Real estate moves billions of dollars, but over the years, its methods haven’t changed much: manual lease reviews, generic listings, slow decisions. Agents and investors work with disconnected systems and hard-to-parse data, which costs time, money, and even reputation.

 

Resistance to change gets more expensive as customer expectations grow. Tenants want personalized listings, and real estate investors need quick, data-backed insights - things you can’t deliver with teams spending days on low-priority, high-urgency tasks.

 

McKinsey reports that generative AI could bring between $110 billion and $180 billion in value to real estate. Early adopters have already seen more than 10% growth in NOI through tenant retention, smarter asset management, and automation. Yet most firms haven’t even considered AI.

 

Soon, the competition in real estate will center around intelligent automation, not just prices and locations. We know this firsthand, as our team has worked on many digital transformation projects, designing NLP tools, AI engines, and predictive maintenance modules for real estate. In this article, we’re sharing what we’ve learned: experience, insights, and numbers.

Contents

TL;DR

  • McKinsey reports that generative AI could add $110B–$180B in value to real estate. 
  • Our success story: a U.S.-based rental platform cut lease review time from 25 to just 2 minutes, and saw a 22% increase in lease-to-deal conversions.

How AI Agents Are Changing Real Estate:

4 Functions of AI Agents In Real Estate:

  • Analyze user behavior to highlight leads most likely to convert.
  • Track listing activity and market shifts to recommend real-time price updates.
  • Answer routine questions and escalate complex ones to agents.
  • Spot early signs of missed payments or maintenance issues based on tenant patterns.

AI and Real Estate Application Examples:

  • AvalonBay: AI leasing agent raised conversions by 65%.
  • CBRE: Predictive AI cut maintenance and energy costs by 20% and reduced site visits by 25%.
  • Redfin: AI home recommendations get 4× more clicks, leading to stronger leads.

When AI Starts Paying Off:

  • Short-term: It saves time and helps close more deals. One client of ours gained $2.85M in value with better lead scoring and AI assistants.
  • Long-term: It improves forecasting and adds lasting value to the portfolio.

Success Story: 92% Cut in Lease Reviews Time with AI for Real Estate

Every real estate business wants to grow, but sometimes that brings more problems than opportunities. Our client, a rental platform from the US, contacted us when their legal team got overwhelmed with lease reviews. They were looking for a fast, lasting solution, and we knew how to help.

Challenges Faced

As the firm expanded into new cities, it started getting more lease agreements from landlords. One review took around 30 minutes, which was not a big deal when the volume was low, but after expansion, it became a bottleneck. Deals slowed down, and so did growth.

At first, our client thought of a simple rule-based chatbot to flag abnormalities. But that idea fell apart fast: leases came in every format imaginable, PDFs, phone photos, Word docs, even handwritten notes from small landlords. Basic tools were useless with such inconsistent data quality, so we went with something smarter.

Final Solution

We built a custom AI real estate system specifically for their problem. It used OCR to read different file types and a fine-tuned language model we trained on thousands of lease docs. It could analyze key details: lease length, rent terms, renewal options, cancellation rules, and so on. Our developers also added a feedback loop, so the system kept learning and improving as the client’s team used it.

Probably the best feature was real-time processing. The system broke down leases and flagged the important parts in seconds, giving employees clean summaries and instant risk highlights.

Project Results

Aside from a huge morale boost for the legal team, we saw some more measurable results:

  • Review time dropped from ~25 minutes to under 2.
  • Deal conversion rose by 22% in the first 90 days.
  • The legal team spent 40% less time reviewing leases and focused on bigger priorities.

The Result of Inoxoft's Case Study: Building a Real Estate AI Agent

Want your processes to be faster, cleaner, and less repetitive? Let’s talk about building a custom AI agent for your business.

Practical Uses and Benefits of AI In Real Estate

Let’s rewind to that same real estate firm, which was overloaded with leads. Now imagine a scenario where the AI system can say, “These five leads are three times more likely to buy,” based on what they searched, the tone of their messages, and similar cases. That’s not just possible, that’s what AI agents already do. Let’s go into more detail.

