The Impact of Environmental Factors on Housing Prices: A Case Study of Taipei Housing Transactions

Abstract

Most research on housing price modeling only utilizes a single environmental factor. The goals of this paper are to select the appropriate factors and to identify the influencing patterns for 3 major types of real estates through model building that includes 49 housing factors. The datasets were composed by 33,027 transactions in Taipei City from July 2013 to the end of 2016. The models utilized were Decision Tree (DT), Artificial Neural Networks (ANN), Random Forest (RF), Model Tree (MT), and Multiple Regression (MR). The importance of each factor derived from the above 5 models is thus analyzed and ranked for the 3 housing types. Also, this paper adopts Generalized Additive Models (GAM) to derive the patterns of important factors influencing housing prices that includes increasing, decreasing, and non-linear relationships.