# Airbnb Market Analyzer — Price Distribution by Area & Dates (`jdepablos/airbnb-market-analyzer`) Actor One call = one short-term rental market analyzed. Live price distribution (median, percentiles, nightly), rating mix and new-listing share for any city or neighbourhood — structured for hosts, investors and AI agents. - **URL**: https://apify.com/jdepablos/airbnb-market-analyzer.md - **Developed by:** [Juan de Pablos](https://apify.com/jdepablos) (community) - **Categories:** Travel, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $40.00 / 1,000 market scans This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Airbnb Market Analyzer — Price Distribution by Area & Dates **One call = one short-term rental market analyzed.** Give it a city or neighbourhood (plus optional dates) and get the live market picture as structured data: price distribution (median, percentiles, per-night), rating mix, share of new listings and a sample of the listings behind the numbers. Built for hosts pricing their place, STR investors comparing areas, and AI agents. ```json { "location": "Madrid, España", "checkin": "2026-08-10", "checkout": "2026-08-12", "analysis": { "n": 36, "currency": "EUR", "priceBasis": "total-stay", "nights": 2, "median": 174.5, "nightlyMedian": 87.25, "p10": 122, "p25": 143, "p75": 187, "p90": 199.5, "ratingAvg": 4.76, "ratedSharePct": 91.7, "newListingsSharePct": 8.3, "confidence": "high" }, "listings": [ { "name": "Apartamento Ático Loft centro Madrid", "price": 176, "rating": 4.7, "url": "https://www.airbnb.com/rooms/…" } ], "meta": { "listingsAnalyzed": 36, "pagesFetched": 2, "geo": { "resolvedName": "Madrid, Comunidad de Madrid, España", "radiusKm": 10 } } } ```` ### What makes the numbers trustworthy - **Deterministic geography.** Airbnb's text search quietly mixes worldwide "inspiration" results into responses (we measured it: searching Madrid returned cabins in Brazil). This actor geocodes your location and searches **by map bounding box** instead — every listing in the sample is actually in your market. `radiusKm` controls the scope: ~10 for a city, 2–3 for a neighbourhood. - **Dates = real quotes.** With `checkin`/`checkout` the prices are actual total-stay quotes for those nights (a `nightlyMedian` is derived). Without dates you get Airbnb's indicative nightly prices — the output labels which basis you're looking at (`priceBasis`). - **Cleaned sample.** Duplicates and geographically inconsistent results are dropped before any statistics run, and `meta.filteredOut` tells you what was removed. ### Use cases - **Host pricing**: "what does the market charge for 2 guests in my area next weekend?" — median and p25–p75 band in one call. - **Investor screening**: batch-run 10 areas and compare `nightlyMedian`, `ratingAvg` and `newListingsSharePct` (market saturation signal). - **Seasonality tracking**: schedule weekly runs with fixed dates offset and diff the medians over time. ### Use with AI agents (MCP) ``` https://mcp.apify.com?tools=jdepablos/airbnb-market-analyzer ``` Your agent gets a "what's the going rate in X?" tool with self-describing structured output — no pagination, no HTML, no post-processing. ### Pricing Pay per event: **$0.04 per market scan** (up to 5 result pages ≈ 90 listings sampled, stats + sample included). Scans that return no listings are **never charged**. ### FAQ **Where do prices come from?** Airbnb's public search results for your bounding box — live offer prices (asks), not booked reservations. The output states this via `priceBasis`. **Which domains?** `airbnb.es` (EUR) and `airbnb.com` (USD). Same inventory, different display currency. **How is this different from AirDNA?** AirDNA sells deep historical subscriptions ($40–500/month). This is the live market snapshot as a pay-per-call API — no subscription, agent-ready, and you can build your own history by scheduling snapshots. **Is this affiliated with Airbnb?