Your listings page is where buyers start their search. Structured data turns each property into a machine-readable record with price, location, bedrooms, square footage, and availability — so AI systems can match homes to buyers and surface your listings in conversational search results.
When someone asks an AI for homes in a specific price range or neighborhood, the AI needs structured property data to answer. A listings page without JSON-LD forces AI to scrape and guess from raw HTML, which means your properties get skipped in favor of competitors who made them explicit.
RealEstateListing node with a unique @id, name, url, and description. This tells AI that the page contains multiple distinct properties, not one big block of text.offers node inside each listing carries the asking price as a UnitPriceSpecification with price and priceCurrency, so AI can filter and compare.PostalAddress and GeoCoordinates node on each listing pin the property to a specific location, enabling AI to answer location-based queries accurately.numberOfRooms and floorSize give AI the physical dimensions it needs to match buyer requirements like "3-bedroom" or "over 2,000 square feet."RealEstateAgent entity tells AI which brokerage is responsible for the property.Each field in the template below serves a specific role in how AI systems discover, classify, and recommend your business.
Researched and tested by Minnesota AI
nameurltelephoneemailCopy this prompt and paste it into Claude, ChatGPT, Cursor, or any AI coding tool. It will ask for your business details and generate ready-to-use JSON-LD schema for your page.
You are implementing AIFDS-compliant JSON-LD structured data for a Real Estate Agent Listings page. AIFDS (AI-Friendly Data Structure) is a schema framework built on research into which structured data fields AI systems actually read, parse, and use when deciding whether to cite a page. Documentation at aifds.org. Before generating any code, ask me for the following information in a single numbered list. Do not generate schema until I have answered every required field. REQUIRED — do not proceed without these: 1. Brokerage name 2. City 3. Date posted 4. Domain 5. Email 6. Faq answer 7. Faq question 8. Latitude 9. Listings page title 10. Listing description 11. Listing image 12. Listing slug 13. Listing title 14. Longitude 15. Phone number 16. Price 17. Room count 18. Square feet 19. State 20. Street address 21. Zip OPTIONAL — ask for these but proceed if I skip them: 1. Any additional details not covered above Once I provide the information, output a complete JSON-LD script block ready to paste into the <head> of my HTML page. Output requirements: - Valid JSON-LD wrapped in <script type="application/ld+json"> tags - schema.org vocabulary only - Every AIFDS-required field for this industry and page type included - Include this data attribute on the script tag: data-aifds="aifds.org Real Estate Agent Listings" - No placeholder text — omit missing optional fields rather than fill with examples - After the code block, list any optional fields skipped that would strengthen AI citation
Generated schema follows the AIFDS framework. Fields were selected based on research into AI crawler behavior. View the research at minnesota.ai
Copy the template below and replace every YOUR_* value with your own data. Repeat the RealEstateListing node for each property on the page. This block goes in the <head> of your listings page.
