# 99acres Data Scraper (`datablow/99acres-data-scraper`) Actor Scrapes locality reviews and historical price trends from 99acres.com. Supports direct 99acres URLs and delivers results to the provided email. Powered by Camoufox for stealthy Firefox-based scraping. - **URL**: https://apify.com/datablow/99acres-data-scraper.md - **Developed by:** [datablow](https://apify.com/datablow) (community) - **Categories:** Real estate, Automation, Other - **Stats:** 12 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.007 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## 🏠 99acres Reviews & Price Trends Scraper Extract **locality insights, price trends, and user reviews** from 99acres in a structured format — ready for analysis, reporting, or AI applications. --- ### 🚀 How It Works 1. Provide a **99acres review URL** 2. Set the **number of reviews** 3. Enter your **email (optional)** 4. Run the actor → Get results instantly (CSV via email or dataset) --- ### ⚙️ Input Options * 🔗 **99acres URL** – Review or price trends page * 🔢 **Max Reviews** – Limit number of reviews per location * 📧 **User Email** – Receive results directly in your inbox --- ### 📊 Output Data Structured dataset including: * 📍 Locality name & overall rating * 💰 Average price per sqft & appreciation trends * 📝 Reviews (title, positives, negatives, user type) * 📈 Historical price trends --- ### 💡 Use Cases #### 📊 Data Science * Price trend analysis & growth forecasting * Feature engineering from reviews & trends * Correlation analysis (ratings vs price growth) --- #### 🤖 AI / Machine Learning * Property price prediction models * Review sentiment analysis (NLP) * AI-based locality ranking & recommendations * Automated review summarization (LLMs) --- #### 📊 Market Research * Identify high-growth investment locations * Compare localities (price, sentiment, livability) * Understand buyer/tenant preferences * Detect undervalued vs overpriced areas --- #### 📦 Data & Analytics * Build pipelines (Apify → n8n → BigQuery) * Create dashboards (Looker Studio, Power BI, Tableau) * Generate automated reports (CSV / PDF) --- #### 💰 Business / SaaS * Automated property insight reports (email-based) * Real estate intelligence APIs * PropTech apps for buyers, agents & investors --- #### 🔥 Advanced Applications * LLM-powered real estate assistant * Investment scoring models (risk vs return) * Geo-spatial insights (price heatmaps, sentiment maps) --- ### 📌 Summary Turn raw real estate data into: > 📊 Insights → 🤖 AI → 💰 Business Value Perfect for **analysts, investors, and PropTech builders**. # Actor input Schema ## `startUrls` (type: `array`): List of 99acres review page URLs to scrape directly. Example: 'https://www.99acres.com/madhapur-hyderabad-reviews-and-ratings-wrffid' ## `maxReviewsPerPage` (type: `integer`): Max number of reviews to scrape per location. Default is 100. Set to 0 to scrape all available reviews. ## `useProxy` (type: `boolean`): Recommended to avoid blocks. Requires a paid Apify plan for residential proxies. ## `email` (type: `string`): Email address where the results will be sent via n8n integration. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.99acres.com/madhapur-hyderabad-reviews-and-ratings-wrffid" } ], "maxReviewsPerPage": 100, "useProxy": true, "email": "user@example.com" } ```` # 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 = { "startUrls": [ { "url": "https://www.99acres.com/madhapur-hyderabad-reviews-and-ratings-wrffid" } ], "email": "user@example.com" }; // Run the Actor and wait for it to finish const run = await client.actor("datablow/99acres-data-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 = { "startUrls": [{ "url": "https://www.99acres.com/madhapur-hyderabad-reviews-and-ratings-wrffid" }], "email": "user@example.com", } # Run the Actor and wait for it to finish run = client.actor("datablow/99acres-data-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 '{ "startUrls": [ { "url": "https://www.99acres.com/madhapur-hyderabad-reviews-and-ratings-wrffid" } ], "email": "user@example.com" }' | apify call datablow/99acres-data-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datablow/99acres-data-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "99acres Data Scraper", "description": "Scrapes locality reviews and historical price trends from 99acres.com. Supports direct 99acres URLs and delivers results to the provided email. Powered by Camoufox for stealthy Firefox-based scraping.", "version": "1.0", "x-build-id": "3kNFkWHoJ7eZzSvVk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datablow~99acres-data-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datablow-99acres-data-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datablow~99acres-data-scraper/runs": { "post": { "operationId": "runs-sync-datablow-99acres-data-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datablow~99acres-data-scraper/run-sync": { "post": { "operationId": "run-sync-datablow-99acres-data-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "startUrls": { "title": "99acres Direct URLs", "type": "array", "description": "List of 99acres review page URLs to scrape directly. Example: 'https://www.99acres.com/madhapur-hyderabad-reviews-and-ratings-wrffid'", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxReviewsPerPage": { "title": "Max reviews per location", "minimum": 0, "type": "integer", "description": "Max number of reviews to scrape per location. Default is 100. Set to 0 to scrape all available reviews.", "default": 100 }, "useProxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Recommended to avoid blocks. Requires a paid Apify plan for residential proxies.", "default": true }, "email": { "title": "User Email", "pattern": "^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$", "type": "string", "description": "Email address where the results will be sent via n8n integration." } } }, "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 } } } } } } } } } } ```