# Realtor Property Details Cheap (Pay Per Result) (`kawsar/realtor-property-details-cheap`) Actor Realtor.com property details scraper that extracts price, beds, baths, sqft, tax history, and school ratings from any US address or property ID, so investors and agents can pull structured listing data at scale. - **URL**: https://apify.com/kawsar/realtor-property-details-cheap.md - **Developed by:** [Kawsar](https://apify.com/kawsar) (community) - **Categories:** Real estate, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### Realtor Property Details Scraper ![Realtor Property Details Scraper](https://images.bigibyte.com/cdn-cgi/image/width=560,fit=scale-down,format=webp/apify-Kawsar-Realtor-Property-Details-Cheap-Pay-Per-Result.webp) Pull full property details from Realtor.com for any US address or numeric property ID. The actor resolves plain-text addresses automatically, calls the Realtor.com API, and returns structured data: listing price, beds, baths, square footage, year built, status, tax history, school ratings, estimated value, photos, and more. No API key, no browser, no manual lookups. Each result is stored as a flat, table-ready record with 17 individual columns plus the full raw JSON object. Works for active listings, off-market homes, and recently sold properties. The most affordable Realtor.com property data scraper on Apify. Pay only for what you use — no subscriptions, no minimums, no wasted credits. ### What data does this actor extract? Every property comes back as a flat row with key fields broken out as separate columns, so the results open cleanly in Google Sheets, Excel, or any BI tool without any preprocessing. #### Output columns (table view) | Field | Type | Description | |-------|------|-------------| | `searchQuery` | string | The original address or property ID you submitted | | `propertyId` | string | Resolved Realtor.com property ID | | `status` | string | Listing status: `for_sale`, `sold`, `off_market`, `pending`, etc. | | `listPrice` | number | Current listing price in USD | | `lastSoldPrice` | number | Price the property last sold for | | `lastSoldDate` | string | Date of the last sale | | `lastUpdateDate` | string | Date the listing was last updated on Realtor.com | | `beds` | number | Number of bedrooms | | `baths` | number | Number of bathrooms | | `sqft` | number | Interior square footage | | `yearBuilt` | number | Year the property was built | | `propertyType` | string | Property type: `single_family`, `condo`, `townhouse`, etc. | | `address` | string | Street address line | | `city` | string | City | | `state` | string | Two-letter state code | | `postalCode` | string | ZIP code | | `href` | string | Direct Realtor.com listing URL | | `propertyData` | object | Full raw API response with tax history, schools, photos, estimates, mortgage info, and more | #### Example output record ```json { "searchQuery": "742 Evergreen Terrace, Springfield, IL 62704", "propertyId": "9535495300", "status": "for_sale", "listPrice": 485000, "lastSoldPrice": 410000, "lastSoldDate": "2021-06-15", "lastUpdateDate": "2026-02-14", "beds": 4, "baths": 3, "sqft": 2200, "yearBuilt": 1998, "propertyType": "single_family", "address": "742 Evergreen Terrace", "city": "Springfield", "state": "IL", "postalCode": "62704", "href": "https://www.realtor.com/realestateandhomes-detail/742-Evergreen-Terrace_Springfield_IL_62704", "propertyData": { "description": { "lot_sqft": 9200, "garage": 2, "pool": false, "heating": "Forced Air", "cooling": "Central" }, "tax_history": [{ "year": 2023, "tax": 6800 }, { "year": 2022, "tax": 6500 }], "schools": { "schools": [{ "name": "Springfield Elementary", "rating": 8, "distance_in_miles": 0.4 }] }, "estimates": { "current_values": [{ "estimate": 492000 }] }, "photos": [{ "href": "https://ap.rdcpix.com/..." }], "mortgage": { "estimate": { "monthly_payment": 2640 } }, "property_history": [{ "date": "2021-06-15", "event_name": "Sold", "price": 410000 }], "local": { "flood": { "flood_factor_score": 1 }, "noise": { "score": 42 } } } } ```` ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `searchQueries` | array | required | List of US addresses or Realtor.com property IDs. Mix both in the same run. One per line. | | `maxItems` | integer | 0 (unlimited) | Maximum number of properties to process. Set to 0 for no limit. | | `timeoutSecs` | integer | 300 | Total actor run time limit in seconds. | | `requestTimeoutSecs` | integer | 30 | Per-request HTTP timeout in seconds before moving on. | | `proxyConfiguration` | object | Datacenter (United States) | Proxy type and location. Supports Datacenter, Residential, Special, and custom proxies. Optional. | #### Example input ```json { "searchQueries": [ "3291777140", "3808 Warm Meadows St, Las Vegas", "13018 Lumberjack Rd NW Unit 1" ], "maxItems": 0, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } } ``` ### Use cases - **Real estate investment research**: pull list price, last sold price, and estimated value for a watchlist of addresses to spot underpriced properties fast - **Competitive market analysis**: compare beds, baths, sqft, and price per sqft across a neighborhood to understand pricing trends without visiting Realtor.com manually - **Lead enrichment**: append property specs to a CRM export — beds, baths, sqft, year built, and listing status added to every address in your contact list - **Off-market property monitoring**: track addresses that are currently off-market and detect when a new listing goes live - **Portfolio valuation**: batch-process a list of owned properties to pull current estimates, recent price changes, and tax records in one run - **Property data pipelines**: feed structured real estate data into Google Sheets, Airtable, or a database on a schedule using Apify integrations ### How it works 1. For each item in `searchQueries`, the actor checks whether the input is a numeric string (treated as a Realtor.com property ID) or a plain-text address. 2. Plain-text addresses are sent to Realtor.com's geocoder, which returns a numeric property ID. 3. The actor calls the Realtor.com GraphQL API with the property ID and retrieves the full listing record. 4. Key fields are extracted and stored as flat columns. The complete raw API response is saved in `propertyData` for anything deeper. 