Real Estate AEO: How to Get Your Listings and Expertise Found by AI Search

Why AI Is Changing How People Find Real Estate Agents

Real estate is one of the highest-consideration purchases anyone makes. When someone is buying a home — often the largest financial decision of their life — they do not casually scroll through a list of agents and pick one. They research. They compare. They ask for recommendations. And increasingly, they are asking an AI.

The shift is already happening at scale. ChatGPT has over 400 million weekly active users. Perplexity handles millions of searches daily. Google's AI Overviews now appear on a significant percentage of search results pages. And real estate queries are among the fastest-growing categories across all of these platforms. Buyers and sellers are asking questions like:

• "Best real estate agent in Austin for first-time buyers"
• "Top-rated realtor near me who specializes in luxury condos"
• "Real estate agent who knows the Mueller neighborhood in Austin"
• "Which realtor has the most experience selling homes in Lakeway?"
• "Best listing agent for selling a home fast in Denver"
• "Real estate agent for relocation to Nashville — who do locals recommend?"

And AI gives them a direct answer. Not a Zillow directory page with 200 agent headshots. Not a list of ads. A specific, curated recommendation with agent names, transaction history, areas of expertise, client review summaries, and often a direct link to contact the recommended agent. For the buyer, this is a dramatically better experience than traditional search. For the agent who gets recommended, it is the most valuable lead source in modern real estate marketing.

Think about what this means for your business. When someone asks ChatGPT for the best agent in your market and AI recommends three agents by name, those three agents are going to get the call. The other 500 agents in that market? They never even entered the conversation. This is fundamentally different from traditional SEO, where appearing on page one still meant competing with nine other results. In AI search, the recommendation is the result. You are either in the answer or you do not exist.

This is where Answer Engine Optimization (AEO) meets real estate. AEO is the practice of optimizing your online presence so AI search engines recommend you. For real estate professionals, it means structuring your listing data, agent credentials, neighborhood expertise, and technical signals so that when a buyer or seller asks AI for an agent in your market, you are in the answer. This guide gives you the complete playbook.

The window of opportunity is wide open. The vast majority of real estate agents have done zero AEO work. Their websites are template-based IDX sites with no structured data, no neighborhood content, no schema markup, and no strategy for AI visibility. If you implement even half the steps in this guide, you will be years ahead of your competition.

7 Steps to Get Your Real Estate Business Recommended by AI

Step 1: Implement RealEstateListing Schema on Every Listing

RealEstateListing schema is the single most impactful technical change you can make for real estate AEO. This structured data markup tells AI search engines the exact details of every property you represent — price, location, bedrooms, bathrooms, square footage, lot size, listing status, and the agent representing it — all in a machine-readable format that AI models prioritize over unstructured text.

Most real estate websites display listing information in human-readable formats: pretty photos, formatted descriptions, nicely designed detail pages. That is great for human visitors, but AI cannot reliably extract structured data from visual layouts. It needs machine-readable markup that explicitly declares each data point. Without RealEstateListing schema, AI has to guess your listing details from page content — and it frequently gets prices wrong, misidentifies bedroom counts, or skips your listings entirely.

Here is a complete RealEstateListing schema template:

{
  "@context": "https://schema.org",
  "@type": "RealEstateListing",
  "name": "Stunning 4BR Modern Home in Mueller",
  "url": "https://www.youragentsite.com/listings/mueller-modern-home",
  "description": "Beautifully updated 4-bedroom, 3-bathroom modern home in Austin's Mueller neighborhood. Open floor plan, chef's kitchen with quartz countertops, primary suite with walk-in closet, private backyard. Walking distance to Mueller Lake Park, Thinkery children's museum, and H-E-B. Top-rated AISD schools.",
  "datePosted": "2026-02-15",
  "image": [
    "https://www.youragentsite.com/images/mueller-home-1.jpg",
    "https://www.youragentsite.com/images/mueller-home-2.jpg"
  ],
  "offers": {
    "@type": "Offer",
    "price": "625000",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  },
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "1234 Aldrich Street",
    "addressLocality": "Austin",
    "addressRegion": "TX",
    "postalCode": "78723",
    "addressCountry": "US"
  },
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": 30.2985,
    "longitude": -97.7052
  },
  "numberOfRooms": 7,
  "numberOfBedrooms": 4,
  "numberOfBathroomsTotal": 3,
  "floorSize": {
    "@type": "QuantitativeValue",
    "value": 2450,
    "unitCode": "FTK"
  },
  "yearBuilt": 2018,
  "broker": {
    "@type": "RealEstateAgent",
    "name": "Jane Smith",
    "telephone": "+1-512-555-0123",
    "email": "jane@youragentsite.com",
    "url": "https://www.youragentsite.com",
    "areaServed": {
      "@type": "City",
      "name": "Austin",
      "addressRegion": "TX"
    }
  }
}

