| dc.description.abstract | Since 2020, there has been a wave of real estate discussion in the society, and there are many topics related to housing and land sales and transfers. With such a high level of interest in real estate, the industry needs to accelerate the pace of digital transformation by continuously optimizing internal workflows, building customer relationship management systems with algorithms and big data, etc., and leading companies to the ever-changing challenges with new thinking. However, there is currently no objective basis for measuring the competitiveness of the objects commissioned. If relevant tools or indicators can be developed to measure the status of the objects, it will help to improve the efficiency of the sales of the objects. A review of past literature reveals that there is no objective assessment of housing competitiveness, and studies and research on days of sales. Therefore, the objectives of this study are as follows.
1. to construct a days-to-sale model and evaluate the accuracy rate by using the data of housing transactions undertaken by the real estate brokerage industry.
2. to identify the significant factors and variables that can effectively predict the days of sales.
Based on the application background of real estate industry, this study uses the original object data commissioned by the real estate industry and then uses an algorithm to filter the important features, apply various methods to model and compare the advantages and disadvantages, and use the cross-fold method as a validation to evaluate the effectiveness of various models. After the evaluation of model effectiveness, the random forest and gradient lifter performed better than linear regression, and the same results were obtained for the regression indicators MSE and MAE. In terms of important variables, the relationship between the independent variables and the day-of-sale prediction model, the three most important variables were ＂gate orientation＂, ＂administrative area＂, and ＂reason for sale＂. | en_US |