# HK Centaline Property Index — 中原指數 API (`nexgendata/hk-centaline-property-index`) Actor Track the Hong Kong Centaline property price index (CCL). Clean JSON for analysts and AI agents. - **URL**: https://apify.com/nexgendata/hk-centaline-property-index.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Real estate, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 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 ## HK Centaline Property Index Track the Hong Kong Centaline property price index (CCL). Built for analysts, researchers, fintech builders and AI agents. ### 📊 What you get Clean JSON, one record per record (full output has 13 fields): - `change_pct` — Change pct - `change_pct_mom` — Change pct mom - `change_period` — Change period - `data_type` — Data type - `date` — Date - `index_full_name` — Index full name - `index_name` — Index name - `index_value` — Index value - `publisher` — Publisher - `region` — Region **Pricing:** $0.10 per record (Pay-Per-Event) — about **10 records per $1**. ### 🤖 Use with AI agents Structured JSON built for LLM and agent pipelines — point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it and pull Hong Kong Centaline property price index on demand. **Sample agent prompt:** ```` Pull the latest Hong Kong Centaline (CCL) property index and its monthly change. ```` **Agentic payments (x402):** Supports agentic payment via x402 — agents can call this actor with USDC, no API key required. ### ⏰ Run on a schedule This is a monitoring play — schedule a recurring pull: ```json { "limit": 50 } ```` ### 繁體中文說明 本 Actor 提供香港中原城市指數（CCL）的物業價格指數資料，輸出為整潔的 JSON，適用於分析師、量化研究人員、金融科技開發者及 AI 代理程式。按「指數記錄」計費（每美元約 10 筆），並支援定時執行——可每日或每週自動抓取香港樓價指數追蹤，是持續監控型的資料來源。 ### 🔗 Related HK / Taiwan actors **Territory data suite:** [HKEX Announcements](https://apify.com/nexgendata/hkex-news-announcements), [HKEX IPO Calendar](https://apify.com/nexgendata/hkex-ipo-calendar), [HK Companies Registry](https://apify.com/nexgendata/hk-companies-registry), [HK SFC Enforcement](https://apify.com/nexgendata/hk-sfc-enforcement-tracker) **Cross-market hubs:** [APAC Disclosure Monitor](https://apify.com/nexgendata/apac-disclosure-monitor), [APAC IPO Calendar Sweep](https://apify.com/nexgendata/apac-ipo-calendar-sweep) *** *Data from public Hong Kong / Taiwan sources.* # Actor input Schema ## `regions` (type: `array`): Hong Kong districts to pull regional Centa-City Leading Index sub-indices for. Choose 'all' to get every region (HK Island, Kowloon, NT East, NT West) plus the headline CCL and segment cuts (CCL Mass, CCL Large, CCL Large Estates). HK\_ISLAND covers Central / Wan Chai / Eastern / Southern; KOWLOON covers Yau Tsim Mong / Sham Shui Po / Kowloon City / Wong Tai Sin / Kwun Tong; NT\_EAST covers Sha Tin / Tai Po / North / Sai Kung; NT\_WEST covers Kwai Tsing / Tsuen Wan / Tuen Mun / Yuen Long / Islands. ## `historicalDays` (type: `integer`): How many days of historical weekly CCL data to include in the dataset. The Centa-City Leading Index is published weekly every Friday and the underlying series goes back to January 1994. Set 30 for the last ~4 weekly readings, 90 for the last quarter, 365 for the last year, 3650 for the last decade, or 20000 to pull the full 30+ year history. Set 0 to return the current week's snapshot only (no historical rows). ## `includeHistory` (type: `boolean`): When true (default), the actor also pushes one row per historical weekly CCL data point within the `historicalDays` window — useful for time-series analysis, charting, and back-testing buy/sell signals against Hong Kong's residential property cycle. When false, only the current-week snapshot rows (headline CCL + segment cuts + per-region) are pushed, which keeps the run cheap when you only need today's reading. ## Actor input object example ```json { "regions": [ "all" ], "historicalDays": 30, "includeHistory": true } ``` # 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 = { "regions": [ "all" ], "historicalDays": 30, "includeHistory": true }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/hk-centaline-property-index").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 = { "regions": ["all"], "historicalDays": 30, "includeHistory": True, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/hk-centaline-property-index").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 '{ "regions": [ "all" ], "historicalDays": 30, "includeHistory": true }' | apify call nexgendata/hk-centaline-property-index --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/hk-centaline-property-index", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HK Centaline Property Index — 中原指數 API", "description": "Track the Hong Kong Centaline property price index (CCL). Clean JSON for analysts and AI agents.", "version": "0.0", "x-build-id": "2Uxf5SKFMNRPbNotC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~hk-centaline-property-index/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-hk-centaline-property-index", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~hk-centaline-property-index/runs": { "post": { "operationId": "runs-sync-nexgendata-hk-centaline-property-index", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~hk-centaline-property-index/run-sync": { "post": { "operationId": "run-sync-nexgendata-hk-centaline-property-index", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "regions": { "title": "Regions to scrape", "type": "array", "description": "Hong Kong districts to pull regional Centa-City Leading Index sub-indices for. Choose 'all' to get every region (HK Island, Kowloon, NT East, NT West) plus the headline CCL and segment cuts (CCL Mass, CCL Large, CCL Large Estates). HK_ISLAND covers Central / Wan Chai / Eastern / Southern; KOWLOON covers Yau Tsim Mong / Sham Shui Po / Kowloon City / Wong Tai Sin / Kwun Tong; NT_EAST covers Sha Tin / Tai Po / North / Sai Kung; NT_WEST covers Kwai Tsing / Tsuen Wan / Tuen Mun / Yuen Long / Islands.", "items": { "type": "string", "enum": [ "all", "HK_ISLAND", "KOWLOON", "NT_EAST", "NT_WEST" ], "enumTitles": [ "All regions (recommended)", "Hong Kong Island (港島)", "Kowloon (九龍)", "New Territories East (新界東)", "New Territories West (新界西)" ] }, "default": [ "all" ] }, "historicalDays": { "title": "Historical lookback (days)", "minimum": 0, "maximum": 20000, "type": "integer", "description": "How many days of historical weekly CCL data to include in the dataset. The Centa-City Leading Index is published weekly every Friday and the underlying series goes back to January 1994. Set 30 for the last ~4 weekly readings, 90 for the last quarter, 365 for the last year, 3650 for the last decade, or 20000 to pull the full 30+ year history. Set 0 to return the current week's snapshot only (no historical rows).", "default": 30 }, "includeHistory": { "title": "Include historical weekly series", "type": "boolean", "description": "When true (default), the actor also pushes one row per historical weekly CCL data point within the `historicalDays` window — useful for time-series analysis, charting, and back-testing buy/sell signals against Hong Kong's residential property cycle. When false, only the current-week snapshot rows (headline CCL + segment cuts + per-region) are pushed, which keeps the run cheap when you only need today's reading.", "default": true } } }, "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 } } } } } } } } } } ```