The key fields AI relies on most heavily are offers (price), address with geocoordinates, numberOfBedrooms, numberOfBathroomsTotal, floorSize, and the broker object linking the listing to you as the agent. Every listing on your website should have this schema. If you use an IDX platform, check whether it supports JSON-LD schema injection — many modern platforms do, or you can add it through a custom plugin or tag manager. You can generate your schema with our free schema generator tool.

Beyond individual listings, implement RealEstateAgent schema on your agent profile page. This tells AI who you are, what areas you serve, your specialties, and your credentials:

{
  "@context": "https://schema.org",
  "@type": "RealEstateAgent",
  "name": "Jane Smith",
  "jobTitle": "Broker Associate, REALTOR",
  "description": "Austin real estate agent specializing in East Austin neighborhoods including Mueller, Windsor Park, and Holly. 12 years of experience, 200+ transactions closed, Certified Negotiation Expert.",
  "url": "https://www.youragentsite.com/about",
  "telephone": "+1-512-555-0123",
  "email": "jane@youragentsite.com",
  "image": "https://www.youragentsite.com/images/jane-headshot.jpg",
  "address": {
    "@type": "PostalAddress",
    "addressLocality": "Austin",
    "addressRegion": "TX",
    "postalCode": "78702",
    "addressCountry": "US"
  },
  "areaServed": [
    { "@type": "City", "name": "Austin" },
    { "@type": "Neighborhood", "name": "Mueller" },
    { "@type": "Neighborhood", "name": "Windsor Park" },
    { "@type": "Neighborhood", "name": "Holly" }
  ],
  "knowsAbout": [
    "First-time home buying",
    "East Austin neighborhoods",
    "New construction",
    "Investment properties"
  ],
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.9",
    "reviewCount": "147",
    "bestRating": "5"
  },
  "memberOf": {
    "@type": "Organization",
    "name": "Austin Board of REALTORS"
  },
  "sameAs": [
    "https://www.zillow.com/profile/janesmith",
    "https://www.realtor.com/realestateagents/jane-smith",
    "https://www.instagram.com/janesmithrealtor"
  ]
}

Step 2: Build Neighborhood Expertise Content

Neighborhood content is your single biggest competitive advantage in real estate AEO. When a buyer asks AI "what is the best neighborhood in Austin for young families" or "tell me about the Mueller neighborhood," AI is looking for detailed, authoritative content from someone who clearly knows the area. Most real estate agent websites have zero neighborhood content — just IDX search pages and a generic "About" blurb. That is a massive opportunity for you.

Create a dedicated neighborhood guide page for every neighborhood and community you serve. Each page should be comprehensive enough to fully answer the questions buyers ask AI:

Core neighborhood data. Median home price, average price per square foot, year-over-year appreciation, median days on market, inventory levels, and price range. Update these numbers quarterly. AI loves specific, current data it can cite with confidence.

School information. School district, individual school names, ratings (GreatSchools, Niche), notable programs, enrollment information. For families with school-age children, this is often the deciding factor — and the question they ask AI first.

Walkability and lifestyle. Walk Score, Transit Score, Bike Score if available. Proximity to parks, trails, restaurants, grocery stores, coffee shops, and entertainment. Commute times to major employment centers. This is the kind of lived-experience detail that AI values because it answers the "what is it actually like to live here" question.

Housing stock description. What types of homes are available? Single-family, townhomes, condos, new construction, historic? What are the typical lot sizes? What architectural styles dominate? What is the age range of homes? This helps AI match buyer preferences to neighborhoods.

Neighborhood character and vibe. Is it quiet and suburban or walkable and urban? What is the demographic mix? Are there community events, farmers markets, neighborhood associations? This subjective but valuable context helps AI make nuanced recommendations.

Pros and honest considerations. Every neighborhood has trade-offs. Being honest about them — "traffic on Lamar can be heavy during rush hour" or "older homes may need foundation work" — actually increases AI trust in your content. AI recognizes balanced, authoritative perspectives over pure marketing spin.

