Examining the spatial and non-spatial linkages between suburban housing markets

Morteza Moallemi, Daniel Melser, Ashton de Silva, Xiaoyan Chen

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Purpose: The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level. Design/methodology/approach: The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018. Findings: The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered. Originality/value: The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

Original languageEnglish
Number of pages25
JournalInternational Journal of Housing Markets and Analysis
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Higher-order spatial autoregression
  • Housing prices
  • Intra-city housing market
  • Melbourne
  • Socioeconomic characteristics
  • Suburban housing market

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