Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia

The Danh Phan

doi:10.1109/iCMLDE.2018.00017

Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia

The Danh Phan

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

69 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings International Conference on Machine Learning and Data Engineering
Subtitle of host publication	iCMLDE 2018
Editors	Phill Kyu Rhee, Daniel Howard, Md Rezaul Bashar
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	35-42
Number of pages	8
ISBN (Electronic)	9781728104041
ISBN (Print)	9781728104058
DOIs	https://doi.org/10.1109/iCMLDE.2018.00017
Publication status	Published - 2019
Event	International Conference on Machine Learning and Data Engineering (iCMLDE) - Sydney, Australia Duration: 3 Dec 2018 → 7 Dec 2018

Conference

Conference	International Conference on Machine Learning and Data Engineering (iCMLDE)
Country/Territory	Australia
City	Sydney
Period	3/12/18 → 7/12/18

Keywords

House price prediction
Regression Trees
Neural Network
Support Vector Machine
Stepwise
Principal Component Analysis
MODEL
SVM

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10.1109/iCMLDE.2018.00017

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Phan, T. D. (2019). Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia. In P. K. Rhee, D. Howard, & M. R. Bashar (Eds.), Proceedings International Conference on Machine Learning and Data Engineering: iCMLDE 2018 (pp. 35-42). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/iCMLDE.2018.00017

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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.",

keywords = "House price prediction, Regression Trees, Neural Network, Support Vector Machine, Stepwise, Principal Component Analysis, MODEL, SVM",

author = "Phan, {The Danh}",

year = "2019",

doi = "10.1109/iCMLDE.2018.00017",

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booktitle = "Proceedings International Conference on Machine Learning and Data Engineering",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "International Conference on Machine Learning and Data Engineering (iCMLDE) ; Conference date: 03-12-2018 Through 07-12-2018",

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Phan, TD 2019, Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia. in PK Rhee, D Howard & MR Bashar (eds), Proceedings International Conference on Machine Learning and Data Engineering: iCMLDE 2018. Institute of Electrical and Electronics Engineers (IEEE), pp. 35-42, International Conference on Machine Learning and Data Engineering (iCMLDE), Sydney, Australia, 3/12/18. https://doi.org/10.1109/iCMLDE.2018.00017

Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia. / Phan, The Danh.
Proceedings International Conference on Machine Learning and Data Engineering: iCMLDE 2018. ed. / Phill Kyu Rhee; Daniel Howard; Md Rezaul Bashar. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 35-42.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia

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Keywords

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