Structure each neighborhood page with clear headings, bullet points, and data tables. Include an FAQ section at the bottom with questions like "Is [neighborhood] a good area for families?" and "What are property taxes like in [neighborhood]?" This directly mirrors how buyers ask AI questions and makes your content the ideal source for AI to cite.

A library of 10 to 20 detailed neighborhood guides instantly positions you as the local expert in AI's eyes. Most of your competitors have zero. This is the single biggest leverage point in real estate AEO.

Step 3: Create Detailed Agent Profiles

Your agent profile page is one of the most important pages on your website for AEO — and most agents treat it as an afterthought. A headshot, a two-sentence bio, and a phone number is not enough for AI to recommend you with confidence. AI needs detailed, verifiable information about your experience, specialties, credentials, and track record.

Build a comprehensive agent profile that includes:

Transaction history and volume. How many transactions have you closed? What is your total sales volume? What is your average sale price? What percentage of your listings sell above asking price? These concrete numbers give AI data points to cite when recommending you.

Area specialization. Which neighborhoods, communities, or property types are your focus? Be specific. Instead of "I serve the Austin metro area," say "I specialize in East Austin neighborhoods including Mueller, Windsor Park, Holly, and Cherrywood, with particular expertise in new construction and mid-century modern homes." Specificity is what triggers AI recommendations for targeted queries.

Credentials and designations. List every relevant credential: REALTOR designation, Certified Negotiation Expert (CNE), Accredited Buyer's Representative (ABR), Seller Representative Specialist (SRS), Graduate REALTOR Institute (GRI), e-PRO, and any local board leadership roles. AI treats verifiable credentials as authority signals.

Client testimonials with context. Do not just list star ratings. Include full testimonials with the client's first name, the type of transaction (first-time buyer, luxury seller, relocation), the neighborhood, and specific outcomes. A testimonial that says "Jane helped us buy our first home in Mueller, negotiated $15K below asking, and closed in 28 days" gives AI far more useful data than "Great agent! Five stars."

Your approach and process. Describe how you work with clients. What does your buyer process look like? How do you market listings? What technology do you use? This kind of transparency is unusual in real estate and stands out to both AI and human visitors.

Make sure this profile page has the RealEstateAgent schema from Step 1 implemented. The combination of rich, detailed content with machine-readable structured data is what makes AI confident enough to recommend you by name.

Step 4: Publish Market Reports and Data Content

Real estate is inherently data-rich, and AI loves data. Monthly or quarterly market reports are one of the highest-value content types for real estate AEO because they demonstrate active market knowledge and give AI current statistics to cite.

Create a recurring market report for each area you serve. Each report should include:

• Median sale price and month-over-month change
• Average days on market
• Active inventory count and months of supply
• Number of homes sold in the period
• Price per square foot trends
• Percentage of homes selling above or below asking price
• New listings added in the period
• Year-over-year comparison for all key metrics

Beyond raw numbers, add your analysis. What do these numbers mean for buyers? For sellers? Is it shifting toward a buyer's or seller's market? Are there seasonal patterns? What should someone expect if they enter the market this month? This interpretive layer is what separates you from a data feed and makes AI treat you as an authoritative source.

When a buyer asks AI "is Austin a good market to buy in right now" or "what are home prices doing in Mueller," your market reports become the data AI references. No one else in your market is creating this kind of consistent, data-rich content — which is exactly why it works so well for AEO.

Structure your market reports with clear headings for each metric, use data tables where possible, and always include the date and time period the data covers. AI needs to assess data freshness, so dated content with clear time references is more valuable than undated pages.

Step 5: Build a Structured Review Strategy

Reviews are one of the strongest signals AI uses when deciding which real estate agents to recommend. AI aggregates reviews from Google Business Profile, Zillow, Realtor.com, Yelp, and your own website. It evaluates overall rating, review volume, recency, sentiment, and the specific details mentioned in reviews.

Implement a systematic review collection process:

Ask at closing. The single most effective time to request a review is at the closing table or within 48 hours of closing. Clients are at peak satisfaction and the experience is fresh. Send a personalized email with direct links to your Google Business Profile and Zillow profile.

Make it specific. Instead of "Would you leave me a review?" ask "Would you share your experience working with me on your Mueller home purchase? It really helps future buyers find me." Specific prompts lead to specific, detailed reviews — which are far more valuable for AI than generic five-star ratings.

