Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11146-005-1370-0
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dc.titleA spatio-temporal autoregressive model for multi-unit residential market analysis
dc.contributor.authorSun, H.
dc.contributor.authorTu, Y.
dc.contributor.authorYu, S.-M.
dc.date.accessioned2013-10-14T05:12:40Z
dc.date.available2013-10-14T05:12:40Z
dc.date.issued2005
dc.identifier.citationSun, H., Tu, Y., Yu, S.-M. (2005). A spatio-temporal autoregressive model for multi-unit residential market analysis. Journal of Real Estate Finance and Economics 31 (2) : 155-187. ScholarBank@NUS Repository. https://doi.org/10.1007/s11146-005-1370-0
dc.identifier.issn08955638
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/46285
dc.description.abstractBy splitting the spatial effects into building and neighborhood effects, this paper develops a two order spatio-temporal autoregressive model to deal with both the spatio-temporal autocorrelations and the heteroscedasticity problem arising from the nature of multi-unit residential real estate data. The empirical results based on 54,282 condominium transactions in Singapore between 1990 and 1999 show that in the multi-unit residential market, a two order spatio-temporal autoregressive model incorporates more spatial information into the model, thus outperforming the models originally developed in the market for single-family homes. This implies that the specification of a spatio-temporal model should consider the physical market structure as it affects the spatial process. It is found that the Bayesian estimation method can produce more robust coefficients by efficiently detecting and correcting heteroscedasticity, indicating that the Bayesian estimation method is more suitable for estimating a real estate hedonic model than the conventional OLS estimation. It is also found that there is a trade off between the heteroscedastic robustness and the incorporation of spatial information into the model estimation. The model is then used to construct building-specific price indices. The results show that the price indices for different condominiums and the buildings within a condominium do behave differently, especially when compared with the aggregate market indices. © 2005 Springer Science + Business Media, Inc.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s11146-005-1370-0
dc.sourceScopus
dc.subjectBayesian
dc.subjectGibbs Sampling
dc.subjectHeteroscedasticity
dc.subjectSingapore condominium market
dc.subjectSpatio-temporal autocorrelation
dc.subjectSpatio-temporal model
dc.typeConference Paper
dc.contributor.departmentREAL ESTATE
dc.description.doi10.1007/s11146-005-1370-0
dc.description.sourcetitleJournal of Real Estate Finance and Economics
dc.description.volume31
dc.description.issue2
dc.description.page155-187
dc.identifier.isiut000231157000004
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