Housing Price Prediction Using Machine Learning Algorithms: The Case of Melbourne City, Australia

Abstract

House price forecasting is an important topic of real estate. The literature attempts to derive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transactions in Australia to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs in the city of Melbourne. Moreover, experiments demonstrate that the combination of Stepwise and Support Vector Machine that is based on mean squared error measurement is a competitive approach.