Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/95918
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dc.contributorDepartment of Building and Real Estateen_US
dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorHui, ECMen_US
dc.creatorLiang, Cen_US
dc.creatorZhong, Jen_US
dc.creatorIp, WCen_US
dc.date.accessioned2022-10-26T01:09:26Z-
dc.date.available2022-10-26T01:09:26Z-
dc.identifier.issn0197-3975en_US
dc.identifier.urihttp://hdl.handle.net/10397/95918-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2016 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Hui, E. C., Liang, C., Zhong, J., & Ip, W. C. (2016). Capture the abrupt changes in Asian residential property markets. Habitat International, 56, 235-244 is available at https://doi.org/10.1016/j.habitatint.2016.06.005.en_US
dc.subjectCUSUM testen_US
dc.subjectEconomic eventsen_US
dc.subjectEmerging marketen_US
dc.subjectJump pointen_US
dc.subjectProperty marketen_US
dc.subjectWavelet analysisen_US
dc.titleCapture the abrupt changes in Asian residential property marketsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage235en_US
dc.identifier.epage244en_US
dc.identifier.volume56en_US
dc.identifier.doi10.1016/j.habitatint.2016.06.005en_US
dcterms.abstractIn this paper, studies on the real estate markets mainly focused on the relationship between abrupt change points and corresponding political issues and economic collapse. Within the past statistical framework, change-point detection technique was widely considered based on large and long data sets. Few studies considered the situation where a limited size of time-series data sets is available in the real estate markets. To fill in this gap, the wavelet analysis with minimax threshold is introduced in this paper. By comparing Daubechies LA(8), wavelet analysis with minimax threshold is a versatile and powerful approach to the analysis of residential data as they are flexible in their function form and provide a robust computational method even with a small sample size. The detected change points reflect some significant political issues and economic collapses. It can be shown from the empirical result that a "diffusion relationship" happened from one location to another.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationHabitat international, Aug. 2016, v. 56, p. 235-244en_US
dcterms.isPartOfHabitat internationalen_US
dcterms.issued2016-08-
dc.identifier.scopus2-s2.0-84974678537-
dc.identifier.eissn1873-5428en_US
dc.description.validate202210 bcwwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberBRE-1081-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC GRF (B-Q42Q/PolyU 152059/14E); and PolyU Research Grants (G-UA6V and G-YBJL)en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6650364-
dc.description.oaCategoryGreen (AAM)en_US
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