Domestic proptech company Zigbang has completely upgraded its AI brokerage service and expanded its scope to include one-room apartments, officetels, and villas. This expansion of the existing apartment-focused interactive search system to encompass all housing types aims to transform its real estate platform from a simple property search service to an AI-driven decision-making infrastructure.
With this update, users can search not only for apartments but also for one-room apartments, officetels, and villas through natural language conversation. By entering common terms like management fees, parking availability, and elevator availability, the AI analyzes these and suggests suitable properties. You can also search for lifestyle-related terms like "quiet neighborhood," "good school district," or "one-room apartment near Line 2" and brands. If the location is unclear, the AI will ask additional questions to refine your search criteria.
A new feature has been introduced that, if no properties match the search criteria, AI gradually expands the search criteria to suggest similar alternatives. While the existing platform's structure ended with a "No results," this shift has shifted the focus to providing a reorganized selection of highly transaction-prone options. Multilingual support has also been added, allowing queries in English, Japanese, and Chinese to be answered in the appropriate language.
The real estate platform industry has long been criticized for remaining stuck in a map- and filter-based search structure. Zigbang's AI broker integrates and analyzes actual transaction prices, complex information, transaction flow, and user behavior data to narrow down potential properties and provide summary information. This represents a shift from a structure that simply lists information to one that supports decision-making.
The expansion of AI's scope to the small rental housing market is seen as a strategic move to broaden market coverage. This expansion encompasses not only the transaction-focused mid- to large-sized residential market but also high-demand rental areas like one-room apartments, officetels, and villas, laying the foundation for simultaneously expanding platform dwell time and transaction touchpoints.
Zigbang has accumulated structured data at the complex and household level nationwide, price prediction models, and user search data. Operating on this data asset, the AI recommendation engine analyzes transaction context and makes recommendations. As data volume and learning experience accumulate, recommendation accuracy is expected to improve.
CEO Ahn Sung-woo stated that AI brokers are a key infrastructure for transforming real estate searches into conversational recommendation structures, and that the platform's competitiveness has been strengthened by expanding to all housing types.
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