5. If a property is not found or a request fails, the record is saved with null fields and the run continues — one bad address never stops the whole batch. ### FAQ **Can I mix addresses and property IDs in the same list?** Yes. Numeric strings are used as property IDs directly. Everything else goes through the address resolver. You can mix both freely in a single run. **Does this work for off-market and sold properties?** Yes. The actor pulls data for any property in the Realtor.com database, not just active listings. Off-market homes, recently sold properties, and pending sales all return data. **Do I need a Realtor.com account or API key?** No. The actor uses the same API endpoint the Realtor.com mobile app uses. No credentials required. **What is inside the `propertyData` field?** The full raw API response, including tax history by year, nearby school ratings and distances, flood risk score, noise score, photo URLs with AI-generated tags, mortgage payment estimates, FHA eligibility, price history, HOA fees, open house dates, virtual tours, and neighborhood median stats. **When should I switch to residential proxies?** Datacenter proxies (US) work for most runs. If you start seeing a high rate of null results on large batches, switch to Residential in the proxy settings. Residential proxies are harder to detect and better suited for high-volume scraping. **How many properties can I process per run?** There is no hard limit. Set `maxItems` to 0 to process your entire input list. Each property uses roughly two API requests: one for address resolution and one for the data fetch. **Can I schedule this actor to run automatically?** Yes. Use the Apify scheduler to run the actor on a daily or weekly schedule. Combine it with Google Sheets integration to keep a spreadsheet updated automatically. ### Integrations Connect this Realtor.com property scraper with other tools using [Apify integrations](https://apify.com/integrations). Push results directly to Google Sheets or Google Drive, trigger workflows in Zapier or Make, sync data to Airtable or Airbyte, or post a Slack notification when a batch finishes. You can also use [webhooks](https://docs.apify.com/integrations/webhooks) to pipe results into your own backend the moment each run completes. # Actor input Schema ## `searchQueries` (type: `array`): List of US property addresses or numeric Realtor.com property IDs to look up. You can mix both — plain addresses like '123 Main St, Austin, TX 78701' and numeric IDs like '1234567890'. One item per line. ## `maxItems` (type: `integer`): Maximum number of properties to process per run. Set to 0 for no limit. ## `timeoutSecs` (type: `integer`): Maximum total run time in seconds before the actor stops. Useful for scheduled runs with a time budget. ## `requestTimeoutSecs` (type: `integer`): How long to wait for each individual HTTP request before timing out and moving on. ## `proxyConfiguration` (type: `object`): Select proxies to use for requests. Helps avoid IP blocking and rate limits. Datacenter proxies are fastest; Residential proxies are harder to detect. ## Actor input object example ```json { "searchQueries": [ "3291777140", "3808 Warm Meadows St, Las Vegas", "13018 Lumberjack Rd NW Unit 1" ], "maxItems": 0, "timeoutSecs": 300, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchQueries": [ "3291777140", "3808 Warm Meadows St, Las Vegas", "13018 Lumberjack Rd NW Unit 1" ], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("kawsar/realtor-property-details-cheap").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "searchQueries": [ "3291777140", "3808 Warm Meadows St, Las Vegas", "13018 Lumberjack Rd NW Unit 1", ], "proxyConfiguration": { "useApifyProxy": True, "apifyProxyCountry": "US", }, } # Run the Actor and wait for it to finish run = client.actor("kawsar/realtor-property-details-cheap").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchQueries": [ "3291777140", "3808 Warm Meadows St, Las Vegas", "13018 Lumberjack Rd NW Unit 1" ], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } }' | apify call kawsar/realtor-property-details-cheap --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kawsar/realtor-property-details-cheap", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Realtor Property Details Cheap (Pay Per Result)", "description": "Realtor.com property details scraper that extracts price, beds, baths, sqft, tax history, and school ratings from any US address or property ID, so investors and agents can pull structured listing data at scale.", "version": "0.0", "x-build-id": "2CSZBfbhcAtf7vudy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kawsar~realtor-property-details-cheap/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kawsar-realtor-property-details-cheap", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/kawsar~realtor-property-details-cheap/runs": { "post": { "operationId": "runs-sync-kawsar-realtor-property-details-cheap", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/kawsar~realtor-property-details-cheap/run-sync": { "post": { "operationId": "run-sync-kawsar-realtor-property-details-cheap", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "searchQueries" ], "properties": { "searchQueries": { "title": "Addresses or Property IDs", "type": "array", "description": "List of US property addresses or numeric Realtor.com property IDs to look up. You can mix both — plain addresses like '123 Main St, Austin, TX 78701' and numeric IDs like '1234567890'. One item per line.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Maximum number of properties to process per run. Set to 0 for no limit.", "default": 0 }, "timeoutSecs": { "title": "Overall timeout (seconds)", "minimum": 30, "maximum": 3600, "type": "integer", "description": "Maximum total run time in seconds before the actor stops. Useful for scheduled runs with a time budget.", "default": 300 }, "requestTimeoutSecs": { "title": "Per-request timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "How long to wait for each individual HTTP request before timing out and moving on.", "default": 30 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to use for requests. Helps avoid IP blocking and rate limits. Datacenter proxies are fastest; Residential proxies are harder to detect." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```