The predictive performance of multi-level models of housing submarkets: a comparative analysis

Chris Leishman, Greg Costello, Steven Rowley, Craig Watkins

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Much of the housing sub-market literature has focused on establishing methods that allow the partitioning of data into distinct market segments. This paper seeks to move the focus on to the question of how best to model sub-markets once they have been identified. It focuses on evaluating the effectiveness of multilevel models as a technique for modelling sub-markets. The paper uses data on housing transactions from Perth, Western Australia, to develop and compare three competing sub-market modelling strategies. Model 1 consists of a city-wide 'benchmark'; model 2 provides a series of sub-market-specific hedonic estimates (this is the 'industry standard') and models 3 and 4 provide two variants on the multilevel model (differentiated by variation in the degrees of spatial granularity embedded in the model structure). The results suggest that the more granular multilevel specification enhances empirical performance and reduces the incidence of non-random spatial errors.

Original languageEnglish
Pages (from-to)1201-1220
Number of pages20
JournalUrban Studies
Volume50
Issue number6
Early online date29 Jan 2013
DOIs
Publication statusPublished - May 2013

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