“One of the most exciting things we’re seeing is how AI can finally get us away from the computer and back in front of people, our clients. For a long time, the real estate sector has pushed back on technology. But with AI, we’re starting to see real, practical ways it can take over the tasks that used to eat up our time. Whether it’s writing a newsletter, generating a listing description, or building out content for social, we’re not just saving time, we’re showing up better prepared. The more you work with it—edit it, learn how to prompt it, get your voice into it—the more it becomes a tool that supports your day-to-day.”

—  says Ron O’Neil, real estate recruiter and trainer

AI in Lead Qualification

AI can’t replace your instincts, but it can back them up with better information. You don’t have to waste time chasing leads when they’re already sorted by who’s more likely to buy. AI pays attention to how long someone stayed on a listing, whether they wrote a detailed message, or if they’ve browsed your site before.

Say one person looked at the same property five times, checked out the map, and asked about mortgage options. Another clicked once and disappeared. AI can tell the difference and help you act on it. As a result, your best agents can then focus on leads who are genuinely interested, while colder prospects get a light touch through automated messages. About 25% of real estate firms using AI say it’s already helping them close more deals.

AI in Property Valuation

When real estate professionals do traditional CMAs (Comparative Market Analyses), they gather a few comparable property listings and leave the rest to their gut feeling. Sometimes it works out, but AI takes that basic idea and makes your pricing spot-on 100% of the time. Agents consider all sorts of data, from local trends to buyers’ intentions in nearby neighborhoods, to make free property value estimates.

Some of the latest AI agents update their pricing models daily, so property valuators always have the most relevant information. And some automated valuation models can predict market trends with 90% accuracy and provide pricing estimates within 3% of the final sale, a much tighter margin than usual. No more pricing too high, watching a listing sit there, and then rushing to change it – you get it right from the start.

AI in Virtual Tours and Staging

Visuals matter more than you think. If the photos or walkthroughs don’t impress, the deal is dead before it even starts. In the past, you had to book a designer, bring in rented furniture, and wait days to stage a place. Now, you can generate digital staging in minutes. What’s great is that it’s not just one look: AI can show the same condo in a clean, modern style, a cozy family one, or something more artsy.

It’s faster (about 80% faster, actually), cheaper, and gets more eyes on the listing. Some agents report 200% more inquiries when they switch to AI staging compared to the old-school method. It just shows how much presentation drives action.

Artificial intelligence and real estate are coming together to support us with data-driven decision making. Smart agents filter out noise from data, seize more opportunities, and create cohesive processes from fragmented ones. People stop wasting their cognitive energy on minor tasks. Leasing teams can focus on advising, legal teams can work on strategy, and so on. What I see is that firms using AI become sharper, quicker to move, and more ready to scale.”

—  says our senior business analyst.

Ready to rethink how you price, stage, and sell? Let’s talk about what AI can do for you.

Benefits of AI in Real Estate and Practical Ways to Use It

Using AI for Real Estate: 4 Strategies to Consider

Many real estate firms start with a statement: “We want to use AI,” when they really should start with a question: “What can we fix with AI?” Is it a data issue, a workflow gap, or a team bottleneck?

AI on its own isn’t all that helpful, but when it has right supporting infrastructure, it changes everything, from how you scale to how quickly you respond. Let’s look at some practical use cases, with expert comments from our senior business analyst.

How to Use AI Agents for Real Estate: Four Practical Examples

Go from Chasing Leads to Focusing on Buyer Intent

Real estate companies rarely have a lead volume problem, but a lead communication one is more common. Every lead gets treated the same. Reps spend time on people who are just browsing, while serious buyers wait in line.

AI can rank leads based on intent and readiness to act. It looks at behavior (not just names and phone numbers), how people browse, what they click, and how fast they follow up. After that, it prioritizes which leads deserve immediate attention and which can be nurtured over time.

But this only works if the team tracks leads consistently and is willing to trust the signals. Some people hesitate, but they need to understand that AI isn’t replacing the sales process—it’s making it smarter.

