# Realtor.com Scraper with Agents | Enterprise Grade (`fatihtahta/realtor-scraper`) Actor Extract US real estate listings from Realtor.com at scale with rich property detail, agent and office data, deep listing metadata, and market insights. Built for enterprise-grade real estate intelligence, lead enrichment, and automated analytics pipelines. - **URL**: https://apify.com/fatihtahta/realtor-scraper.md - **Developed by:** [Fatih Tahta](https://apify.com/fatihtahta) (community) - **Categories:** Real estate, Automation, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $0.70 / 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.com Scraper **Slug:** `fatihtahta/realtor-com-scraper` ### Overview Realtor.com Scraper collects structured residential real estate listing data, including property identifiers, listing URLs, prices, addresses, room counts, lot and building attributes, listing status, media, agent or office contact details, and source metadata when available. [Realtor.com](https://www.realtor.com) is a major U.S. real estate marketplace, making its public listing data useful for market analysis, inventory monitoring, lead enrichment, and operational reporting. The actor converts location-based searches and supported listing filters into repeatable JSON output that is easier to use in analytics tools, data pipelines, and business applications. It is designed for recurring data acquisition workflows where consistent structure, clear input controls, and predictable dataset delivery matter. Availability and field coverage depend on what Realtor.com publicly exposes at run time. ### Why Use This Actor - **Market research and analytics:** collect structured extraction results for market intelligence, pricing analysis, inventory trend tracking, and geographic comparison. - **Product and content teams:** use normalized listing data to support property experiences, editorial research, content operations, or location-based feature planning. - **Developers and data engineering teams:** feed repeatable collection output into downstream systems, ETL jobs, data warehouses, and enrichment pipelines. - **Lead generation and enrichment teams:** build or enrich prospect datasets with public property, agent, office, and listing attributes. - **Monitoring and competitive tracking teams:** schedule recurring runs to monitor listing changes, availability, price movement, property segments, and operational reporting inputs. ### Common Use Cases - **Market intelligence:** monitor supply, pricing, availability, property characteristics, listing locations, and category movement across U.S. markets. - **Lead generation:** build targeted prospect lists from public property listings, agent records, offices, and listing source details. - **Competitive monitoring:** track property availability, price reductions, builder promotions, luxury inventory, or rental market movement. - **Catalog and directory building:** populate internal databases with structured public real estate records and searchable property attributes. - **Data enrichment:** add current public listing details to CRM, BI, underwriting, investment, or analytics datasets. - **Recurring reporting:** schedule periodic runs for dashboards, alerts, geographic comparisons, or trend analysis. ### Quick Start 1. Add one or more U.S. search locations in `location`, such as a city, neighborhood, ZIP code, state, or regional term. 2. Choose the listing category with `deal_type`: `buy`, `rent`, or `sold`. 3. Set a small `limit` for the first validation run, such as 25 or 50 results per location. 4. Run the actor in Apify Console. 5. Inspect the first dataset records to confirm the fields, coverage, and listing type match your use case. 6. Increase coverage, add filters, enable richer details with `enrich_data`, or schedule the actor after the output is verified. ### Input Parameters The actor accepts location-based Realtor.com searches with optional listing category, property profile, timing, pricing, size, age, amenity, and collection controls. | Parameter | Type | Description | Default | |---|---:|---|---| | `location` | array of strings | Search locations. Add each U.S. city, neighborhood, ZIP code, state, or regional term as a separate item. At least one location is required for normal collection. | – | | `deal_type` | string | Listing category. Allowed values: `buy` for for-sale listings, `rent` for rental listings, `sold` for sold listings. | `buy` | | `property_type` | array of strings | Property categories to include. Allowed values: `house`, `condo`, `townhome`, `multi_family`, `mobile`, `land`, `farm`. Leave empty to include all available property types. | – | | `listing_status` | string | Listing status filter. Allowed values: `active`, `pending_contingent`. Leave empty to avoid narrowing by status. | – | | `home_tours` | array of strings | Tour availability filter. Allowed values: `open_house`, `3d_tour`, `virtual_tour`. | – | | `internal_amenities` | array of strings | Interior amenity filters. Allowed values: `accessibility`, `basement`, `central_air`, `central_heat`, `den_office`, `dining_room`, `elevator`, `energy_efficient`, `family_room`, `fireplace`, `forced_air`, `game_room`, `hardwood_floors`, `in_home_laundry`. | – | | `external_amenties` | array of strings | Outdoor amenity filters. Allowed values: `carport`, `corner_lot`, `cul_de_sac`, `golf_course_lot`, `horse_facility`, `pool`, `rv_boat_parking`, `spa_hot_tub`. | – | | `views` | array of strings | View and waterfront filters. Allowed values: `city_view`, `golf_view`, `hill_view`, `lake_view`, `ocean_view`, `river_view`, `water_front`. | – | | `community` | array of strings | Community amenity filters. Allowed values: `community_55_plus`, `boat_facility`, `clubhouse`, `golf_course`, `horse_facility`, `park`, `pool`, `recreation_facility`, `security_feature`, `spa_hot_tub`, `tennis_court`. | – | | `expand_radius` | string | Search radius around each location, in miles. Allowed values: `1`, `2`, `5`, `10`, `25`, `50`. Leave empty to use the standard location boundary. | – | | `max_hoa_fee` | string | Maximum monthly HOA fee. Allowed values: `no_fee`, `50`, `100`, `200`, `300`, `400`, `500`, `750`, `1000`, `1500`, `2000`, `2500`, `3000`. Values are USD per month. | – | | `building_type` | string | Building format filter. Allowed values: `house`, `row_townhouse`, `apartment`, `duplex`, `triplex`, `fourplex`, `garden_home`, `mobile_home`, `manufactured_home_mobile`, `special_purpose`, `residential_commercial_mix`, `manufactured_home`, `commercial_apartment`, `two_apartment_house`, `park_model_mobile_home`, `floathome`. | – | | `publication_date` | string | Listing freshness window. Allowed values: `24_hours`, `7_days`, `14_days`, `21_days`, `30_days`. | – | | `keyword` | string | Text term or phrase used to focus results on matching listings. | – | | `open_house_only` | boolean | Collect only listings with open house availability in the next 7 days. | `false` | | `live_stream_only` | boolean | Collect only listings with live stream open house availability in the next 7 days. | `false` | | `price_reduced` | boolean | Collect only listings marked as price reduced. | `false` | | `builder_promotions` | boolean | Collect only listings with builder promotions. | `false` | | `min_parking` | string | Minimum garage spaces. Allowed values: `1`, `2`, `3`, representing 1+, 2+, or 3+ spaces. | – | | `storey` | string | Storey layout. Allowed values: `single_story`, `two_or_more_stories`. | – | | `maximize_coverage` | boolean | Collect more matching listings for larger searches where broader coverage is preferred. | `false` | | `enrich_data` | boolean | Collect richer property listing details, such as full descriptions, detailed features, photo galleries, agent details, and listing source metadata when available. | `false` | | `min_bedroom` | string | Minimum bedrooms. Allowed values: `studio`, `1`, `2`, `3`, `4`, `5`. | – | | `max_bedroom` | string | Maximum bedrooms. Allowed values: `studio`, `1`, `2`, `3`, `4`, `5`. | – | | `min_bathroom` | string | Minimum bathrooms. Allowed values: `1`, `2`, `3`, `4`, `5`. | – | | `max_bathroom` | string | Maximum bathrooms. Allowed values: `1`, `2`, `3`, `4`, `5`. | – | | `bedroom_count` | string | Exact or minimum bedroom count. Allowed values: `1+`, `2+`, `3+`, `4+`, `5+`, `1`, `2`, `3`, `4`, `5`. | – | | `bathroom_count` | string | Exact or minimum bathroom count. Allowed values: `1+`, `2+`, `3+`, `4+`, `5+`, `1`, `2`, `3`, `4`, `5`. | – | | `limit` | integer | Maximum listings to save per location. Minimum value: `1`. Leave empty to collect as many matching listings as the run can retrieve. | – | | `min_price` | integer | Minimum asking price in USD. Minimum value: `0`. | – | | `max_price` | integer | Maximum asking price in USD. Minimum value: `0`. | – | | `min_building_area` | string | Minimum building area in square feet. Allowed values: `500`, `750`, `1000`, `1250`, `1500`, `1750`, `2000`, `2250`, `2500`, `2750`. | – | | `max_building_area` | string | Maximum building area in square feet. Allowed values: `2250`, `2500`, `2750`, `3000`, `3250`, `3500`, `3750`, `5000`, `7500`, `10000`. | – | | `min_land_area` | string | Minimum lot size. Allowed values: `2000`, `3000`, `4000`, `5000`, `7500`, `10890`, `21780`, `43560`, `87120`, `217800`, `435600`, `653400`, `871200`, `2178000`, `4356000`. Values are square feet, with common acre equivalents shown in the input UI. | – | | `max_land_area` | string | Maximum lot size. Allowed values: `2000`, `3000`, `4000`, `5000`, `7500`, `10890`, `21780`, `43560`, `87120`, `217800`, `435600`, `653400`, `871200`, `2178000`, `4356000`. Values are square feet, with common acre equivalents shown in the input UI. | – | | `min_building_age` | string | Minimum building age in years. Allowed values: `1`, `3`, `5`, `10`, `15`, `20`, `25`, `30`, `50`, `75`, `100`. | – | | `max_building_age` | string | Maximum building age in years. Allowed values: `1`, `3`, `5`, `10`, `15`, `20`, `25`, `30`, `50`, `75`, `100`. | – | | `min_year_built` | string | Earliest year built. Allowed values: `2026`, `2025`, `2024`, `2023`, `2022`, `2021`, `2020`, `2019`, `2018`, `2017`, `2016`, `2015`, `2010`, `2005`, `2000`, `1990`, `1980`, `1970`, `1960`, `1950`, `1940`, `1930`, `1920`, `1910`, `1900`. | – | | `max_year_built` | string | Latest year built. Allowed values: `2026`, `2025`, `2024`, `2023`, `2022`, `2021`, `2020`, `2019`, `2018`, `2017`, `2016`, `2015`, `2010`, `2005`, `2000`, `1990`, `1980`, `1970`, `1960`, `1950`, `1940`, `1930`, `1920`, `1910`, `1900`. | – | ### Choosing Inputs Start with the smallest input set that represents your target scope: one or more `location` values, a `deal_type`, and a conservative `limit`. Broader locations and fewer filters improve discovery, while narrower filters produce more targeted datasets for a specific segment, price band, property profile, or monitoring workflow. Use `publication_date`, `price_reduced`, `open_house_only`, and `builder_promotions` when freshness or listing events matter. Use `min_price`, `max_price`, room counts, area, lot size, building age, and amenity filters when the output needs to match a clearly defined property profile. Increase `limit`, enable `maximize_coverage`, or turn on `enrich_data` only after validating that the output shape and coverage match your downstream needs. ### Example Inputs #### Example: For-sale listings in one market ```json { "location": ["Austin, TX"], "deal_type": "buy", "property_type": ["house", "townhome"], "min_price": 500000, "max_price": 1200000, "limit": 50 } ```` #### Example: Recent rental monitoring ```json { "location": ["10001", "Brooklyn, NY"], "deal_type": "rent", "publication_date": "7_days", "min_bedroom": "1", "max_price": 4500, "limit": 75 } ``` #### Example: Broad discovery with richer records ```json { "location": ["Dallas, TX"], "deal_type": "buy", "property_type": ["house"], "maximize_coverage": true, "enrich_data": true, "limit": 100 } ``` ### Output #### Output Destination The actor writes results to an Apify dataset as JSON records. The dataset is designed for direct consumption by analytics tools, ETL pipelines, and downstream APIs with minimal post-processing. When multiple entity types or record shapes exist, this README documents each shape separately based on the provided Example Output. The example output provided for this actor contains one property listing record shape. #### Record Envelope And Stable Identifiers Each dataset item is a property listing record. The strongest stable identifier shown in the example is `id`, which matches `property_id`; `listing_id` identifies the specific listing record associated with that property. Recommended idempotency key: use `id` for property-level upserts, or `id` + `listing_id` when your workflow needs to preserve distinct listing instances over time. Use the recommended key for deduplication and upserts when syncing repeated runs into warehouses, CRMs, search indexes, or operational databases. Stable identifiers make records easier to merge, deduplicate, compare, and sync across recurring runs. The example also includes `source_context`, which records the input context and detail URL associated with the collected record. #### Examples ##### Example: property listing record ```json { "id": "9287164194", "property_id": "9287164194", "listing_id": "2978732073", "url": "https://www.realtor.com/realestateandhomes-detail/5619-Walnut-Hill-Ln_Dallas_TX_75229_M92871-64194", "title": "5619 Walnut Hill Ln, Dallas, TX, 75229", "property_type": "single_family", "status": "for_sale", "pricing": { "display_price": "$64,000,000", "list_price": 64000000, "currency": "USD", "last_sold_date": "1998-04-09T00:00:00", "price_per_sqft": 2362, "hoa_fee": 0, "estimates": { "current_values": [ { "estimate": 39777100, "is_best_home_value": true } ] }, "mortgage": { "property_tax_rate": 0.01254008, "closing_cost": 2560000, "closing_cost_rate": "4%", "due_at_close": 15360000, "insurance_rate": 0.0109, "county_fha_loan_limit": 563500, "is_listing_price_eligible_for_fha": false, "is_listing_price_jumbo_loan": true, "average_rates": [ { "rate": 0.06319, "loan": { "id": "thirty_year_fix", "display_name": "30-year fixed", "term": 30, "is_va_loan": false } }, { "rate": 0.06156, "loan": { "id": "twenty_year_fix", "display_name": "20-year fixed", "term": 20, "is_va_loan": false } }, { "rate": 0.05464, "loan": { "id": "fifteen_year_fix", "display_name": "15-year fixed", "term": 15, "is_va_loan": false } }, { "rate": 0.0531, "loan": { "id": "ten_year_fix", "display_name": "10-year fixed", "term": 10, "is_va_loan": false } }, { "rate": 0.05674, "loan": { "id": "thirty_year_fha", "display_name": "30-year fixed FHA", "term": 30, "is_va_loan": false } }, { "rate": 0.05768, "loan": { "id": "thirty_year_va", "display_name": "30-year fixed VA", "term": 30, "is_va_loan": true } }, { "rate": 0.05938, "loan": { "id": "five_one_arm", "display_name": "5-year ARM", "term": 30, "is_va_loan": false } }, { "rate": 0.0608, "loan": { "id": "seven_one_arm", "display_name": "7-year ARM", "term": 30, "is_va_loan": false } } ], "monthly_payment": 442561, "monthly_payment_details": [ { "amount": 317548, "display_name": "Principal & interest", "type": "principal_and_interest" }, { "amount": 58133, "display_name": "Home insurance", "type": "home_insurance" }, { "amount": 0, "display_name": "HOA fees", "type": "hoa_fees" }, { "amount": 0, "display_name": "Mortgage insurance", "type": "mortgage_insurance" }, { "amount": 66880, "display_name": "Property tax", "type": "property_tax" } ], "average_rate": { "rate": 0.06319, "loan_term": 30 } } }, "location": { "address": "5619 Walnut Hill Ln", "city": "Dallas", "state": "Texas", "state_code": "TX", "postal_code": "75229", "country": "USA", "county": "Dallas", "county_fips": "48113", "street_view_url": "https://maps.googleapis.com/maps/api/streetview?channel=rdc-streetview&key=AIzaSyAP_xs30yS0J4xp8O1ZKUrUttIXKklrwaM&location=5619+Walnut+Hill+Ln%2C+Dallas%2C+TX+75229&signature=xsEG1JCUT_pxgvDDiLm9tTUw5G8%3D&size=950x428&source=outdoor", "coordinates": { "latitude": 