# 99.co Scraper (`one-api/99co-scraper`) Actor Scrape 99.co Singapore property listings. Search by location, coordinates, polygon, or 99.co URL; pull enriched property details (similar listings, nearby amenities, floor plans, project analysis) by listing id or URL; and look up agents from the public directory. - **URL**: https://apify.com/one-api/99co-scraper.md - **Developed by:** [ONE API](https://apify.com/one-api) (community) - **Categories:** Lead generation, Real estate - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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. 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 ## 99.co Scraper Scrape **99.co Singapore** property listings — residential and commercial. Search by location, coordinates, polygon, or 99.co URL; pull **enriched property details** (similar listings, nearby amenities, floor plans, project analysis) by listing id or URL; and look up **agents** from the public directory. ### What it does - **4 search modes** — auto-detected per line: - Location name (MRT station, neighbourhood, district, project — e.g. `Orchard`, `Tampines`) - Coordinates `lat,lng` or `lat,lng,radius_m` (e.g. `1.3042,103.8324,1500`) - Polygon — 3+ space-separated `lat,lng` pairs (e.g. `1.30,103.82 1.32,103.82 1.32,103.85`) - A `https://www.99.co/...` search URL - **Property Details** — listing id (e.g. `EVPssE5S3KHLKudeJdhwT4`) or 99.co listing URL. Each record is **enriched** with `similarListings`, `nearbyAmenities`, `floorPlans`, and `projectAnalysis` (nearest MRT / schools / supermarkets, facilities) — all kept in the `Raw` column. Toggle `enrich` off for a leaner, faster call. - **Agents** — keyword search of the public agent directory (use `*` to browse all), plus full agent profiles by slug or profile URL (contact info, agency, listings breakdown). - **Location Autocomplete** — resolve free text into 99.co location ids, types, and coordinates (MRT stations, districts, projects, schools). ### Filters Listing type (sale / rent), segment (residential / commercial), property type (HDB / condo / landed / retail / office / industrial / land), price range, bedrooms, bathrooms, floor area — plus an advanced free-form filter field for tenure, furnishing, floor level, property status (resale / new launch / subsale), building age, keywords, and more. Filters apply to every search line. ### Output - **One flat dataset row per result** — sortable, filterable, CSV-exportable. - Columns: `Mode`, `Input Given`, `ID`, `Title`, `Type`, `Price`, `PSF`, `Bedrooms`, `Bathrooms`, `Floor Area (sqft)`, `Address`, `District`, `Tenure`, `Built Year`, `Status`, `Posted`, `Agent Name`, `Agent Phone`, `Agency`, `Photo`, `URL`. - Full upstream JSON kept in the `Raw` column for power users (including all enrichment blocks on property details). ### Inputs - Each section is independent — fill only the ones you need. - Multiple inputs per section: one per line. - `Pages per search` × `Results per page` (max 60) control how many listings each search line returns; pagination stops early when 99.co runs out of results. ### Pricing - Pay per result: **$3 per 1,000 dataset items** — same rate on every Apify plan tier. - Apify start fee: $0.00005 per run. ### Notes - **Singapore only.** 99.co Indonesia runs on a separate platform and is not covered. - Prices are in **SGD**; areas in sqft. - Agent search returns each agent's `slug` — feed it into the *Agent slug or profile URL* field for the full profile. - Sorting: 99.co's API supports distance-based sorting only (`sortOrder=distance` via the advanced filter field); default order is relevance. ### Errors - Failures land in the dataset as rows with `Mode = "ERROR"` and the upstream error message in the `Status` column — nothing is silently dropped. # Actor input Schema ## `search_inputs` (type: `array`): Auto-detects each entry and routes to the right `/search/*` endpoint: • `https://www.99.co/...` search URL → `/search/byurl` • `lat,lng` or `lat,lng,radius_m` (e.g. `1.3042,103.8324,1500`) → `/search/bycoordinates` • 3+ space-separated `lat,lng` pairs (e.g. `1.30,103.82 1.32,103.82 1.32,103.85`) → `/search/bypolygon` • Anything else — MRT station, neighbourhood, district, project name (e.g. `Orchard`, `Tampines`) → `/search/bylocation` Filters below apply to every search call. Each hit returns one dataset row per listing. ## `searchType` (type: `string`): Sale or rental listings. ## `propertySegment` (type: `string`): Residential (HDB / condo / landed) or commercial (retail / office / industrial / land). ## `propertyType` (type: `string`): Main category. `hdb` / `condo` / `landed` need segment = Residential; `retail` / `office` / `industrial` / `land` need segment = Commercial. ## `priceRange` (type: `string`): Format: `min:500000,max:3000000` — or just `min:500000` / `max:3000000`. ## `bedrooms` (type: `string`): Comma-separated counts, e.g. `2,3` for 2- and 3-bedroom listings. ## `bathrooms` (type: `string`): Comma-separated counts, e.g. `2`. ## `floorAreaRange` (type: `string`): Format: `min:600,max:2000`. ## `moreFilters` (type: `string`): Optional query-string of extra filters merged into every search, e.g. `furnishing=fully&tenure=freehold&floorLevel=high&propertyStatus=resale`. Supported keys include: `propertyStatus` (resale/new_launch/subsale), `tenure` (freehold/leasehold), `furnishing` (fully/partial/unfurnished), `floorLevel` (ground/low/mid/high/top/penthouse), `rentalType` (unit/room), `roomType` (master/common), `buildingAge` (5/10/20), `keywords`, `hasFloorPlan`, `condoSubType`, `landedSubType`, `hdbSubType`, `landSizeRange`, `completionYearRange`. ## `pages` (type: `integer`): How many pages to fetch for each search query (stops early when 99.co runs out of results). ## `resultCount` (type: `integer`): 99.co caps each search page at 60 results. ## `property_inputs` (type: `array`): Auto-detects each entry and calls the right endpoint: • Listing id (e.g. `EVPssE5S3KHLKudeJdhwT4`) → `/details/byid` • `https://www.99.co/...` listing URL → `/details/byurl` Each row = one full property record (price, address, agent contact, photos) enriched with similar listings, nearby amenities, floor plans, and project analysis (all in the `Raw` column). ## `enrich` (type: `boolean`): When on, each property detail also includes `similarListings`, `nearbyAmenities`, `floorPlans`, and `projectAnalysis` blocks (kept in the `Raw` column). Turn off for a leaner, faster detail call. ## `agent_search_queries` (type: `array`): Keyword matching agent or agency name → `/agent/search` (one row per agent, includes phone + slug). Use `*` to browse the full directory without a keyword. `Pages per search` and `Results per page` above apply. ## `agent_inputs` (type: `array`): Full agent profile → `/agent/details`. Accepts the agent slug from `/agent/search` (e.g. `R046036F-jack-tan`) or a profile URL (`https://www.99.co/singapore/agents/...`). Returns contact info, agency, and listings breakdown. ## `autocomplete_inputs` (type: `array`): Free-text location lookup → `/autocomplete`. One row per suggestion (MRT stations, districts, projects, schools) with its 99.co location id, type, and coordinates — useful for building precise searches. ## Actor input object example ```json { "search_inputs": [ "Orchard" ], "searchType": "For_Sale", "propertySegment": "residential", "propertyType": "all", "priceRange": "", "bedrooms": "", "bathrooms": "", "floorAreaRange": "", "moreFilters": "", "pages": 1, "resultCount": 25, "property_inputs": [ "EVPssE5S3KHLKudeJdhwT4" ], "enrich": true, "agent_search_queries": [ "tan" ], "agent_inputs": [], "autocomplete_inputs": [] } ```` # 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 = { "search_inputs": [ "Orchard" ], "property_inputs": [ "EVPssE5S3KHLKudeJdhwT4" ], "agent_search_queries": [ "tan" ], "agent_inputs": [], "autocomplete_inputs": [] }; // Run the Actor and wait for it to finish const run = await client.actor("one-api/99co-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 = { "search_inputs": ["Orchard"], "property_inputs": ["EVPssE5S3KHLKudeJdhwT4"], "agent_search_queries": ["tan"], "agent_inputs": [], "autocomplete_inputs": [], } # Run the Actor and wait for it to finish run = client.actor("one-api/99co-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 '{ "search_inputs": [ "Orchard" ], "property_inputs": [ "EVPssE5S3KHLKudeJdhwT4" ], "agent_search_queries": [ "tan" ], "agent_inputs": [], "autocomplete_inputs": [] }' | apify call one-api/99co-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=one-api/99co-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "99.co Scraper", "description": "Scrape 99.co Singapore property listings. Search by location, coordinates, polygon, or 99.co URL; pull enriched property details (similar listings, nearby amenities, floor plans, project analysis) by listing id or URL; and look up agents from the public directory.", "version": "1.0", "x-build-id": "xFX6eebMJ7K0WpDnR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/one-api~99co-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-one-api-99co-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/one-api~99co-scraper/runs": { "post": { "operationId": "runs-sync-one-api-99co-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/one-api~99co-scraper/run-sync": { "post": { "operationId": "run-sync-one-api-99co-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "search_inputs": { "title": "Search query (one per line)", "type": "array", "description": "Auto-detects each entry and routes to the right `/search/*` endpoint:\n• `https://www.99.co/...