The non-linear dynamics of South Australian regional housing markets: A machine learning approach

Ali Soltani, Chyi Lin Lee

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
75 Downloads (Pure)

Abstract

Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

Original languageEnglish
Article number103248
Number of pages21
JournalApplied Geography
Volume166
Early online date30 Mar 2024
DOIs
Publication statusPublished - May 2024

Keywords

  • Australia
  • Hedonic modelling
  • Housing price
  • Machine learning
  • Multi-level modelling
  • Regional housing

Fingerprint

Dive into the research topics of 'The non-linear dynamics of South Australian regional housing markets: A machine learning approach'. Together they form a unique fingerprint.

Cite this