鄭 品杰

This paper describes a property search system called Tech Supplier, which is designed for helping expert searchers in a real estate company to find properties for renovation. ReTech (Real Estate Technology) or PropTech (Property Technology) is known to be one of the most promising topics following FinTech (Financial Technology). Among several potential technologies in ReTech, property search attracts a lot of attention. Different from such item as books, movies, and daily goods, properties are difficult to search for ordinary persons because of several reasons: less opportunity to buy properties, imbalance of knowledge about the properties between customers and salespersons in real estate companies. Therefore, the salesperson usually helps customers to find their relevant properties based on his / her domain knowledge. To improve the efficiency of property search for renovation, Tech Supplier has several functions: floor plan identification function, elimination of apparently irrelevant properties from a retrieved list, and properties search with ranking function. Among those functions, this paper focuses on the latter two ones. Experiments are conducted with the help of expert searchers in a real estate company in terms of search efficiency of the proposed system and effectiveness of a learning to rank. Experimental results show that relevant properties can be found within a higher rank of retrieved lists by introducing a learning to rank. It is also observed that time spent on a session (a series of searches performed for a certain customer need) by expert searchers using Tech Supplier is reduced to 55% of an existing system.