` search URL → `/search/byurl`\n• `lat,lng` or `lat,lng,radius_m` (e.g. `1.3042,103.8324,1500`) → `/search/bycoordinates`\n• 3+ space-separated `lat,lng` pairs (e.g. `1.30,103.82 1.32,103.82 1.32,103.85`) → `/search/bypolygon`\n• Anything else — MRT station, neighbourhood, district, project name (e.g. `Orchard`, `Tampines`) → `/search/bylocation`\n\nFilters below apply to every search call. Each hit returns one dataset row per listing.", "items": { "type": "string" } }, "searchType": { "title": "Listing type", "enum": [ "For_Sale", "For_Rent" ], "type": "string", "description": "Sale or rental listings.", "default": "For_Sale" }, "propertySegment": { "title": "Property segment", "enum": [ "residential", "commercial" ], "type": "string", "description": "Residential (HDB / condo / landed) or commercial (retail / office / industrial / land).", "default": "residential" }, "propertyType": { "title": "Property type", "enum": [ "all", "hdb", "condo", "landed", "retail", "office", "industrial", "land" ], "type": "string", "description": "Main category. `hdb` / `condo` / `landed` need segment = Residential; `retail` / `office` / `industrial` / `land` need segment = Commercial.", "default": "all" }, "priceRange": { "title": "Price range (SGD)", "type": "string", "description": "Format: `min:500000,max:3000000` — or just `min:500000` / `max:3000000`.", "default": "" }, "bedrooms": { "title": "Bedrooms", "type": "string", "description": "Comma-separated counts, e.g. `2,3` for 2- and 3-bedroom listings.", "default": "" }, "bathrooms": { "title": "Bathrooms", "type": "string", "description": "Comma-separated counts, e.g. `2`.", "default": "" }, "floorAreaRange": { "title": "Floor area range (sqft)", "type": "string", "description": "Format: `min:600,max:2000`.", "default": "" }, "moreFilters": { "title": "More filters (advanced)", "type": "string", "description": "Optional query-string of extra filters merged into every search, e.g. `furnishing=fully&tenure=freehold&floorLevel=high&propertyStatus=resale`. Supported keys include: `propertyStatus` (resale/new_launch/subsale), `tenure` (freehold/leasehold), `furnishing` (fully/partial/unfurnished), `floorLevel` (ground/low/mid/high/top/penthouse), `rentalType` (unit/room), `roomType` (master/common), `buildingAge` (5/10/20), `keywords`, `hasFloorPlan`, `condoSubType`, `landedSubType`, `hdbSubType`, `landSizeRange`, `completionYearRange`.", "default": "" }, "pages": { "title": "Pages per search", "minimum": 1, "maximum": 50, "type": "integer", "description": "How many pages to fetch for each search query (stops early when 99.co runs out of results).", "default": 1 }, "resultCount": { "title": "Results per page", "minimum": 1, "maximum": 60, "type": "integer", "description": "99.co caps each search page at 60 results.", "default": 25 }, "property_inputs": { "title": "Listing id or 99.co URL (one per line)", "type": "array", "description": "Auto-detects each entry and calls the right endpoint:\n• Listing id (e.g. `EVPssE5S3KHLKudeJdhwT4`) → `/details/byid`\n• `https://www.99.co/...` listing URL → `/details/byurl`\n\nEach row = one full property record (price, address, agent contact, photos) enriched with similar listings, nearby amenities, floor plans, and project analysis (all in the `Raw` column).", "items": { "type": "string" } }, "enrich": { "title": "Enrich property details", "type": "boolean", "description": "When on, each property detail also includes `similarListings`, `nearbyAmenities`, `floorPlans`, and `projectAnalysis` blocks (kept in the `Raw` column). Turn off for a leaner, faster detail call.", "default": true }, "agent_search_queries": { "title": "Agent search keyword (one per line)", "type": "array", "description": "Keyword matching agent or agency name → `/agent/search` (one row per agent, includes phone + slug). Use `*` to browse the full directory without a keyword. `Pages per search` and `Results per page` above apply.", "items": { "type": "string" } }, "agent_inputs": { "title": "Agent slug or profile URL (one per line)", "type": "array", "description": "Full agent profile → `/agent/details`. Accepts the agent slug from `/agent/search` (e.g. `R046036F-jack-tan`) or a profile URL (`https://www.99.co/singapore/agents/...`). Returns contact info, agency, and listings breakdown.", "items": { "type": "string" } }, "autocomplete_inputs": { "title": "Autocomplete query (one per line)", "type": "array", "description": "Free-text location lookup → `/autocomplete`. One row per suggestion (MRT stations, districts, projects, schools) with its 99.co location id, type, and coordinates — useful for building precise searches.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```