Replace Static Pricing with a Performance-Based One

Pricing still works the way it did years ago: set it, hope for the best, and wait. If no one bites, someone may eventually lower it, but by then, interest has already faded. AI suggests a better approach.

Using AI to track views, saves, clicks, and skips, you can catch soft signals that your property prices are off long before it’s obvious to the team. Add in neighborhood trends, seasonal demand, and even inventory velocity, and pricing becomes responsive, not reactive.

“To harness AI strategically, you have to stay flexible. Pricing teams need to adjust mid-listing based on signals, not instincts. It takes some getting used to, but once you do, you’ll see the benefits and spot more opportunities than ever.”

Improve your outcomes with AI agents! Reach out, we’d love to make your vision real.

Scale Support with AI Without Losing the Human Touch

Listings, rental terms, documents, timelines – different people, same questions, day in and day out. Answering each one feels exhausting, unnecessary, and over time, it can break the system under the weight of volume.

A good AI assistant can automate customer support in a blink. When built right, it doesn’t spit out canned responses, but understands how your listings are structured, pulls data from your backend, and adapts it to the context. It also knows when to reply and when to pass it off. As a result, your teams stop copy-pasting and get back to doing real work.

“I always say: design with care. Train your agents on the words people use, clean and organize your data before feeding it in, and – most importantly – define when AI needs to escalate to human support.”

Turn Property Management from Reactive to Predictive 

In property management, it’s common to react to problems, not prevent them. A tenant reports a leak, someone misses a payment, the heat stops working – and only then does the team respond. But the thing is, that approach is far less effective and more costly. 

With the right data, AI can flag early warning signs and make accurate predictions: maintenance issues, rent delays that hint at bigger problems, and tenants who want to leave. Catching these early gives you a chance to step in before someone gets frustrated.

“Execution here depends on structure. You need to build clean data pipelines and connect systems. AI can’t use data that’s scattered across emails, PDFs, and spreadsheets. Your agent can be a great predictive tool, but it has to be a part of the system, not a standalone.”

 

Real-World Examples of Artificial Intelligence in Real Estate

What was a futuristic idea just a moment ago is now a reality for global companies. Artificial intelligence real estate does more than automate boring tasks, it supports people with pricing, leasing, maintenance, and investment strategies. Let’s look at three distinct cases.

AvalonBay’s AI Leasing Agent

A leader in the business world for nearly 30 years, AvalonBay Communities – a major apartment real estate investment trust (REIT) – decided to rethink its leasing and resident services. Partnering with New York-based startup Elise AI, they created a virtual assistant called “Sidney.”

Sidney chats with potential renters via text or email, answers questions, and even sets up self-guided tours, supported by smart locks, AI tools that answer FAQs, and digital maps. Sidney also takes care of routine after-hours inquiries that would otherwise sit in a backlog for days.

Once someone becomes a tenant, they can use Sidney’s help to sign leases, request maintenance, or get updates. With AI support, property tours are available seven days a week, ten hours a day, which is well beyond traditional office hours. Renters love the instant responses so much that many even ask to meet “Sidney” in person, mistaking it for a real human.

Since presenting their virtual assistant to more than 90,000 apartments, AvalonBay’s lead-to-lease conversion rates grew by over 65%, saving them operational costs and 2–3 hours of work per employee each day. 

Overall, the AI assistant has helped AvalonBay capture more leads, convert more leases, and improve customer relationship management, becoming a core part of their growth strategy.

If you also want to make AI part of your long-term strategy, contact us to see how we can help!

CBRE’s Nexus

CBRE, the world’s biggest commercial real estate services firm, launched its Smart Facilities Management (FM) Solution, covering over a billion square feet and 20,000 client sites.

At the heart of this system is Nexus, an AI-based platform. It monitors data from HVAC sensors, energy meters, occupancy information, work orders, and more, tracking equipment performance and predicting when something may need attention. On top of that, it gives property managers tips, such as adjusting climate settings or combining space usage to save energy. CBRE says this approach has led to big improvements, including less downtime, longer-lasting equipment, and lower labor costs for building owners.