Diversify your platforms. AI aggregates reviews across platforms, so having reviews on multiple sites is more powerful than having all your reviews on a single platform. Focus on Google Business Profile (highest weight), Zillow (real estate-specific), Realtor.com, and your own website.

Respond to every review. AI considers review responses as an engagement signal. Thank positive reviewers by name, reference specific details of their transaction, and address any negative reviews professionally and constructively. This shows AI (and future clients) that you are actively engaged with your client base.

Display reviews on your website with Review schema. Add your best reviews to your website and implement Review schema markup. This gives AI a first-party source for your review data that it can cross-reference with third-party platforms. The consistency between your on-site reviews and platform reviews builds confidence in your overall rating.

The goal is not just a high rating — it is a volume of detailed, recent reviews across multiple platforms that give AI abundant data to work with when deciding whether to recommend you.

Step 6: Create Comprehensive FAQ Sections

FAQ content is uniquely powerful for real estate AEO because it directly mirrors how buyers and sellers ask AI questions. When someone asks ChatGPT "how much are closing costs in Texas?" or "what should I look for in a home inspection?" AI is looking for clear, direct answers — and FAQ sections with FAQ schema markup are the ideal format.

Create FAQ sections at three levels:

Site-level FAQ page. A comprehensive FAQ page covering the most common buyer and seller questions in your market: What are closing costs? How does earnest money work? What inspections are recommended? How long does the closing process take? What is the difference between pre-qualification and pre-approval? What happens if the appraisal comes in low?

Neighborhood-level FAQs. Add FAQ sections to each neighborhood guide page with neighborhood-specific questions: What are property taxes in Mueller? Is Windsor Park a good neighborhood for families? What is the HOA fee in Great Hills? Are there flooding concerns in Onion Creek? These hyper-local FAQs are exactly what buyers are asking AI.

Listing-level FAQs. For featured listings or your own listings, add FAQ sections addressing common buyer questions about the property: What school zone is this home in? What are the monthly utility costs? When was the roof last replaced? Is there an HOA? These property-specific FAQs help AI provide detailed responses when buyers ask about specific listings.

Every FAQ section should have FAQPage schema markup. This structured data tells AI exactly what questions you answer and what the answers are, in a format AI can directly extract and cite. Without the schema, AI has to parse your FAQ from the page HTML — with schema, it can read the Q&A pairs instantly.

Write each answer in a way that could stand alone as a complete response. The first sentence should directly answer the question, followed by supporting detail. Aim for 75 to 200 words per answer — long enough to be comprehensive, short enough to be quotable by AI.

Step 7: Add an llms.txt File for AI Crawlers

The llms.txt file is a relatively new standard that acts as a guide specifically for AI crawlers. While robots.txt tells search engine crawlers what they can and cannot access, llms.txt tells AI language models what your site is about, what your most important content is, and how to navigate your site for the most valuable information.

Place an llms.txt file in your website's root directory. For a real estate agent website, it should include:

# Jane Smith Real Estate
> Austin real estate agent specializing in East Austin neighborhoods. 12 years of experience, 200+ transactions closed, Certified Negotiation Expert.

## About
- [Agent Profile](https://www.youragentsite.com/about): Full bio, credentials, transaction history, and client testimonials
- [Areas Served](https://www.youragentsite.com/neighborhoods): Comprehensive guides for Mueller, Windsor Park, Holly, Cherrywood, and 15 more Austin neighborhoods

## Current Listings
- [Active Listings](https://www.youragentsite.com/listings): All current property listings with full details and RealEstateListing schema
- [Recently Sold](https://www.youragentsite.com/sold): Recent closings with sale prices and transaction details

## Market Data
- [Austin Market Reports](https://www.youragentsite.com/market-reports): Monthly market updates with pricing trends, inventory, and analysis
- [Mueller Market Report](https://www.youragentsite.com/market-reports/mueller): Neighborhood-specific data for Mueller
- [Windsor Park Market Report](https://www.youragentsite.com/market-reports/windsor-park): Neighborhood-specific data for Windsor Park

## Buyer & Seller Resources
- [First-Time Buyer Guide](https://www.youragentsite.com/first-time-buyers): Complete guide to buying your first home in Austin
- [Seller Guide](https://www.youragentsite.com/sellers): How to prepare, price, and sell your Austin home
- [FAQ](https://www.youragentsite.com/faq): Answers to common real estate questions

