Housing price prediction using machine learning algorithms

the case of Melbourne city, Australia

The Danh Phan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 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 languageEnglish
Title of host publicationProceedings International Conference on Machine Learning and Data Engineering
Subtitle of host publicationiCMLDE 2018
EditorsPhill Kyu Rhee, Daniel Howard, Md Rezaul Bashar
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages35-42
Number of pages8
ISBN (Electronic)9781728104041
ISBN (Print)9781728104058
DOIs
Publication statusPublished - 2019
EventInternational Conference on Machine Learning and Data Engineering (iCMLDE) - Sydney, Australia
Duration: 3 Dec 20187 Dec 2018

Conference

ConferenceInternational Conference on Machine Learning and Data Engineering (iCMLDE)
CountryAustralia
CitySydney
Period3/12/187/12/18

Keywords

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

Cite this

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