32.882056, "longitude": -96.810164 }, "street_number": "5619", "street_name": "Walnut Hill", "street_suffix": "Ln", "validation_code": "121", "street_view_metadata_url": "https://maps.googleapis.com/maps/api/streetview/metadata?channel=rdc-streetview&key=AIzaSyAP_xs30yS0J4xp8O1ZKUrUttIXKklrwaM&location=5619+Walnut+Hill+Ln%2C+Dallas%2C+TX+75229&signature=5nhYMy2o16yOCdzdUKoFCOSRadI%3D&size=640x480&source=outdoor", "search_areas": [ { "city": "dallas", "state_code": "tx" } ], "city_metadata": { "slug_id": "Dallas_TX", "county_needed_for_uniq": false }, "neighborhoods": [ "Preston Hollow", "Northwest Dallas" ], "postal_market_badge": "Hot" }, "property": { "beds": 10.0, "baths": 17.0, "baths_consolidated": "12.5+", "sqft": 27092, "lot_sqft": 683326, "year_built": 1938, "garage": 7, "sold_date": "1998-04-09", "description": "A symbol of an era, a setting for the future Dallas most legendary estate is ready for its next chapter. Since 1938, the prestigious Crespi Estate has hosted presidents, dignitaries and icons like Coco Chanel and the Duke and Duchess of Windsor. Designed by Swiss architect Maurice Fatio for Italian count Pio Crespi, this palatial French-style chateau blends intimacy with grandeur across more than 27, 000 square feet. With 10 bedrooms, 12.5 baths, a ballroom, an Art Deco bar, two wine cellars, a conservatory, a library imported from France, a media room and a 3, 000-square-foot primary suite, luxury is everywhere. Enhancements by noted architect Peter Marino include an elegant 3, 300-plus-square-foot guesthouse and a remarkable 4, 800-plus-square-foot entertainment pavilion with a 19-seat theatre. The three adjacent lots are included in the listing, making a total of 15.687 acres. The grounds feature a swimming pool and spa, tennis and bocce ball courts, gardens, greenhouses, a creek and a heliport. A rare opportunity to own a Dallas masterpiece.", "baths_full": 12.0, "baths_half": 5.0, "stories": 3.0 }, "features": { "details": [ { "category": "Bedrooms", "items": [ "Bedrooms: 10" ], "parent_category": "Interior" }, { "category": "Other Rooms", "items": [ "Total Rooms: 15", "Bedroom-Primary: 26.00 x 34.00", "Bedroom-Primary Level: 2", "Bedroom-Primary Features: Custom Closet System, Dual Primary Baths, Ensuite Bath, Fireplace, Garden Tub, Linen Closet, Separate Shower, Separate Vanities, Shower Body Sprays, Sitting Area in Primary, Walk-in Closet(s)", "Bedroom: 17.00 x 22.00", "Bedroom Level: 2", "Bedroom Features: Ensuite Bath, Walk-in Closet(s)", "Bedroom: 17.00 x 20.00", "Bedroom Level: 2", "Bedroom Features: Ensuite Bath, Walk-in Closet(s)", "Bedroom: 20.00 x 22.00", "Bedroom Level: 3", "Bedroom Features: Ensuite Bath, Walk-in Closet(s)", "Utility Room: 16.00 x 14.00", "Utility Room Level: 1", "Utility Room Features: Built-in Cabinets, Dual Sinks, Other, Room for Freezer, Separate Utility Room, Sink in Utility", "Living Room: 20.00 x 33.00", "Living Room Level: 1", "Library: 46.00 x 26.00", "Library Level: 1", "Dining Room: 16.00 x 16.00", "Dining Room Level: 1", "Exercise Room: 25.00 x 19.00", "Exercise Room Level: 3", "Family Room Dimensions: 33.0 x 22.0", "Living Room Dimensions: 26.0 x 46.0", "Media Room Dimensions: 16.0 x 25.0", "Office/Study Room Dimensions: 20.0 x 19.0", "Family Room Level: 1", "Living Room Level: 1", "Media Room Level: B", "Office/Study Room Level: 1" ], "parent_category": "Interior" }, { "category": "Bathrooms", "items": [ "Total Bathrooms: 17", "Full Bathrooms: 12", "1/2 Bathrooms: 5" ], "parent_category": "Interior" }, { "category": "Interior Features", "items": [ "Built-in Features", "Built-in Wine Cooler", "Cathedral Ceiling(s)", "Cedar Closet(s)", "Chandelier", "Decorative Lighting", "Double Vanity", "Eat-in Kitchen", "Elevator", "Flat Screen Wiring", "Granite Counters", "High Speed Internet Available", "Kitchen Island", "Multiple Staircases", "Natural Woodwork", "Open Floorplan", "Paneling", "Pantry", "Smart Home System", "Sound System Wiring", "Vaulted Ceiling(s)", "Wainscoting", "Walk-In Closet(s)", "Wet Bar", "Flooring: Carpet, Hardwood, Marble, Stone, Tile, Wood", "Window Features: Electric Shades, Skylights(s), Window Coverings" ], "parent_category": "Interior" }, { "category": "Appliances", "items": [ "Built-in Refrigerator", "Commercial Grade Range", "Commercial Grade Vent", "Dishwasher", "Disposal", "Electric Water Heater", "Gas Range", "Gas Water Heater", "Ice Maker", "Microwave", "Convection Oven", "Double Oven", "Plumbed For Gas in Kitchen", "Refrigerator", "Vented Exhaust Fan", "Laundry Features: Electric Dryer Hookup, Utility Room, Full Size W/D Area, Washer Hookup" ], "parent_category": "Interior" }, { "category": "Heating and Cooling", "items": [ "Cooling Features: Ceiling Fan(s), Central Air, Gas, Multi Units, Zoned", "Fireplace Features: Bedroom, Dining Room, Family Room, Fire Pit, Gas Logs, Gas Starter, Great Room, Library, Living Room, Masonry, Master Bedroom, Outside, Recreation Room, Stone, Wood Burning", "Heating Features: ENERGY STAR Qualified Equipment, Fireplace(s), Natural Gas, Zoned", "Number of Fireplaces: 11" ], "parent_category": "Interior" }, { "category": "Kitchen and Dining", "items": [ "Dining Room Dimensions: 20.0 x 30.0", "Dining Room Level: 1", "Kitchen Dimensions: 26.0 x 18.0", "Kitchen Features: Breakfast Bar, Built-in Cabinets, Dual Sinks, Eat-in Kitchen, Farm Sink, Kitchen Island, Natural Stone/Granite Type, Pantry, Room for Freezer, Second Sink, Water Line to Refrigerator", "Kitchen Level: 1" ], "parent_category": "Interior" }, { "category": "Exterior and Lot Features", "items": [ "Attached Grill", "Balcony", "Covered Patio/Porch", "Fire Pit", "Garden(s)", "Gas Grill", "Rain Gutters", "Lighting", "Outdoor Grill", "Outdoor Kitchen", "Outdoor Living Center", "Playground", "Private Entrance", "Private Landing Strip", "Sport Court", "Storage", "Tennis Court(s)", "Fencing: Block, Gate, Masonry, Perimeter, Security, Wood", "Other Structures: Greenhouse, Guest House, Outdoor Kitchen, Pool House, Poultry Coop, Separate Entry Quarters, Tennis Court(s)", "Patio And Porch Features: Covered, Front Porch, Patio, Rear Porch, Roof Top Deck/Patio, Side Porch, Terrace" ], "parent_category": "Exterior" }, { "category": "Pool and Spa", "items": [ "Pool Features: Cabana, Diving Board, Fenced, Gunite, Heated, In Ground, Lap, Outdoor Pool, Separate Spa/Hot Tub, Water Feature", "Spa: Yes" ], "parent_category": "Exterior" }, { "category": "Waterfront and Water Access", "items": [ "Creek" ], "parent_category": "Exterior" }, { "category": "Land Info", "items": [ "Acreage", "Landscaped", "Lrg. Backyard Grass", "Many Trees", "Sprinkler System", "Water/Lake View", "Lot Description: 10 to < 50 Acres", "Lot Size Acres: 15.687", "Vegetation: Cleared, Grassed, Wooded", "Lot Size Square Feet: 683326" ], "parent_category": "Exterior" }, { "category": "Garage and Parking", "items": [ "Attached Garage: Yes", "Covered Spaces: 7", "Garage Spaces: 7", "Parking Features: Additional Parking, Circular Driveway, Covered, Drive Through, Electric Gate, Garage, Garage Door Opener, Gated, Gravel, Kitchen Level, Paver Block, Side By Side" ], "parent_category": "Exterior" }, { "category": "Homeowners Association", "items": [ "Association: No", "Calculated Total Monthly Association Fees: 0" ], "parent_category": "Community" }, { "category": "School Information", "items": [ "Elementary School: Pershing", "High School: Hillcrest", "Middle School: Benjamin Franklin", "School District: Dallas ISD" ], "parent_category": "Community" }, { "category": "Other Property Info", "items": [ "Tax Block: 5516", "Source Listing Status: Active", "County: Dallas", "Directions: See GPS. 