House price forecasting using the multi-level modelling method in Sydney

Ge, XJ; Mangioni, V; Shi, S; Herath, S

House price forecasting using the multi-level modelling method in Sydney

Ge, XJ Mangioni, V Shi, S

Herath, S

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Publisher:: Emerald
Publication Type:: Journal Article
Citation:: International Journal of Housing Markets and Analysis, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Ge, XJ
dc.contributor.author	Mangioni, V
dc.contributor.author	Shi, S https://orcid.org/0000-0003-4188-5151
dc.contributor.author	Herath, S https://orcid.org/0000-0002-7958-3274
dc.date.accessioned	2022-12-05T02:07:33Z
dc.date.available	2022-12-05T02:07:33Z
dc.date.issued	2022-01-01
dc.identifier.citation	International Journal of Housing Markets and Analysis, 2022
dc.identifier.issn	1753-8270
dc.identifier.issn	1753-8289
dc.identifier.uri	http://hdl.handle.net/10453/164105
dc.description.abstract	Purpose: This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value. Design/methodology/approach: Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model. Findings: Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results. Research limitations/implications: It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions. Practical implications: While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security. Originality/value: The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.
dc.language	en
dc.publisher	Emerald
dc.relation	UrbanGrowth NSWUrbanGrowth
dc.relation.ispartof	International Journal of Housing Markets and Analysis
dc.relation.isbasedon	10.1108/IJHMA-06-2022-0083
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design, 14 Economics, 15 Commerce, Management, Tourism and Services
dc.title	House price forecasting using the multi-level modelling method in Sydney
dc.type	Journal Article
utslib.for	12 Built Environment and Design
utslib.for	14 Economics
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	open_access	*
dc.date.updated	2022-12-05T02:07:32Z
pubs.publication-status	Published

Abstract:

Purpose: This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value. Design/methodology/approach: Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model. Findings: Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results. Research limitations/implications: It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions. Practical implications: While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security. Originality/value: The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.

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http://hdl.handle.net/10453/164105