The hedonic pricing model is extensively applied in property pricing modelling. It considers property as a bundled commodity and models its price as a function of its constituent parts – physical characteristics, neighbourhood attributes, and location factor. However, several issues in the conventional HPM hinder its accuracy in predicting property prices. This paper reviewed these issues with the ways of addressing them simultaneously. The review found the major issues in HPM include – normality of property prices, linearity, heteroskedasticity, multicollinearity, spatial dependence, spatial heterogeneity, spatial autocorrelation, and aggregation bias. These issues were found to substantially reduced the accuracy of property price modelling. These issues are minimised by specifying correct functional form which log-log specification was mostly found to be more efficient, dimension reduction using PCA or factor analysis, and property market segmentation. The use of these measures significantly reduces estimation errors and improves model fit thereby increasing the accuracy of property price prediction.  The review recommends caution in choosing the correct functional form a well as the application of property market segmentation in modelling property market using different methodologies.