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A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities
Purpose – A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process. Design/methodology/approach – Seemingly unrelated regression estimators are adapted to dea...
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| Published in: | International journal of housing markets and analysis 2013-01, Vol.6 (4), p.405-421 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c399t-8798de7a28a7fa8b614f12ce283173a52afcc582cbd7c27e0f1a413f765e44a73 |
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| cites | cdi_FETCH-LOGICAL-c399t-8798de7a28a7fa8b614f12ce283173a52afcc582cbd7c27e0f1a413f765e44a73 |
| container_end_page | 421 |
| container_issue | 4 |
| container_start_page | 405 |
| container_title | International journal of housing markets and analysis |
| container_volume | 6 |
| creator | Ma, Le Liu, Chunlu |
| description | Purpose
– A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.
Design/methodology/approach
– Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.
Findings
– The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.
Originality/value
– This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market. |
| doi_str_mv | 10.1108/IJHMA-03-2013-0021 |
| format | article |
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– A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.
Design/methodology/approach
– Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.
Findings
– The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.
Originality/value
– This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.</description><identifier>ISSN: 1753-8270</identifier><identifier>EISSN: 1753-8289</identifier><identifier>DOI: 10.1108/IJHMA-03-2013-0021</identifier><language>eng</language><publisher>Bingley: Emerald Group Publishing Limited</publisher><subject>Causality ; Cities ; Consumer spending ; Economic models ; Equilibrium ; Error correction & detection ; Heterogeneity ; Housing ; Housing markets ; Housing prices ; Influence ; Price levels ; Property management & built environment ; Real estate & property ; Regions ; Studies</subject><ispartof>International journal of housing markets and analysis, 2013-01, Vol.6 (4), p.405-421</ispartof><rights>Emerald Group Publishing Limited</rights><rights>Copyright Emerald Group Publishing Limited 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-8798de7a28a7fa8b614f12ce283173a52afcc582cbd7c27e0f1a413f765e44a73</citedby><cites>FETCH-LOGICAL-c399t-8798de7a28a7fa8b614f12ce283173a52afcc582cbd7c27e0f1a413f765e44a73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1432236573/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1432236573?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11667,21366,21373,27901,27902,33588,33962,36037,43709,43924,44339,73964,74211,74638</link.rule.ids></links><search><creatorcontrib>Ma, Le</creatorcontrib><creatorcontrib>Liu, Chunlu</creatorcontrib><title>A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities</title><title>International journal of housing markets and analysis</title><description>Purpose
– A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.
Design/methodology/approach
– Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.
Findings
– The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.
Originality/value
– This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.</description><subject>Causality</subject><subject>Cities</subject><subject>Consumer spending</subject><subject>Economic models</subject><subject>Equilibrium</subject><subject>Error correction & detection</subject><subject>Heterogeneity</subject><subject>Housing</subject><subject>Housing markets</subject><subject>Housing prices</subject><subject>Influence</subject><subject>Price levels</subject><subject>Property management & built environment</subject><subject>Real estate & property</subject><subject>Regions</subject><subject>Studies</subject><issn>1753-8270</issn><issn>1753-8289</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>ALSLI</sourceid><sourceid>DPSOV</sourceid><sourceid>M0C</sourceid><sourceid>M2L</sourceid><recordid>eNptkc9KAzEQxhdRsFZfwFPA82r-7G7SYylqKxUveg7TdNambpM1yYI-gO_tbiuC4GmG4ffN8H2TZZeMXjNG1c3iYf44zanIOWUip5Szo2zEZClyxdXk-LeX9DQ7i3FLaaVKxUfZ15S04LAhGIIPxPgQ0CTrHYG2DR7MhiRP8KNtfEASW0gWmgPWwJ7rRwmDi8TXJG2QrP3OOnCJbHwXrXslOwhvmIh1ZNrFFKCx4IiB1qZhk00W43l2UkMT8eKnjrOXu9vn2TxfPt0vZtNlbsRkknIlJ2qNErgCWYNaVayoGTfIlWBSQMmhNqa3ZVZrabhEWjMomKhlVWJRgBTj7Oqwt7f23mFMeuu74PqTmhWCc1GVUvQUP1Am-BgD1roNtnfxqRnVQ9x6H7emQg9x6yHuXsQOItxh73H9v-bPi8Q3ghKEhA</recordid><startdate>20130101</startdate><enddate>20130101</enddate><creator>Ma, Le</creator><creator>Liu, Chunlu</creator><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DPSOV</scope><scope>DWQXO</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K6~</scope><scope>KC-</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>M0C</scope><scope>M2L</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope></search><sort><creationdate>20130101</creationdate><title>A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities</title><author>Ma, Le ; Liu, Chunlu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-8798de7a28a7fa8b614f12ce283173a52afcc582cbd7c27e0f1a413f765e44a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Causality</topic><topic>Cities</topic><topic>Consumer spending</topic><topic>Economic models</topic><topic>Equilibrium</topic><topic>Error correction & detection</topic><topic>Heterogeneity</topic><topic>Housing</topic><topic>Housing markets</topic><topic>Housing prices</topic><topic>Influence</topic><topic>Price levels</topic><topic>Property management & built environment</topic><topic>Real estate & property</topic><topic>Regions</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Le</creatorcontrib><creatorcontrib>Liu, Chunlu</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Politics Collection</collection><collection>ProQuest Central</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Politics Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>Political Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of housing markets and analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ma, Le</au><au>Liu, Chunlu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities</atitle><jtitle>International journal of housing markets and analysis</jtitle><date>2013-01-01</date><risdate>2013</risdate><volume>6</volume><issue>4</issue><spage>405</spage><epage>421</epage><pages>405-421</pages><issn>1753-8270</issn><eissn>1753-8289</eissn><abstract>Purpose
– A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.
Design/methodology/approach
– Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.
Findings
– The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.
Originality/value
– This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.</abstract><cop>Bingley</cop><pub>Emerald Group Publishing Limited</pub><doi>10.1108/IJHMA-03-2013-0021</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1753-8270 |
| ispartof | International journal of housing markets and analysis, 2013-01, Vol.6 (4), p.405-421 |
| issn | 1753-8270 1753-8289 |
| language | eng |
| recordid | cdi_emerald_primary_10_1108_IJHMA-03-2013-0021 |
| source | Social Science Premium Collection; ABI/INFORM Global; Politics Collection; Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list) |
| subjects | Causality Cities Consumer spending Economic models Equilibrium Error correction & detection Heterogeneity Housing Housing markets Housing prices Influence Price levels Property management & built environment Real estate & property Regions Studies |
| title | A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities |
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