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

The Danh Phan

150 citations0/0 files storedInternational Conference on Machine Learning and Data Engineering (iCMLDE)

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Housing Market and Economics Forecasting Techniques and Applications Data Mining and Machine Learning Applications

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.

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2014Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data 2010Housing price forecasting based on genetic algorithm and support vector machine 2014Forecasting the U.S. real house price index 2014Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection 2014A SVM Stock Selection Model within PCA

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