To put it in numbers, replacing routine checkups with predictive maintenance has helped CBRE’s clients cut energy use by up to 20% and reduce site visits by 25%, as technicians now only go out when necessary.

According to the firm’s report, Smart FM Solution has been a key revenue driver for CBRE, granting them a 13% revenue increase in 2023. So, managing billions of square feet with AI has paid off, both in cost savings and in more reliable building operations.

Redfin Matchmaker

Redfin, a nationwide real estate brokerage and listings site, wanted to make house-hunting less stressful and more engaging, so they created an AI recommendation engine called  “Redfin Matchmaker”. Tracking user search behavior, preferences, and historical sales data, it suggests the homes people are likely to choose.

Although it’s based on a simple idea – making users spend more time on the site – it showed impressive results. Redfin’s CTO shared that when Matchmaker suggests a home, users are 4 times more likely to click on it compared to traditional filter-based listings.

What’s more, relevant AI recommendations lead to more tour requests and give Redfin’s agents an extra competitive edge. The company’s experience shows that AI can pick up on subtle interaction details that traditional software can’t, making it more effective for both user experience and analytics.

AI Real Estate Investing: What to Expect and When

Executives often ask when they’ll start seeing a return on their AI investment. The answer comes down to how you use the tool and what you want to achieve with it. Done right, AI delivers in two ways: quick wins that improve daily operations and long-term value that builds up. Let’s discuss both.

AI Agents ROI: What to Expect From AI Agents in Real Estate

Short-Term Wins: Faster Deals and Leaner Teams

During the first 6-12 months, AI agents solve bottlenecks and add structure to your processes. Let’s take an example.

A mid-sized leasing and property management company with:

  • 50 leasing agents
  • 30,000 inbound leads a year
  • 5% close rate before using AI
  • $4,000 revenue per closed deal

Each agent spends about 2 hours a day on repetitive admin tasks. Now, let’s see how AI changes that:

  • With intent-based lead scoring, AI classifies buyers, flagging high-potential ones so your agents close more deals.
  • AI assistants take over lead responses, FAQs, and scheduling.

Here’s what happens:

  • Close rate grows from 5% to 6.5% → 450 more closed deals
  • 450 × $4,000 = $1.8 million in additional revenue

At the same time:

  • 50 agents × 2 hours/day = 26,400 hours saved each year
  • At $40/hour internal cost → $1.05 million in time saved

Altogether, that’s about $2.85 million in value within the first year. Even if you factor in $500K for development, data integration, and support, you still have a net gain of $2.3 million, or a 470% ROI. And that’s not even counting the less measurable benefits, like happier agents or better user experience.

Long-Term Payoffs: Predictability and Strategic Edge

After the first year, AI changes how your business works in general, making your flows more predictable, organized, and strategic.

  • Forecasting tools improve cash flow visibility and identify investment opportunities. You get a better idea of when rent will come in, where leases might end, or which real estate properties could sit vacant. 
  • Predictive maintenance models reduce emergency repairs, extend equipment life, and stabilize operating costs. 
  • Pricing systems respond to real estate market changes, so your rates stay competitive without undercutting margins.

Over time, these improvements show up in your financial performance:

  • A 6% improvement in NOI on a $20M portfolio = $1.2M/year
  • At a 5% cap rate, that’s $24 million in added portfolio value

What’s more, you can use AI capabilities to build a stronger brand: improve responsiveness, provide personalized services, and get clearer insights. People choose you for a better experience, your team becomes more transparent, and the whole business gets easier to manage.