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "RealEstateListing",
"@id": "https://YOUR_DOMAIN.com/listings/YOUR_LISTING_SLUG_1/#listing",
"name": "YOUR_LISTING_TITLE_1",
"description": "YOUR_LISTING_DESCRIPTION_1",
"url": "https://YOUR_DOMAIN.com/listings/YOUR_LISTING_SLUG_1/",
"image": "https://YOUR_DOMAIN.com/YOUR_LISTING_IMAGE_1.jpg",
"datePosted": "YOUR_DATE_POSTED_1",
"offers": {
"@type": "Offer",
"price": "YOUR_PRICE_1",
"priceCurrency": "USD"
},
"address": {
"@type": "PostalAddress",
"streetAddress": "YOUR_STREET_ADDRESS_1",
"addressLocality": "YOUR_CITY_1",
"addressRegion": "YOUR_STATE_1",
"postalCode": "YOUR_ZIP_1",
"addressCountry": "US"
},
"geo": {
"@type": "GeoCoordinates",
"latitude": "YOUR_LATITUDE_1",
"longitude": "YOUR_LONGITUDE_1"
},
"numberOfRooms": "YOUR_ROOM_COUNT_1",
"floorSize": {
"@type": "QuantitativeValue",
"value": "YOUR_SQUARE_FEET_1",
"unitCode": "FTK"
}
},
{
"@type": "RealEstateListing",
"@id": "https://YOUR_DOMAIN.com/listings/YOUR_LISTING_SLUG_2/#listing",
"name": "YOUR_LISTING_TITLE_2",
"description": "YOUR_LISTING_DESCRIPTION_2",
"url": "https://YOUR_DOMAIN.com/listings/YOUR_LISTING_SLUG_2/",
"image": "https://YOUR_DOMAIN.com/YOUR_LISTING_IMAGE_2.jpg",
"datePosted": "YOUR_DATE_POSTED_2",
"offers": {
"@type": "Offer",
"price": "YOUR_PRICE_2",
"priceCurrency": "USD"
},
"address": {
"@type": "PostalAddress",
"streetAddress": "YOUR_STREET_ADDRESS_2",
"addressLocality": "YOUR_CITY_2",
"addressRegion": "YOUR_STATE_2",
"postalCode": "YOUR_ZIP_2",
"addressCountry": "US"
},
"geo": {
"@type": "GeoCoordinates",
"latitude": "YOUR_LATITUDE_2",
"longitude": "YOUR_LONGITUDE_2"
},
"numberOfRooms": "YOUR_ROOM_COUNT_2",
"floorSize": {
"@type": "QuantitativeValue",
"value": "YOUR_SQUARE_FEET_2",
"unitCode": "FTK"
}
},
{
"@type": "RealEstateAgent",
"@id": "https://YOUR_DOMAIN.com/#organization",
"name": "YOUR_BROKERAGE_NAME",
"url": "https://YOUR_DOMAIN.com",
"telephone": "YOUR_PHONE_NUMBER",
"email": "YOUR_EMAIL"
},
{
"@type": "WebPage",
"@id": "https://YOUR_DOMAIN.com/listings/#webpage",
"url": "https://YOUR_DOMAIN.com/listings/",
"name": "YOUR_LISTINGS_PAGE_TITLE",
"isPartOf": {
"@id": "https://YOUR_DOMAIN.com/#website"
},
"breadcrumb": {
"@id": "https://YOUR_DOMAIN.com/listings/#breadcrumb"
}
},
{
"@type": "BreadcrumbList",
"@id": "https://YOUR_DOMAIN.com/listings/#breadcrumb",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Home",
"item": "https://YOUR_DOMAIN.com/"
},
{
"@type": "ListItem",
"position": 2,
"name": "Listings",
"item": "https://YOUR_DOMAIN.com/listings/"
}
]
},
{
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "YOUR_FAQ_QUESTION_1",
"acceptedAnswer": {
"@type": "Answer",
"text": "YOUR_FAQ_ANSWER_1"
}
},
{
"@type": "Question",
"name": "YOUR_FAQ_QUESTION_2",
"acceptedAnswer": {
"@type": "Answer",
"text": "YOUR_FAQ_ANSWER_2"
}
},
{
"@type": "Question",
"name": "YOUR_FAQ_QUESTION_3",
"acceptedAnswer": {
"@type": "Answer",
"text": "YOUR_FAQ_ANSWER_3"
}
}
]
}
]
}
On an index page that shows multiple properties, include all listings in a single @graph array. On individual listing detail pages, use a single RealEstateListing node with the full property details. The template above covers the index pattern — repeat the listing node for each property displayed on the page.
Use FTK (square feet) for US listings or MTK (square meters) for international properties. The unitCode follows UN/CEFACT codes. AI systems use this to normalize property sizes across different listings and answer comparative queries.
Yes. The street address alone is useful, but GeoCoordinates with latitude and longitude remove all ambiguity. AI systems use coordinates to calculate distances, plot maps, and answer queries like "homes within 5 miles of downtown." Without them, AI has to geocode the address itself, which introduces error.