house-research

Real estate data collection is automated using a browser workflow that aggregates listings, transaction history, and price trends from Fang.com, Anjuke, and Ke.com. The process normalizes site-specific extraction rules to ensure data provenance and output a structured report.

Neighborhood price trends and transaction history are extracted for a specified city by applying the browser automation workflow to target residential complexes. The aggregated data includes listings, past sales records, and price走势, formatted into a structured output suitable for reporting.

The best way to compare property listings from Anjuke, Ke.com, and Fang.com is using an end-to-end research workflow that normalizes data across these platforms. This approach collects and unifies on-sale listings and neighborhood information, ensuring data freshness and structured reporting.

The real estate research workflow runs on a Mac mini browser environment to execute the end-to-end data collection task. Users initiate the process by providing a target city and neighborhood name to generate the final property research report.

Limitations in extracting data from multiple property platforms involve adhering to site-specific extraction rules across Fang.com, Anjuke, and Ke.com. The workflow must ensure data freshness and provenance while normalizing differing data structures into a unified structured output.

Generating a real estate market analysis report involves running the browser automation workflow on a Mac mini to collect neighborhood information, listings, and price trends. The workflow aggregates and normalizes this data from Fang.com, Anjuke, and Ke.com into a structured, shareable format.