Rail transport is one of the factors that boost economic growth. Increased accessibility while saving travel costs and time offered by rail transport attracts foreign and local investments, which lead to increased house prices and rents. Nonetheless, it is argued that noise pollution coming from rail transport may also reduce house prices and rents because these areas are less desirable for occupation and investment. Hence, this research aims to establish rail transport’s impact on house prices and rents through a critical review of the literature. An overview of previous studies shows that house prices and rents are significantly influenced by proximity to rail transports. This indicates that proximity to rail transports is accounted for when making house purchase and rent decisions. Thus, property valuers, planners, and developers should consider rail transport location in planning, developing, and valuing properties.

The Hedonic Price Model (HPM), a prominent model used in real estate appraisal and economics, has been argued to be marred with nonlinearity, multicollinearity and heteroscedasticity problems that affect the accuracy of price predictions. An alternative method called Artificial Neural Network Model (ANN) was identified as capable of addressing the shortcomings of HPM and produces superior predictive performance. Hence, this study aims to evaluate the forecasting performance between HPM and ANN using Malaysian housing transaction data from the period between 2009 to 2018, sourced from the Valuation and Property Service Department, Johor Bahru. The models’ performance was evaluated and compared based on their statistical and predictive performance. Results showed that ANN outperformed HPM in both statistical and predictive performance. This study benefits the expansion of academic and practical knowledge in enhancing the accuracy of house price forecasting.