Study of Influential Factors on Housing Prices by Using MLR

Abstract

With the emergence of the 2019 pandemic, the global economy experienced a downturn, but the housing market quickly rebounded post-pandemic, heightening the anxiety surrounding home buying. Previous research predominantly employed regression models to forecast housing prices, but these studies largely focused on specific factors. Hence, this article aims to offer a more comprehensive viewpoint by exploring the impact of multiple independent variables on housing prices, particularly emphasizing the effects of the age of buildings, the surrounding environment, and architectural factors. The methodology used in this study is the Multiple Linear Regression (MLR) model. It analyzes the real estate market data in Taipei, meticulously constructing and validating multiple models. The findings reveal that the age of buildings and the distance to the nearest subway station negatively influence housing prices, whereas the number of convenience stores positively impacts them. Among these factors, the quantity of convenience stores exerts the most significant effect on housing prices. Overall, this article provides a novel perspective and tools for understanding and predicting housing prices, assisting real estate developers and buyers in making more informed decisions in the complex real estate market. This study highlights the multidimensional nature of real estate value and contributes to the sustainable development of the real estate market.