🏠 Real Estate & Property Intelligence Pipeline

This actor is a powerful data-gathering tool that transforms raw news from top RSS feeds (focused on Real Estate, Property, and Housing Markets) into structured, actionable intelligence. It uses DuckDuckGo News Search to gather real-time context and an LLM (OpenAI) to perform advanced analysis.

This actor is designed to run on a schedule, continuously gathering fresh intelligence. The structured data it produces is intended to be consumed by other systems, such as our flagship actor, Content Blueprint AI, which can use this stream of data to generate reports, market briefings, analysis, and more.

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How to Use

You have two primary ways to use this actor:

1. As a Data Source for Downstream Systems: This is the primary intended use. Run this actor on a schedule to build a dataset of fresh intelligence. Other tools, including the Content Blueprint AI actor, can then be pointed to this actor's dataset to generate final content and reports.
2. As a Standalone Tool: Run this actor to generate high-quality, structured news intelligence. You are free to download the resulting dataset for your own market analysis, investment research, or to feed into other custom property workflows.

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Features

• Comprehensive Source Aggregation: Gathers news from a curated list of top-tier Real Estate and Property feeds (e.g., Realtor.com, HousingWire, BiggerPockets).
• Real-Time Grounding (DuckDuckGo): Uses the DuckDuckGo News Search to find fresh, corroborating snippets for each article, enriching the context before analysis. This ensures the summary is based on current, cross-validated information.
• Advanced AI Analysis: Leverages an LLM to analyze each article for sentiment/market dynamic (e.g., High Growth, Cooling Market, Policy Driven), categorize the topic (e.g., Residential Sales/Pricing, Commercial/Office/Retail, Investment/Financing/REITs), and extract key entities.
• Resilient Processing: The pipeline is designed to fall back to the original RSS summary if the DuckDuckGo search fails, ensuring the run completes without crashing.
• Duplicate Prevention: Intelligently tracks processed articles across runs to ensure you only process and pay for new information.
• Cost-Saving Test Mode: Includes a test mode to run the full workflow with dummy data, allowing for development and testing without incurring API costs.

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Setup and Configuration

Before running the actor, you only need to provide an API key for the LLM service.

1. OpenAI API Key:
   • You will need an API key from your OpenAI account.

Add Keys to Apify Secrets

For security, add this key as a secret environment variable in your Apify Actor settings:

• OPENAI_API_KEY: Your OpenAI API Key.

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Cost of Usage 💸

Important Note: The costs listed below are for this actor only. Using this data with any other actor will incur its own separate API and platform costs.

Costs for This Actor

1. Apify Platform Usage: Standard platform costs for running the actor, which depends on the duration of the run.
2. DuckDuckGo Search: This service is free and is handled internally by the actor, replacing the need for paid search APIs. The actor performs one search query for every article it processes.
3. OpenAI API: This is the primary cost. The actor makes two LLM calls for every article: one to summarize the DuckDuckGo Search snippets and another to perform the final analysis (sentiment, category, etc.).

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Input

Field	Type	Default	Description
source	String	all	The news source category to use (e.g., 'all', 'housingwire', 'realtor').
customFeedUrl	String	null	A custom RSS feed URL to use if source is set to custom.
maxArticles	Integer	20	The maximum number of new articles to fetch and process in a single run.
region	String	wt-wt	Region to limit search results by (e.g., 'us-en' for US, 'wt-wt' for World).
timeLimit	String	w	Limit search results by time (e.g., 'd' for day, 'w' for week).
runTestMode	Boolean	false	Bypasses all external API calls for zero-cost testing. Do not enable in production.

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Output

The actor saves its results in the dataset. Each item is a structured JSON object with the following fields:

Field	Type	Description
source	String	The name of the news source (e.g., 'HousingWire').
title	String	The original title of the news article.
url	String	The URL of the original article.
published	String	The publication date string from the RSS feed.
summary	String	The AI-generated summary of the article.
sentiment	String	The AI-analyzed market dynamic (e.g., High Growth, Cooling Market).
category	String	The AI-assigned category (e.g., Residential Sales/Pricing, Mortgage/Interest Rates).
key_entities	Array of Strings	A list of key entities like cities, regions, companies, or financial metrics.