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All rights reserved." }, "other_listings": { "realtor": [ { "listing_id": "2978732073", "status": "for_sale", "primary": true, "unique": true }, { "listing_id": "2965725191", "status": "off_market" }, { "listing_id": "2955206305", "status": "off_market" } ] } }, "lead": { "show_contact_an_agent": true, "lead_type": "co_broke", "market_type": "pure", "is_veterans_united_eligible": false, "is_schedule_a_tour": false, "show_lead_form": true, "show_text_leads": true, "opcity": { "flip_the_market_enabled": true, "phones": [ { "number": "(214)307-9611", "category": "comm_console" } ] }, "ready_connect_mortgage": { "show_contact_a_lender": false, "show_veterans_united": false } }, "flags": { "is_new_listing": false, "has_specials": false, "has_matterport": false, "is_usda_eligible": false, "suppression": { "has_suppress_sold_price": true, "has_suppress_ai_distribution": true, "has_suppress_sold_price_with_range": true, "has_suppress_foreclosure": true } }, "marketing": { "products": [ "core.agent", "core.broker", "co_broke" ], "brand_name": "essentials", "branding": [ { "type": "Office", "name": "Briggs Freeman Sotheby's International Realty" } ], "rmn_listing_attribution": false, "is_promoted_listing": false }, "metrics": { "is_popular_by_ldp_views": true, "is_popular_by_leads": true, "is_popular_by_saves": true }, "source_context": { "seed_id": "3493b3272f73", "seed_type": "query", "seed_value": "Dallas, TX", "page_index": 1, "enriched": true, "detail_url": "https://www.realtor.com/realestateandhomes-detail/5619-Walnut-Hill-Ln_Dallas_TX_75229_M92871-64194" } } ``` ### Field Reference #### Property Listing Record **id** *(string, required)*: Stable property identifier; recommended property-level idempotency key. **property\_id** *(string, required)*: Property identifier from the listing record. **listing\_id** *(string, optional)*: Listing-specific identifier for the current listing instance. **url** *(string, required)*: Public Realtor.com listing URL. **title** *(string, optional)*: Display title, typically address-based. **property\_type** *(string, optional)*: Normalized property category shown for the listing. **status** *(string, optional)*: Listing status, such as `for_sale`. **pricing.display\_price / pricing.list\_price / pricing.currency** *(string, number, string; optional)*: Human-readable price, numeric price, and currency code. **pricing.last\_sold\_date** *(string, optional)*: Last sold timestamp when available. **pricing.price\_per\_sqft** *(number, optional)*: Price per square foot. **pricing.hoa\_fee** *(number, optional)*: HOA fee value when available. **pricing.estimates.current\_values\[].estimate** *(number, optional)*: Estimated property value. **pricing.estimates.current\_values\[].is\_best\_home\_value** *(boolean, optional)*: Indicates the preferred estimate in the record. **pricing.mortgage.property\_tax\_rate** *(number, optional)*: Property tax rate used in the mortgage estimate. **pricing.mortgage.closing\_cost / pricing.mortgage.closing\_cost\_rate / pricing.mortgage.due\_at\_close** *(number, string, number; optional)*: Closing cost details. **pricing.mortgage.insurance\_rate** *(number, optional)*: Insurance rate used in the estimate. **pricing.mortgage.county\_fha\_loan\_limit** *(number, optional)*: County FHA loan limit. **pricing.mortgage.is\_listing\_price\_eligible\_for\_fha / pricing.mortgage.is\_listing\_price\_jumbo\_loan** *(boolean, optional)*: Loan eligibility indicators. **pricing.mortgage.average\_rates\[].rate** *(number, optional)*: Mortgage rate for a listed loan product. **pricing.mortgage.average\_rates\[].loan.id / display\_name / term / is\_va\_loan** *(string, string, number, boolean; optional)*: Loan product metadata. **pricing.mortgage.monthly\_payment** *(number, optional)*: Estimated monthly payment. **pricing.mortgage.monthly\_payment\_details\[].amount / display\_name / type** *(number, string, string; optional)*: Payment component breakdown. **pricing.mortgage.average\_rate.rate / loan\_term** *(number, number; optional)*: Summary rate and term. **location.address / city / state / state\_code / postal\_code / country** *(string, optional)*: Core address fields. **location.county / county\_fips** *(string, optional)*: County name and FIPS code. **location.street\_view\_url / street\_view\_metadata\_url** *(string, optional)*: Public street-view related media URLs when present. **location.coordinates.latitude / longitude** *(number, optional)*: Geographic coordinates. **location.street\_number / street\_name / street\_suffix** *(string, optional)*: Parsed street components. **location.validation\_code** *(string, optional)*: Address validation code when present. **location.search\_areas\[].city / state\_code** *(string, optional)*: Search area metadata. **location.city\_metadata.slug\_id / county\_needed\_for\_uniq** *(string, boolean; optional)*: City metadata. **location.neighborhoods\[]** *(array of strings, optional)*: Neighborhood names associated with the property. **location.postal\_market\_badge** *(string, optional)*: Postal market indicator when available. **property.beds / baths / baths\_consolidated** *(number, number, string; optional)*: Bedroom and bathroom counts. **property.sqft / lot\_sqft** *(number, optional)*: Building and lot area in square feet. **property.year\_built** *(number, optional)*: Construction year. **property.garage** *(number, optional)*: Garage space count. **property.sold\_date** *(string, optional)*: Last sold date. **property.description** *(string, optional)*: Listing description. **property.baths\_full / baths\_half** *(number, optional)*: Full and half bathroom counts. **property.stories** *(number, optional)*: Number of storeys. **features.details\[].category / parent\_category** *(string, optional)*: Feature group labels. **features.details\[].items\[]** *(array of strings, optional)*: Feature descriptions within each group. **features.tags\[]** *(array of strings, optional)*: Normalized feature tags. **features.display\_tags\[].id / label** *(string, optional)*: User-facing feature tags. **features.ranked\_tags\[].label / rank\_order / tag / tag\_source** *(string, number, string, string; optional)*: Ranked feature tags and their source category. **contacts.advertisers\[].type / fulfillment\_id / name / email / url / mls\_set / nrds\_id** *(string, optional)*: Advertiser or agent identity and contact fields. **contacts.advertisers\[].office.name / fulfillment\_id / email / url / mls\_set / slogan** *(string, optional)*: Office metadata. **contacts.advertisers\[].office.address.line / city / state\_code / postal\_code** *(string, optional)*: Office address fields. **contacts.advertisers\[].office.phones\[].number / type / primary** *(string, string, boolean; optional)*: Office phone entries. **contacts.advertisers\[].office.photo.url** *(string, optional)*: Office photo URL. **contacts.advertisers\[].phones\[].number / type / primary** *(string, string, boolean; optional)*: Advertiser phone entries. **contacts.advertisers\[].broker.fulfillment\_id / name** *(string, optional)*: Broker identity. **contacts.advertisers\[].photo.url** *(string, optional)*: Advertiser photo URL. **contacts.advertisers\[].address.line / city / state\_code / postal\_code / country** *(string, optional)*: Advertiser address fields when present. **contacts.consumer\_advertisers\[].type / advertiser\_id / agent\_id / broker\_id / office\_id / name / phone / url / show\_realtor\_logo** *(string, boolean; optional)*: Consumer-facing advertiser summary. **contacts.consumer\_advertisers\[].address.city / state\_code** *(string, optional)*: Consumer-facing advertiser location. **contacts.consumer\_advertisers\[].photo.url** *(string, optional)*: Consumer-facing advertiser photo URL. **media.photo\_count** *(number, optional)*: Number of photos reported for the listing. **media.images\[].url / is\_primary / type / tags\[]** *(string, boolean, string, array; optional)*: Listing image URL, primary-image flag, image type, and image tags. **media.virtual\_tours\[].url** *(string, optional)*: Virtual tour URL. **media.augmented\_gallery\[].key / category / photo\_count** *(string, string, number; optional)*: Gallery grouping metadata. **media.augmented\_gallery\[].photos\[].url / tag** *(string, string; optional)*: Gallery photo URL and category tag. **media.augmented\_gallery\[].details\[].sub\_category / items\[]** *(string, array; optional)*: Gallery-related detail groups. **media.home\_tours.virtual\_tours\[].url / type / category** *(string, string, string; optional)*: Home tour metadata. **source.permalink / property\_id / id / name / type / source\_listing\_id** *(string, optional)*: Listing source identifiers and labels. **source.list\_date / last\_update\_date** *(string, optional)*: Source list and update timestamps. **source.raw.status** *(string, optional)*: Source status label. **source.agents\[].agent\_id / name / source\_id / office\_id / type** *(string, optional)*: Source-level agent references. **source.disclaimer.text** *(string, optional)*: Source disclaimer text. **source.other\_listings.realtor\[].listing\_id / status / primary / unique** *(string, string, boolean, boolean; optional)*: Related Realtor.com listing references. **lead.show\_contact\_an\_agent / is\_schedule\_a\_tour / show\_lead\_form / show\_text\_leads** *(boolean, optional)*: Lead experience flags. **lead.lead\_type / market\_type** *(string, optional)*: Lead and market classification. **lead.is\_veterans\_united\_eligible** *(boolean, optional)*: Veterans United eligibility flag. **lead.opcity.flip\_the\_market\_enabled** *(boolean, optional)*: Lead routing indicator. **lead.opcity.phones\[].number / category** *(string, string; optional)*: Lead phone contact metadata. **lead.ready\_connect\_mortgage.show\_contact\_a\_lender / show\_veterans\_united** *(boolean, optional)*: Mortgage lead display flags. **flags.is\_new\_listing / has\_specials / has\_matterport / is\_usda\_eligible** *(boolean, optional)*: Listing flags. **flags.suppression.has\_suppress\_sold\_price / has\_suppress\_ai\_distribution / has\_suppress\_sold\_price\_with\_range / has\_suppress\_foreclosure** *(boolean, optional)*: Suppression flags included in the public record. **marketing.products\[]** *(array of strings, optional)*: Marketing product labels associated with the listing. **marketing.brand\_name** *(string, optional)*: Marketing brand label. **marketing.branding\[].type / name** *(string, optional)*: Branding entries. **marketing.rmn\_listing\_attribution / is\_promoted\_listing** *(boolean, optional)*: Marketing attribution and promotion indicators. **metrics.is\_popular\_by\_ldp\_views / is\_popular\_by\_leads / is\_popular\_by\_saves** *(boolean, optional)*: Popularity indicators. **source\_context.seed\_id / seed\_type / seed\_value** *(string, optional)*: Input context that produced the record. **source\_context.page\_index** *(number, optional)*: Result page index associated with the record. **source\_context.enriched** *(boolean, optional)*: Indicates whether richer detail fields were collected for the record. **source\_context.detail\_url** *(string, optional)*: Detail URL associated with the record. ### Data Quality, Guarantees, And Handling - **Structured records:** results are normalized into predictable JSON objects for downstream use. - **Best-effort extraction:** fields may vary by region, session, availability, listing category, and interface changes on Realtor.com. - **Optional fields:** null-check optional fields in downstream code, especially media, mortgage estimates, agent details, source metadata, and rich property features. - **Deduplication:** use `id` for property-level deduplication, or `id` + `listing_id` when storing listing instances separately. - **Freshness:** results reflect the publicly available data at run time. - **Repeated runs:** use the recommended idempotency key when syncing data into warehouses, CRMs, search indexes, or operational reporting systems. ### Tips For Best Results - Start with a small `limit` to validate the output shape before scaling up. - Use one geography or listing category per run when you need cleaner segmentation. - Leave optional filters empty when the goal is broad discovery. - Add filters gradually to understand how each field changes coverage. - Use `publication_date`, `price_reduced`, `open_house_only`, or `builder_promotions` for monitoring workflows. - Enable `enrich_data` when descriptions, detailed features, media, source data, or contact details are important. - Use stable identifiers for deduplication when storing results over time. ### How to Run on Apify 1. Open the Actor in Apify Console. 2. Configure the available input fields for the target scope, starting with `location` and `deal_type`. 3. Set the maximum number of outputs to collect with `limit`. 4. Click **Start** and wait for the run to finish. 5. Open the dataset preview to inspect records. 6. Download results in JSON, CSV, Excel, or another supported format. ### Scheduling & Automation #### Scheduling **Automated Data Collection** Use Apify schedules to run the actor automatically and keep real estate datasets fresh for reporting, monitoring, or enrichment workflows. Recurring runs are useful when you need a consistent view of pricing, availability, listing changes, or market movement over time. - Navigate to **Schedules** in Apify Console - Create a new schedule (daily, weekly, or custom cron) - Configure input parameters - Enable notifications for run completion - Add webhooks for automated processing #### Integration Options - **CRM enrichment:** sync public property, agent, office, and listing attributes into account, lead, or territory records. - **BI dashboards:** monitor pricing, inventory, property types, market coverage, and geographic trends over time. - **Data warehouses:** store recurring JSON records for historical analysis, modeling, and operational reporting. - **Google Sheets or Airtable:** review smaller datasets, validate segments, and coordinate lightweight research workflows. - **Webhooks:** trigger validation, alerts, ingestion, or downstream processing after each completed run. - **Data enrichment pipelines:** join Realtor.com listing attributes with internal CRM, analytics, underwriting, or market datasets. ### Export Formats And Downstream Use Apify datasets can be exported or consumed by downstream systems for operational workflows, reporting, and automation. - **JSON:** for APIs, applications, and data pipelines - **CSV or Excel:** for spreadsheet workflows and manual review - **API access:** for automated ingestion into internal systems - **BI and warehouses:** for reporting, dashboards, and historical analysis ### Performance Estimated execution times: - **Small runs (< 1,000 outputs):** ~3-5 minutes - **Medium runs (1,000-5,000 outputs):** ~5-15 minutes - **Large runs (5,000+ outputs):** ~15-30 minutes Execution time varies based on filters, result volume, and how much information is returned per record. Highly filtered runs can finish faster, while broad discovery or detail-rich records may take longer. ### Limitations - Availability depends on what [Realtor.com](https://www.realtor.com) publicly exposes at run time. - Some optional fields may be missing on sparse listings, regional records, or listing categories with limited public data. - Very broad searches may take longer or require higher limits to capture the desired coverage. - Target-side changes can affect field availability, naming, or the presence of rich details. - Regional, account, or availability differences may change visible results. - Rich media, mortgage estimates, contact information, and source metadata are not guaranteed for every listing. ### Troubleshooting - **No results returned:** check filters, location spelling, listing category, and whether Realtor.com has matching public records for the selected scope. - **Fewer results than expected:** broaden filters, raise `limit`, use a broader location, or verify that enough matching records are publicly available. - **Some fields are empty:** optional fields depend on what each listing publicly provides. - **Run takes longer than expected:** reduce scope, lower `limit` for validation, or split broad collection into smaller location or category segments. - **Output changed:** compare the current output with the field reference and report a small sample if support is