 

Metric

Pre-AI

Post-AI

Impact

Leads per Year

30,000

30,000

Close Rate

5%

6.5%

+30% improvement

Deals Closed

1,500

1,950

+450 deals

Revenue per Deal

$4,000

$4,000

Annual Deal Revenue

$6,000,000

$7,800,000

+$1,800,000

Admin Time Saved per Agent/Day

0 hrs

2 hrs

+2 hrs/day per agent

Total Annual Hours Saved (50 agents)

26,400 hrs

Avg Internal Cost per Hour

$40

Labor Cost Saved

$1,056,000

Time efficiency gain

Total Year 1 Value Unlocked

$2,856,000

Revenue+efficiency

Estimated AI Implementation Cost

$500,000

Development, infra, support

Net ROI (Year 1)

$2,356,000

~470% ROI

Built with Real-World Complexity in Mind: How We Approach Real Estate AI

Building an AI agent might seem like too big of a leap. And honestly, it can be if you don’t have the right team on board. We’ve spent years breaking down those barriers. Here are a few reasons why working with us feels different:

  • Our ready-made domestic lead qualification models help increase conversion rates by up to 65%.
  • With AI Cursor that speeds up our development, we launch most projects in 2 to 4 weeks, not 6 months like many others.
  • We use reusable code patterns and automation to cut engineering costs by 30%.
  • Everything we create is fully custom and explainable, built to match your stack and industry.
  • We stay post-launch to train your team, fine-tune the model, and support adoption.

Plus, Inoxoft has 10+ years of experience, 170+ specialists, and 230+ delivered projects. With a 5/5 rating on Clutch, our clients know they’re in good hands.

Book a free consultation, and let’s talk about your project.

Real Estate AI: Top Reasons to Choose Inoxoft

Final Thoughts

So, real estate artificial intelligence is still in its early days, with a lot of room for growth and new ideas. While some people are trying to understand how smart agents will change the residential real estate market, adopters have already seen a 10% growth in NOI.

Sure, the decision to invest in an AI agent is worth considering, but keep in mind that it takes careful thought and planning. Make sure you have a solid foundation, reliable data sources, and a team that can adapt when needed. After all, it’s about being patient, precise, and clear about your goals.

We’ve been working with real estate and AI projects for over 10 years. Inoxoft also has strong experience in other regulated industries, a great reputation on Clutch, a team of AI talents, and a portfolio full of successful projects. 

Got an AI integration project in mind? We’d love to become part of your team.

Frequently Asked Questions

Can AI in the real estate industry replace a person?

Although AI will reshape real estate, it can’t fully replace a human agent. Using AI in real estate, you can automate certain tasks (respond to emails, sort leads, or provide property recommendations), but there’s still a lot that needs a personal touch. 

Real estate agents bring their experience, negotiation skills, and local knowledge to the table. AI might make agents' jobs easier by handling repetitive tasks or portfolio management, but the human aspect of building trust with clients and understanding the nuances of the market is something AI can't replace.

What form of AI is most commonly used in real estate?

The most common AI in real estate development includes machine learning, natural language processing (NLP), and chatbots. Machine learning is used to analyze property data and predict trends, such as property values or investment opportunities. NLP powers chatbots that interact with potential clients, answer questions, or schedule meetings. AI is also used in virtual reality tours, where it helps create realistic, immersive experiences for potential buyers.

How to use AI to find real estate leads?

Artificial intelligence for real estate helps find leads by analyzing online behaviors and predicting which people are most likely to be interested in buying or renting properties. Here’s how it works:

1. Lead generation and scoring: AI looks at data like website visits, social media, and past transactions to figure out which leads are more likely to convert into customers.
2. Predictive analytics: AI analyzes past transactions and trends to predict who might be ready to buy or sell soon. For example, if a neighborhood has seen a lot of home sales recently, AI flags people in that area as potential sellers.
3. Automated outreach: AI automates the process of reaching out to leads through email or SMS campaigns, saving time.

How do AI agents transform property searches?

In the past, buyers had to scroll through long lists and multiple websites. Now, AI can filter houses based on specific property features, like the number of bedrooms, energy efficiency, or whether a home has smart systems installed.

For real estate companies, this shift opens new revenue streams. AI tools offer free property matching services, give insights on market trends, or even predict the value of a property over time. These tools are becoming part of the rapidly expanding AI ecosystem, which is making real estate easier to manage and more profitable to work in.

Whether it’s improving property searches, analyzing property characteristics, or building new investment and revenue models, AI is helping people navigate real estate effectively, supporting sustainable real estate practices.