## Neighborhood Guides
- [Mueller](https://www.youragentsite.com/neighborhoods/mueller): Schools, pricing, walkability, housing stock, and lifestyle
- [Windsor Park](https://www.youragentsite.com/neighborhoods/windsor-park): Schools, pricing, walkability, housing stock, and lifestyle
- [Holly](https://www.youragentsite.com/neighborhoods/holly): Schools, pricing, walkability, housing stock, and lifestyle

The llms.txt file gives AI a roadmap to your most valuable content. Without it, AI crawlers have to discover your content through links and sitemaps, which can miss important pages or fail to understand their relative importance. With llms.txt, you are explicitly telling AI: "Here is what I am an expert in, and here is where to find the evidence."

Keep your llms.txt file updated as you add new neighborhood guides, market reports, and listings. AI crawlers check this file regularly, and a current llms.txt signals an actively maintained, authoritative site.

What AI Engines Look for in Real Estate Recommendations

Understanding how AI decides which real estate agents to recommend helps you prioritize your AEO efforts. AI is not randomly picking names from a directory. It is evaluating multiple signals to determine which agents are the most credible, relevant, and trustworthy for a specific query. Here is what matters most:

Specificity and relevance. When a buyer asks about a specific neighborhood, property type, or buyer situation, AI looks for the agent whose content most precisely matches that query. An agent with a detailed Mueller neighborhood guide is far more likely to be recommended for "best agent for Mueller Austin" than an agent whose website only mentions "I serve the greater Austin area." This is why neighborhood content (Step 2) is so critical — it creates the specificity signals AI needs to match you to targeted queries.

Structured data completeness. AI engines prioritize information they can extract with high confidence. RealEstateAgent schema, RealEstateListing schema, FAQ schema, and Review schema all provide machine-readable data that AI can cite without guessing. An agent with complete schema markup across their site gives AI ten times more usable data than one with no schema — and AI rewards that data availability with recommendations.

Review volume, quality, and recency. AI gives significant weight to reviews because they represent third-party validation. It is not just about having a 4.9 rating — volume and recency matter enormously. An agent with 150 reviews and a 4.8 average is more likely to be recommended than one with 8 reviews and a 5.0 average. Recent reviews (last 6 months) signal active practice. Detailed reviews that mention specific neighborhoods, transaction types, and outcomes give AI rich data to cite.

Content depth and topical authority. AI evaluates whether you have genuine expertise or are just a name in a directory. An agent who publishes monthly market reports, has 15 neighborhood guides, writes first-time buyer content, and maintains updated listing pages demonstrates the kind of topical authority AI trusts. The breadth and depth of your content directly correlates with how often AI recommends you.

Cross-platform consistency. AI cross-references your information across your website, Google Business Profile, Zillow, Realtor.com, social media, and review platforms. Consistent information — same name, same phone number, same areas served, same credentials — builds AI confidence. Inconsistencies (different phone numbers across platforms, different specialties listed on different sites) reduce trust and decrease recommendation likelihood.

Transaction evidence. AI looks for verifiable evidence of your activity and success. Sold listings with details, closing announcements, transaction volume claims that are supported by review counts and platform data — these all contribute to an evidence-based picture that AI can confidently present to users. Claims without evidence (like "top agent in Austin" without supporting data) are ignored or deprioritized.

Site health and AI accessibility. Technical factors like page load speed, mobile responsiveness, HTTPS, clean URL structure, and — critically — AI crawler access all affect whether AI can effectively index your content. Many real estate websites inadvertently block AI crawlers through restrictive robots.txt rules or reliance on JavaScript rendering that AI crawlers cannot execute. If AI cannot access your content, it cannot recommend you. Check your robots.txt to ensure you are not blocking GPTBot, ClaudeBot, PerplexityBot, or other AI user agents.

Common Real Estate AEO Mistakes

Real estate has some industry-specific AEO pitfalls that can undermine even well-intentioned optimization efforts. Avoid these common mistakes:

Relying entirely on IDX for listing content. Most real estate websites use IDX feeds that display MLS listing data in a standardized format. The problem is that IDX content is duplicated across every agent website in the MLS — hundreds of sites showing the exact same listing descriptions, photos, and data. AI deprioritizes duplicate content. If your listings are identical to what appears on 500 other agent sites, AI has no reason to cite your version. Add unique value: write your own listing descriptions, add neighborhood context, include virtual tour links, and implement RealEstateListing schema that other agents do not have.