needed. ### FAQ #### What data does this actor collect? It collects public Realtor.com property listing records, including identifiers, URLs, prices, location, property attributes, listing status, features, media, agent or office contact details, source metadata, and related flags when available. #### Can I filter by location, category, date, price, or other criteria? Yes. The actor supports location searches, `deal_type`, property type, listing status, publication date, keyword, price range, room counts, building and lot size, building age, year built, amenities, open house availability, builder promotions, and price-reduction filters. #### Why did I receive fewer results than my limit? The `limit` is a maximum, not a guarantee. A run may return fewer records when the selected location, listing category, or filters have fewer matching public listings. #### Can I schedule recurring runs? Yes. Use Apify schedules to run the actor daily, weekly, or with a custom cron expression for monitoring and recurring reporting. #### How do I avoid duplicates across runs? Use `id` for property-level deduplication. If your workflow tracks separate listing instances, use a composite key such as `id` + `listing_id`. #### Can I export the data to CSV, Excel, or JSON? Yes. Apify datasets can be exported in JSON, CSV, Excel, and other supported formats. #### Does this actor collect private data? No. The actor is intended for publicly available Realtor.com listing information. Users are responsible for using collected data lawfully and in accordance with applicable terms and regulations. #### What should I include when reporting an issue? Include the input used, the run ID, expected versus actual behavior, and a small output sample if it helps demonstrate the issue. Redact sensitive internal information before sharing. ### Compliance & Ethics #### Responsible Data Collection This actor collects publicly available real estate listing information from for legitimate business purposes, including: - **Real estate** research and market analysis - **Listing monitoring** and operational reporting - **CRM, BI, and data enrichment** workflows This section is informational and not legal advice. Users are responsible for ensuring their use of the actor and its outputs complies with applicable laws, regulations, contractual obligations, and platform terms. #### Best Practices - Use collected data in accordance with applicable laws, regulations, and the target site’s terms - Respect individual privacy and personal information - Use data responsibly and avoid disruptive or excessive collection - Do not use this actor for spamming, harassment, or other harmful purposes - Follow relevant data protection requirements where applicable, including GDPR and CCPA ### Support For help, use the Actor page or Issues section associated with this actor. Include the input used (redacted if needed), the run ID, expected versus actual behavior, and a small output sample when it helps clarify the issue. Avoid sharing secrets, private credentials, or sensitive internal data in support requests. # Actor input Schema ## `deal_type` (type: `string`): Choose whether to collect properties for sale, properties for rent, or sold properties. Keep the default when your workflow is focused on for-sale inventory. ## `location` (type: `array`): Enter each location as a separate item, for example "San Francisco", "10001", "New York", or "Austin". Broader locations can discover more listings; specific locations keep the dataset more targeted. ## `property_type` (type: `array`): Include only the selected Realtor.com property categories. Leave empty to keep all available property types in scope. ## `listing_status` (type: `string`): Include only active listings or only pending and contingent listings. Leave empty when listing status should not narrow the dataset. ## `home_tours` (type: `array`): Include only listings that offer selected tour options such as open houses, 3D tours, or virtual tours. Leave empty when tour availability is not required. ## `internal_amenities` (type: `array`): Include only listings with selected interior features. Leave empty when interior amenities should not narrow the dataset. ## `external_amenties` (type: `array`): Include only listings with selected exterior or outdoor features. Leave empty when outdoor amenities should not narrow the dataset. ## `views` (type: `array`): Include only listings with selected view or waterfront features. Leave empty when views should not narrow the dataset. ## `community` (type: `array`): Include only listings with selected neighborhood or community features. Leave empty when community amenities should not narrow the dataset. ## `expand_radius` (type: `string`): Expand the search around each selected location by the chosen number of miles. Leave empty to use Realtor.com's standard location boundary. ## `max_hoa_fee` (type: `string`): Include only listings with no HOA fee or with a monthly HOA fee at or below the selected amount. Leave empty when HOA fees should not narrow the dataset. ## `building_type` (type: `string`): Include only a specific building format, such as house, apartment, duplex, or row/townhouse. Leave empty when any building type is acceptable. ## `publication_date` (type: `string`): Include only listings published within the selected recent time window. Leave empty to include listings regardless of publication date. ## `keyword` (type: `string`): Enter a term or phrase to focus results on listings that match your text criteria. Leave empty when no keyword filter is needed. ## `open_house_only` (type: `boolean`): Collect only listings that show open house availability in the next 7 days. Keep this off when you want a broader result set. ## `live_stream_only` (type: `boolean`): Collect only listings that show live stream open house availability in the next 7 days. Keep this off for a broader result set. ## `price_reduced` (type: `boolean`): Collect only listings marked as price reduced. Keep this off when price reductions should not narrow the dataset. ## `builder_promotions` (type: `boolean`): Collect only listings with builder promotions. Keep this off when builder promotions should not narrow the dataset. ## `min_parking` (type: `string`): Include only listings with at least this many garage spaces. Leave empty when garage parking should not narrow the dataset. ## `storey` (type: `string`): Collect only single-storey listings or only listings with two or more storeys. Leave empty when storey layout should not narrow the dataset. ## `min_bedroom` (type: `string`): Include only listings with at least this many bedrooms. Choose studio together with maximum bedrooms set to studio when you only want studio listings. ## `max_bedroom` (type: `string`): Include only listings with at most this many bedrooms. Leave empty when there is no maximum bedroom requirement. ## `min_bathroom` (type: `string`): Include only listings with at least this many bathrooms. Leave empty when there is no minimum bathroom requirement. ## `max_bathroom` (type: `string`): Include only listings with at most this many bathrooms. Leave empty when there is no maximum bathroom requirement. ## `bedroom_count` (type: `string`): Select either a minimum bedroom count, such as 3+, or an exact bedroom count, such as 3. Leave empty when bedroom count should not narrow the dataset. ## `bathroom_count` (type: `string`): Select either a minimum bathroom count, such as 2+, or an exact bathroom count, such as 2. Leave empty when bathroom count should not narrow the dataset. ## `min_price` (type: `integer`): Include only listings with an asking price at or above this amount. Leave empty when there is no minimum price. ## `max_price` (type: `integer`): Include only listings with an asking price at or below this amount. Leave empty when there is no maximum price. ## `min_building_area` (type: `string`): Include only listings with a building area at or above this many square feet. Leave empty when there is no minimum building size. ## `max_building_area` (type: `string`): Include only listings with a building area at or below this many square feet. Leave empty when there is no maximum building size. ## `min_land_area` (type: `string`): Include only listings with a lot size at or above this value. Leave empty when there is no minimum land size. ## `max_land_area` (type: `string`): Include only listings with a lot size at or below this value. Leave empty when there is no maximum land size. ## `min_building_age` (type: `string`): Include only listings with a building age at or above this many years. Leave empty when there is no minimum building age. ## `max_building_age` (type: `string`): Include only listings with a building age at or below this many years. Leave empty when there is no maximum building age. ## `min_year_built` (type: `string`): Include only listings built in or after this year. Leave empty when there is no earliest year requirement. ## `max_year_built` (type: `string`): Include only listings built in or before this year. Leave empty when there is no latest year requirement. ## `enrich_data` (type: `boolean`): Enable this for more complete property listing records. This may make the run slower because the actor may spend more time collecting details for each result. Turn it off for faster runs when the standard result fields are enough. ## `limit` (type: `integer`): Enter the maximum number of listings to save for each location. Leave empty to collect as many matching listings as the run can retrieve. ## Actor input object example ```json { "deal_type": "buy", "open_house_only": false, "live_stream_only": false, "price_reduced": false, "builder_promotions": false, "enrich_data": false, "limit": 100 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "limit": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("fatihtahta/realtor-scraper").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 = { "limit": 100 } # Run the Actor and wait for it to finish run = client.actor("fatihtahta/realtor-scraper").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 '{ "limit": 100 }' | apify call fatihtahta/realtor-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fatihtahta/realtor-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Realtor.com Scraper with Agents | Enterprise Grade", "description": "Extract US real estate listings from Realtor.com at scale with rich property detail, agent and office data, deep listing metadata, and market insights. Built for enterprise-grade real estate intelligence, lead enrichment, and automated analytics pipelines.", "version": "0.0", "x-build-id": "O0geIFdxKqXg8wTD7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fatihtahta~realtor-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fatihtahta-realtor-scraper", "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/fatihtahta~realtor-scraper/runs": { "post": { "operationId": "runs-sync-fatihtahta-realtor-scraper", "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/fatihtahta~realtor-scraper/run-sync": { "post": { "operationId": "run-sync-fatihtahta-realtor-scraper", "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": [ "location" ], "properties": { "deal_type": { "title": "Choose Deal Type", "enum": [ "buy", "rent", "sold" ], "type": "string", "description": "Choose whether to collect properties for sale, properties for rent, or sold properties. Keep the default when your workflow is focused on for-sale inventory.", "default": "buy" }, "location": { "title": "Choose a Location (eg. \"San Francisco\", \"10001\", \"New York\")", "minItems": 1, "type": "array", "description": "Enter each location as a separate item, for example \"San Francisco\", \"10001\", \"New York\", or \"Austin\". Broader locations can discover more listings; specific locations keep the dataset more targeted.", "items": { "type": "string" } }, "property_type": { "title": "Filter by Property Type", "uniqueItems": true, "type": "array", "description": "Include only the selected Realtor.com property categories. Leave empty to keep all available property types in scope.", "items": { "type": "string", "enum": [ "apartment", "house", "condo", "townhome", "multi_family", "mobile", "land", "farm" ], "enumTitles": [ "Apartment", "House", "Condo", "Townhome", "Multi-family", "Mobile", "Land", "Farm" ] } }, "listing_status": { "title": "Filter by Listing Status", "enum": [ "active", "pending_contingent" ], "type": "string", "description": "Include only active listings or only pending and contingent listings. Leave empty when listing status should not narrow the dataset." }, "home_tours": { "title": "Filter by Home Tour Options", "uniqueItems": true, "type": "array", "description": "Include only listings that offer selected tour options such as open houses, 3D tours, or virtual tours. Leave empty when tour availability is not required.", "items": { "type": "string", "enum": [ "open_house", "3d_tour", "virtual_tour" ], "enumTitles": [ "Open House", "3D Tour", "Virtual Tour" ] } }, "internal_amenities": { "title": "Filter by Interior Amenities", "uniqueItems": true, "type": "array", "description": "Include only listings with selected interior features. Leave empty when interior amenities should not narrow the dataset.", "items": { "type": "string", "enum": [ "accessibility", "basement", "central_air", "central_heat", "den_office", "dining_room", "elevator", "energy_efficient", "family_room", "fireplace", "forced_air", "game_room", "hardwood_floors", "in_home_laundry" ], "enumTitles": [ "Accessibility", "Basement", "Central air", "Central heat", "Den / office", "Dining room", "Elevator", "Energy efficient", "Family room", "Fireplace", "Forced air", "Game room", "Hardwood floors", "In-home laundry" ] } }, "external_amenties": { "title": "Filter by Outdoor Amenities", "uniqueItems": true, "type": "array", "description": "Include only listings with selected exterior or outdoor features. Leave empty when outdoor amenities should not narrow the dataset.", "items": { "type": "string", "enum": [ "carport", "corner_lot", "cul_de_sac", "golf_course_lot", "horse_facility", "pool", "rv_boat_parking", "spa_hot_tub" ], "enumTitles": [ "Carport", "Corner lot", "Cul-de-sac", "Golf course lot", "Horse facility", "Pool", "RV / boat parking", "Spa / hot tub" ] } }, "views": { "title": "Filter by Views and Waterfront", "uniqueItems": true, "type": "array", "description": "Include only listings with selected view or waterfront features. Leave empty when views should not narrow the dataset.", "items": { "type": "string", "enum": [ "city_view", "golf_view", "hill_view", "lake_view", "ocean_view", "river_view", "water_front" ], "enumTitles": [ "City view", "Golf course view", "Hill / mountain view", "Lake view", "Ocean view", "River view", "Waterfront" ] } }, "community": { "title": "Filter by Community Amenities", "uniqueItems": true, "type": "array", "description": "Include only listings with selected neighborhood or community features. Leave empty when community amenities should not narrow the dataset.", "items": { "type": "string", "enum": [ "community_55_plus", "boat_facility", "clubhouse", "golf_course", "horse_facility", "park", "pool", "recreation_facility", "security_feature", "spa_hot_tub", "tennis_court" ], "enumTitles": [ "55+ community", "Boat facility", "Clubhouse", "Golf course", "Horse facility", "Park", "Pool", "Recreation facility", "Security feature", "Spa / hot tub", "Tennis court" ] } }, "expand_radius": { "title": "Set Search Radius", "enum": [ "1", "2", "5", "10", "25", "50" ], "type": "string", "description": "Expand the search around each selected location by the chosen number of miles. Leave empty to use Realtor.com's standard location boundary." }, "max_hoa_fee": { "title": "Set Maximum Monthly HOA Fee", "enum": [ "no_fee", "50", "100", "200", "300", "400", "500", "750", "1000", "1500", "2000", "2500", "3000" ], "type": "string", "description": "Include only listings with no HOA fee or with a monthly HOA fee at or below the selected amount. Leave empty when HOA fees should not narrow the