The sale price of apartment buildings is a key factor in the success or failure of apartment projects, and the factors that affect the sale price of apartments vary widely, including location, environmental factors, and economic conditions. Existing methods of predicting the sale price do not reflect the nonlinear characteristics of apartment prices, which are determined by the complex impact factors of reality, because statistical analysis is conducted under the assumption of a linear model. To improve these problems, a new analysis technique is needed to predict apartment sales prices by complex nonlinear influencing factors. Using machine learning techniques that have recently attracted attention in the field of engineering, it is possible to predict the sale price reflecting the complexity of various factors. Therefore, this study aims to conduct a basic study for the development of a machine learning-based prediction model for apartment sale prices.
This research was supported by a grant (NRF-2019R1A2C1009398) from the National Research Foundation of Korea by Ministry of Science, ICT and Future Planning. This research was supported by a grant (NRF-2018R1C1B6004123) from the National Research Foundation of Korea by Ministry of Science, ICT and Future Planning.