Having no content beyond listings. Many real estate agents treat their website as a listing search portal and nothing else. No neighborhood guides, no market reports, no buyer or seller resources, no FAQ sections, no blog content. This gives AI almost nothing to work with beyond listing data that exists on dozens of other sites. Without original content demonstrating your expertise, AI has no basis to recommend you over any other agent.

Claiming to serve everywhere. "I serve clients throughout the DFW metroplex and surrounding areas" is the AEO equivalent of claiming nothing. AI cannot match you to specific neighborhood queries when your claimed service area is an entire metropolitan region. Instead of trying to appear everywhere, go deep on the neighborhoods where you actually have expertise. Detailed guides for 10 specific neighborhoods will generate far more AI recommendations than a vague claim to serve a region of 7 million people.

Ignoring Google Business Profile. Your Google Business Profile is one of the most important data sources AI uses for local service providers, including real estate agents. An incomplete or outdated GBP — missing photos, wrong hours, no reviews, generic business description — significantly hurts your AI visibility. Optimize your GBP with a detailed description mentioning specific neighborhoods, add photos regularly, keep your information current, and actively collect Google reviews.

Using only photos with no text context. Real estate is a visual industry, and agents love beautiful listing photos. But AI cannot extract meaningful data from images alone. A listing page with 40 photos and a two-sentence description gives AI almost nothing to work with. Every listing page needs detailed text content: property description, neighborhood context, school information, proximity to amenities, and structured data. Photos are for humans; text and schema are for AI.

Blocking AI crawlers without knowing it. Some real estate website platforms have aggressive robots.txt rules that block AI crawlers (GPTBot, ClaudeBot, PerplexityBot) by default. Some CDNs and security plugins also block AI user agents. If AI cannot crawl your website, it cannot recommend you — period. Check your robots.txt and server logs to ensure AI crawlers have access to your content.

Never updating content. Real estate is a dynamic market. Prices change monthly. Inventory fluctuates. Neighborhoods evolve. A neighborhood guide written two years ago with outdated prices and closed restaurants actively hurts your credibility with AI. AI considers content freshness as a quality signal. Update your market reports monthly, refresh neighborhood guides quarterly, and keep your listing data current. Stale content tells AI your site is abandoned.

Skipping schema markup entirely. The majority of real estate agent websites have zero structured data markup. No RealEstateAgent schema, no RealEstateListing schema, no FAQ schema, no Review schema. This forces AI to extract all information from unstructured HTML, which is error-prone and incomplete. Adding schema markup to your site is one of the fastest, highest-impact changes you can make. Learn how in our step-by-step schema markup guide.

Frequently Asked Questions

The Agents Who Start Now Will Dominate Their Markets

AI-powered property search is not a future trend. It is happening right now, at massive scale. Hundreds of millions of people are asking ChatGPT, Claude, Perplexity, and Google AI Overviews to recommend real estate agents and help them find homes — and those AI models are evaluating your website, your reviews, your structured data, and your content to decide whether to recommend you.

The vast majority of real estate agents have done zero AEO work. They have template IDX websites with no schema markup, no neighborhood content, generic agent bios, passive review collection, and no strategy for AI visibility. Their listing content is duplicated across hundreds of other agent sites. Their Google Business Profiles are incomplete. And many are blocking AI crawlers without even realizing it.

Every step you implement from this guide puts distance between you and your competition. Start with the highest-impact changes: implement RealEstateListing and RealEstateAgent schema (Step 1), build detailed neighborhood guides for your core markets (Step 2), and create a comprehensive agent profile with verifiable credentials and testimonials (Step 3). These three steps alone can dramatically increase your AI visibility.

Then build out the remaining steps: publish regular market reports, systematize review collection, add FAQ schema to every relevant page, and create an llms.txt file to guide AI crawlers to your best content. Each step compounds. Together, they create a lead generation channel that grows as AI search grows — and AI search is growing faster than any other channel in real estate marketing.

In real estate, timing is everything. The agents who build their AI presence now — while competitors are still figuring out what AEO even means — will have an insurmountable head start. The best time to start was six months ago. The second best time is today.

Want to see how AI-visible your real estate business is right now? Scan your website free with Vida AEO and get your AI visibility score in under 60 seconds. See exactly which optimizations your site needs and where to start.