dataset." }, "building_type": { "title": "Filter by Building Type", "enum": [ "house", "row_townhouse", "apartment", "duplex", "triplex", "fourplex", "garden_home", "mobile_home", "manufactured_home_mobile", "special_purpose", "residential_commercial_mix", "manufactured_home", "commercial_apartment", "two_apartment_house", "park_model_mobile_home", "floathome" ], "type": "string", "description": "Include only a specific building format, such as house, apartment, duplex, or row/townhouse. Leave empty when any building type is acceptable." }, "publication_date": { "title": "Filter by Publication Date", "enum": [ "24_hours", "7_days", "14_days", "21_days", "30_days" ], "type": "string", "description": "Include only listings published within the selected recent time window. Leave empty to include listings regardless of publication date." }, "keyword": { "title": "Filter by Keyword", "type": "string", "description": "Enter a term or phrase to focus results on listings that match your text criteria. Leave empty when no keyword filter is needed." }, "open_house_only": { "title": "Include Open House Listings Only", "type": "boolean", "description": "Collect only listings that show open house availability in the next 7 days. Keep this off when you want a broader result set.", "default": false }, "live_stream_only": { "title": "Include Live Stream Showings Only", "type": "boolean", "description": "Collect only listings that show live stream open house availability in the next 7 days. Keep this off for a broader result set.", "default": false }, "price_reduced": { "title": "Include Price-Reduced Listings Only", "type": "boolean", "description": "Collect only listings marked as price reduced. Keep this off when price reductions should not narrow the dataset.", "default": false }, "builder_promotions": { "title": "Include Builder Promotion Listings Only", "type": "boolean", "description": "Collect only listings with builder promotions. Keep this off when builder promotions should not narrow the dataset.", "default": false }, "min_parking": { "title": "Set Minimum Garage Spaces", "enum": [ "1", "2", "3" ], "type": "string", "description": "Include only listings with at least this many garage spaces. Leave empty when garage parking should not narrow the dataset." }, "storey": { "title": "Choose Storey Layout", "enum": [ "single_story", "two_or_more_stories" ], "type": "string", "description": "Collect only single-storey listings or only listings with two or more storeys. Leave empty when storey layout should not narrow the dataset." }, "min_bedroom": { "title": "Set Minimum Bedrooms", "enum": [ "studio", "1", "2", "3", "4", "5" ], "type": "string", "description": "Include only listings with at least this many bedrooms. Choose studio together with maximum bedrooms set to studio when you only want studio listings." }, "max_bedroom": { "title": "Set Maximum Bedrooms", "enum": [ "studio", "1", "2", "3", "4", "5" ], "type": "string", "description": "Include only listings with at most this many bedrooms. Leave empty when there is no maximum bedroom requirement." }, "min_bathroom": { "title": "Set Minimum Bathrooms", "enum": [ "1", "2", "3", "4", "5" ], "type": "string", "description": "Include only listings with at least this many bathrooms. Leave empty when there is no minimum bathroom requirement." }, "max_bathroom": { "title": "Set Maximum Bathrooms", "enum": [ "1", "2", "3", "4", "5" ], "type": "string", "description": "Include only listings with at most this many bathrooms. Leave empty when there is no maximum bathroom requirement." }, "bedroom_count": { "title": "Choose Exact or Minimum Bedrooms", "enum": [ "1+", "2+", "3+", "4+", "5+", "1", "2", "3", "4", "5" ], "type": "string", "description": "Select either a minimum bedroom count, such as 3+, or an exact bedroom count, such as 3. Leave empty when bedroom count should not narrow the dataset." }, "bathroom_count": { "title": "Choose Exact or Minimum Bathrooms", "enum": [ "1+", "2+", "3+", "4+", "5+", "1", "2", "3", "4", "5" ], "type": "string", "description": "Select either a minimum bathroom count, such as 2+, or an exact bathroom count, such as 2. Leave empty when bathroom count should not narrow the dataset." }, "min_price": { "title": "Set Minimum Price", "minimum": 0, "type": "integer", "description": "Include only listings with an asking price at or above this amount. Leave empty when there is no minimum price." }, "max_price": { "title": "Set Maximum Price", "minimum": 0, "type": "integer", "description": "Include only listings with an asking price at or below this amount. Leave empty when there is no maximum price." }, "min_building_area": { "title": "Set Minimum Building Area", "enum": [ "500", "750", "1000", "1250", "1500", "1750", "2000", "2250", "2500", "2750" ], "type": "string", "description": "Include only listings with a building area at or above this many square feet. Leave empty when there is no minimum building size." }, "max_building_area": { "title": "Set Maximum Building Area", "enum": [ "2250", "2500", "2750", "3000", "3250", "3500", "3750", "5000", "7500", "10000" ], "type": "string", "description": "Include only listings with a building area at or below this many square feet. Leave empty when there is no maximum building size." }, "min_land_area": { "title": "Set Minimum Land Area", "enum": [ "2000", "3000", "4000", "5000", "7500", "10890", "21780", "43560", "87120", "217800", "435600", "653400", "871200", "2178000", "4356000" ], "type": "string", "description": "Include only listings with a lot size at or above this value. Leave empty when there is no minimum land size." }, "max_land_area": { "title": "Set Maximum Land Area", "enum": [ "2000", "3000", "4000", "5000", "7500", "10890", "21780", "43560", "87120", "217800", "435600", "653400", "871200", "2178000", "4356000" ], "type": "string", "description": "Include only listings with a lot size at or below this value. Leave empty when there is no maximum land size." }, "min_building_age": { "title": "Set Minimum Building Age", "enum": [ "1", "3", "5", "10", "15", "20", "25", "30", "50", "75", "100" ], "type": "string", "description": "Include only listings with a building age at or above this many years. Leave empty when there is no minimum building age." }, "max_building_age": { "title": "Set Maximum Building Age", "enum": [ "1", "3", "5", "10", "15", "20", "25", "30", "50", "75", "100" ], "type": "string", "description": "Include only listings with a building age at or below this many years. Leave empty when there is no maximum building age." }, "min_year_built": { "title": "Set Earliest Year Built", "enum": [ "2026", "2025", "2024", "2023", "2022", "2021", "2020", "2019", "2018", "2017", "2016", "2015", "2010", "2005", "2000", "1990", "1980", "1970", "1960", "1950", "1940", "1930", "1920", "1910", "1900" ], "type": "string", "description": "Include only listings built in or after this year. Leave empty when there is no earliest year requirement." }, "max_year_built": { "title": "Set Latest Year Built", "enum": [ "2026", "2025", "2024", "2023", "2022", "2021", "2020", "2019", "2018", "2017", "2016", "2015", "2010", "2005", "2000", "1990", "1980", "1970", "1960", "1950", "1940", "1930", "1920", "1910", "1900" ], "type": "string", "description": "Include only listings built in or before this year. Leave empty when there is no latest year requirement." }, "enrich_data": { "title": "Collect Richer Property Listing Details", "type": "boolean", "description": "Enable this for more complete property listing records. This may make the run slower because the actor may spend more time collecting details for each result. Turn it off for faster runs when the standard result fields are enough.", "default": false }, "limit": { "title": "Set Maximum Results per Location", "minimum": 1, "type": "integer", "description": "Enter the maximum number of listings to save for each location. Leave empty to collect as many matching listings as the run can retrieve." } } }, "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 } } } } } } } } } } ```