resident care through AI-driven solutions. Its focus on multifamily housing operations makes it a relevant replacement for the previously mentioned CRE AI in discussions about AI applications in real estate.

Pricing: Custom pricing based on implementation scope and organizational needs

Now—

Having explored both the potential and the tools, a crucial question remains: How do you actually implement AI successfully in a real estate organization? The answer lies in understanding some common patterns—and pitfalls.

AI in real estate: best practices and challenges

Implementation is where the rubber meets the road—and where many AI initiatives in real estate either soar or stumble.

After spending time with dozens of commercial real estate firms implementing AI, patterns emerge that separate successful deployments from expensive experiments. And these patterns might not be what you'd expect.

The process problem

The story typically begins the same way: ambitious plans to modernize operations across the board, usually sparked by a competitor's latest tech announcement or a compelling vendor presentation. Eager executives get excited about AI and decide they're going to automate everything at once.

Three months and several hundred thousand dollars later, they've got a mess of half-working systems and frustrated employees. Meanwhile, the firm down the street started with just lease abstraction, got it working smoothly, and is now gradually expanding to other processes.

Pick one pain point that's costing you real money right now. Maybe it's the fact that your analysts spend 70% of their time just digging through closing documents. Or perhaps it's those quarterly portfolio reviews that take three weeks to compile. Start there.