Abstract
Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms, whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search process renders it difficult for agents and consumers to understand the status changes of objects. In this study, Python is used to write web crawler and image recognition programs to capture object information from the web pages of real estate agents; perform data screening, arranging, and cleaning; compare the text of real estate object information; as well as integrate and use the convolutional neural network of a deep learning algorithm to implement image recognition. In this study, data are acquired from two business-to-consumer real estate agency networks, i.e., the Sinyi real estate agent and the Yungching real estate agent, and one consumer-to-consumer real estate agency platform, i.e., the, FiveNineOne real estate agent. The results indicate that text mining can reveal the similarities and differences between the objects, list the number of days that the object has been available for sale on the website, and provide the price fluctuations and fluctuation times during the sales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for real estate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differences between their commodities and other businesses in approximately 2 min, as well as rapidly determine developable objects via comparison results provided by the system. Meanwhile, consumers require less time in searching and comparing prices after they have understood the commodity dynamic information, thereby allowing them to use the most efficient approach to purchase real estate objects of their interest.
Highlights
Data are acquired from two business-to-consumer real estate agency networks, i.e., the Sinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent
If the staff of Sinyi real estate agent (S-REA) wish to know whether the status of their own objects has changed, they can query through the dropped worksheet; if they wish to know the information regarding other real estate objects and develop potential objects, they can use the added worksheet
4.2.2 Price Fluctuations and Number of Days for Sale of S-REA, Yungching real estate agent (Y-REA), and FiveNineOne real estate agent (F-REA) Objects Based on the text mining of daily crawled object information, the price of 135 among 3,907 objects was reduced during the data acquisition period, the price of 47 objects was reduced by more than twice, and the most number of days an object was advertised for sale on the real estate agency website was 97 days
Summary
In the current digital age, consumers typically rely on the search function provided by the Internet when purchasing products. The system (1) uses Python to develop a crawler program to extract web page information from two business-to-consumer (B2C)-type real estate agency websites in Taiwan, i.e., the Sinyi real estate agent (S-REA) and Yungching real estate agent (Y-REA), and a consumer-to-consumer (C2C)-type real estate trading platform, i.e., the FiveNineOne real estate agent (F-REA), including text descriptions and images of real estate objects; (2) conducts object difference analysis via text mining and deep learning image comparison; (3) provides comparison results to allow real estate agents to rapidly develop new objects; (4) recommends text or image information to consumers after performing a comparison to allow them to obtain real-time and dynamic information regarding objects, as well as reduce the time and cost of searching
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
