Where Does AI Stand in Real Estate at the Close of 2026?

Why real estate companies jumped in

Adoption wasn't gradual. According to NAR's 2025 Technology Survey, 68% of agents already use some kind of artificial intelligence tool, up from 28% the year before. The shift is visible in Latin America too: proptech and software are the fastest-growing industries for investment in the region, with compound annual growth rates of 200% and 300%, according to a study by Endeavor and Glisco Partners, driven by the sector's digitalization, high market potential, foreign investment, and a fragmented real estate market.

The reasons are operational. The first is response time: a well-configured assistant can answer a prospect at three in the morning, collect their details, and qualify their interest while the team sleeps, cutting response times from several hours to seconds. The second is repetitive work: Morgan Stanley projects $34 billion in efficiency gains for the sector by 2030, with 37% of tasks automatable.

The third is the cost of selling. There are no legally set fees in the region, but custom carries weight. In Mexico, commissions range from 3% to 8%; in Argentina, the standard is 3% plus VAT from each party; in Colombia, 3% for urban properties; in Chile, typically 2% plus VAT from each party; and in Brazil, 6% for urban housing and 3% to 5% for pre-construction projects. For a developer, that percentage is paid on every unit, on top of the cost of sustaining a sales force that grows with each new project.

How it evolved

First came button bots: fixed menus that filtered but didn't understand. Then generative AI: writing listings, descriptions, and more natural replies. Today the conversation is about agents. Unlike generative AI, which creates content in response to instructions, agents plan and act with minimal supervision, running continuous processes.

The most recent step is coordinated systems: several specialized agents that share context and work under business rules, with people supervising.

The four dimensions of the market

1. Sales conversion

This is the most mature. In the United States, Lofty integrates agents that qualify leads, nurture prospects, and book appointments; EliseAI dominates customer service in multifamily rentals. In the region, Alohome automates the sales process for developers through smart funnels and lead qualification, and in October 2025 it was acquired by Colombian company Koggi, which aims to bring mortgage financing, construction, and home buying together into a single ecosystem.

2. Planning and feasibility

Tools like TestFit and Autodesk Forma generate design scenarios and connect them to financial models before the land is purchased. In Latin America, adoption is still in its early days.

3. Valuation and market intelligence

In Europe, PriceHubble leads with explainable valuations. In the region, the challenge is the lack of centralized data; even so, Tuhabi uses algorithms for fast appraisals, and startups like mirando.ia offer brokers property search and comparables-based valuation.

4. Compliance and sustainability

In Europe, platforms like Deepki automate the energy monitoring required by regulation. In the region, the focus is on anti-money laundering checks and e-signatures built into the sales process, with few solutions connected to the sales flow.

Agents: what already exists and what's missing

What already exists

There are mature, accessible tools for each piece:

• Real estate CRMs. Tokko Broker and Kommo publish to listing portals, organize inventory, and manage the pipeline.
• General-purpose conversational agents. Agentify handles WhatsApp, Instagram, and other channels for any industry.
• Tools for brokers. mirando.ia covers search, valuation, and lead generation.
• Vertical funnels. Alohome and similar solutions cover new-home sales.

What's missing

The market solved pieces, not the full journey:

• End-to-end integration. From the ad to the conversation, qualification, assignment, and follow-up, with no manual hand-offs and no data scattered across multiple vendors.
• Control over the sales force. Knowing who has each prospect, whether they're working it, and why a hot opportunity was lost.
• Your own data to make decisions. Without a centralized record of transactions, the information each company generates is worth more than ever: peak demand hours, the areas people search for, the ads that convert.
• Business logic. Inventory, payment plans, sales criteria, and assignment rules that change with every operation and that a generic platform doesn't know.

How to solve it

With architecture before more tools: a single source of truth for inventory, an agent that looks up that information and takes action, business rules that define what the AI decides and what the team decides, and a dashboard that turns every conversation into insight. And with a clear expectation: AI handles the first contact, qualifies, and coordinates; the showing, the negotiation, and the closing are still human.

How we're solving it at MMonter Studio

Our first production implementation is MADI, a system for a real estate company in Mexico with a large sales force and several developments. Before, customer service depended on a single WhatsApp number, and prospects were assigned by hand, with no visibility into what happened next.

Today the journey is seamless: the prospect clicks the ad, WhatsApp opens, and they chat with the agent, which looks up information on the developments, qualifies them according to the business's criteria, and sends documents. The system suggests a sales advisor based on rules defined by the company; in this first stage, management approves each assignment, although the system can assign on its own. From the dashboard, the team can reassign prospects, send notes, see who's handling each conversation, and spot lost opportunities. Every decision the agent makes is recorded.

The dashboard also answers questions that used to have no answer: what time prospects write in and whether more advisors are needed during those hours, which areas they're looking for, which ad brought them in, and how they phrase what they ask for. With that, campaigns get adjusted and lead quality improves. The goal is to run the operation with a team of 7 advisors, down from the current 20; the progress is already visible.

The difference isn't the technology, it's how it's built. This isn't a platform for dragging boxes around and writing prompts. It's a system designed with the client: we step into the operation, understand how they sell today, and build on top of that, from campaigns and content to the Meta integration and the dashboard. Real estate is the first case; the same way of working applies to any sales operation.

At the close of 2026

AI is already in real estate. What's still open is who manages to connect the pieces. Developers in other countries across the region are already looking to cut the cost of their commissions with systems like this, and that's where the opportunity lies.