** No. Data comes from publicly visible search results. # Actor input Schema ## `location` (type: `string`): City, neighbourhood or area to analyze, e.g. "Madrid, España" or "Malasaña, Madrid". For several markets use `items`. ## `items` (type: `array`): Up to 10 markets per run: `[{"location": "Sevilla"}, {"location": "Lisboa", "checkin": "2026-08-10", "checkout": "2026-08-12"}]`. ## `checkin` (type: `string`): Optional. With dates, prices are real quotes for the stay (and a nightly median is derived). Without dates, Airbnb shows indicative nightly prices. ## `checkout` (type: `string`): Optional, requires check-in. ## `adults` (type: `integer`): Guests the market is quoted for. ## `domain` (type: `string`): airbnb.es quotes EUR, airbnb.com quotes USD. ## `maxPages` (type: `integer`): Each page adds ~18 listings to the sample (max 5 pages ≈ 90 listings). ## `strictGeo` (type: `boolean`): Drop listings geographically inconsistent with the sample (Airbnb mixes 'flexible' out-of-area results into searches). Recommended. ## `maxListings` (type: `integer`): How many individual listings to include in the output (stats always use the full cleaned sample). ## `proxy` (type: `object`): Residential proxy recommended (default). ## `radiusKm` (type: `integer`): Market radius around the geocoded center. ~10 for a city, 2-3 for a neighbourhood. ## Actor input object example ```json { "location": "Madrid, España", "adults": 2, "domain": "airbnb.es", "maxPages": 3, "strictGeo": true, "maxListings": 24, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] }, "radiusKm": 10 } ``` # 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 = { "location": "Madrid, España", "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("jdepablos/airbnb-market-analyzer").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 = { "location": "Madrid, España", "proxy": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("jdepablos/airbnb-market-analyzer").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 '{ "location": "Madrid, España", "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call jdepablos/airbnb-market-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jdepablos/airbnb-market-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Airbnb Market Analyzer — Price Distribution by Area & Dates", "description": "One call = one short-term rental market analyzed. Live price distribution (median, percentiles, nightly), rating mix and new-listing share for any city or neighbourhood — structured for hosts, investors and AI agents.", "version": "0.1", "x-build-id": "K1SHu6RcRTfT1gA5N" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jdepablos~airbnb-market-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jdepablos-airbnb-market-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jdepablos~airbnb-market-analyzer/runs": { "post": { "operationId": "runs-sync-jdepablos-airbnb-market-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jdepablos~airbnb-market-analyzer/run-sync": { "post": { "operationId": "run-sync-jdepablos-airbnb-market-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "location": { "title": "Market / area", "type": "string", "description": "City, neighbourhood or area to analyze, e.g. \"Madrid, España\" or \"Malasaña, Madrid\". For several markets use `items`." }, "items": { "title": "Batch markets", "type": "array", "description": "Up to 10 markets per run: `[{\"location\": \"Sevilla\"}, {\"location\": \"Lisboa\", \"checkin\": \"2026-08-10\", \"checkout\": \"2026-08-12\"}]`." }, "checkin": { "title": "Check-in (YYYY-MM-DD)", "type": "string", "description": "Optional. With dates, prices are real quotes for the stay (and a nightly median is derived). Without dates, Airbnb shows indicative nightly prices." }, "checkout": { "title": "Check-out (YYYY-MM-DD)", "type": "string", "description": "Optional, requires check-in." }, "adults": { "title": "Adults", "type": "integer", "description": "Guests the market is quoted for.", "default": 2 }, "domain": { "title": "Airbnb domain", "enum": [ "airbnb.es", "airbnb.com" ], "type": "string", "description": "airbnb.es quotes EUR, airbnb.com quotes USD.", "default": "airbnb.es" }, "maxPages": { "title": "Result pages to sample", "minimum": 1, "maximum": 5, "type": "integer", "description": "Each page adds ~18 listings to the sample (max 5 pages ≈ 90 listings).", "default": 3 }, "strictGeo": { "title": "Strict geo filter", "type": "boolean", "description": "Drop listings geographically inconsistent with the sample (Airbnb mixes 'flexible' out-of-area results into searches). Recommended.", "default": true }, "maxListings": { "title": "Sample listings returned", "minimum": 0, "maximum": 90, "type": "integer", "description": "How many individual listings to include in the output (stats always use the full cleaned sample).", "default": 24 }, "proxy": { "title": "Proxy", "type": "object", "description": "Residential proxy recommended (default).", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }, "radiusKm": { "title": "Radius (km)", "minimum": 1, "maximum": 50, "type": "integer", "description": "Market radius around the geocoded center. ~10 for a city, 2-3 for a neighbourhood.", "default": 10 } } }, "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 } } } } } } } } } } ```