Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China
Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity...
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| Veröffentlicht in: | Chinese geographical science 2023-04, Vol.33 (2), p.233-249 |
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| creator | Wang, Yang Wu, Kangmin Zhang, Hong’ou Liu, Yi Yue, Xiaoli |
| description | Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations. The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development. |
| doi_str_mv | 10.1007/s11769-023-1341-5 |
| format | Article |
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Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations. The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.</description><identifier>ISSN: 1002-0063</identifier><identifier>EISSN: 1993-064X</identifier><identifier>DOI: 10.1007/s11769-023-1341-5</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Built environment ; Case studies ; Development strategies ; Earth and Environmental Science ; Geography ; Heterogeneity ; Housing ; Housing policy ; Housing prices ; Metropolitan areas ; Natural environment ; Science ; Urban areas ; Urban development ; Urban environments</subject><ispartof>Chinese geographical science, 2023-04, Vol.33 (2), p.233-249</ispartof><rights>Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023</rights><rights>Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-f05411dd4a1b67d5e7652ade4d47415186af2a252b94683fe7d77cc60e35aaf73</citedby><cites>FETCH-LOGICAL-c359t-f05411dd4a1b67d5e7652ade4d47415186af2a252b94683fe7d77cc60e35aaf73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11769-023-1341-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11769-023-1341-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Wu, Kangmin</creatorcontrib><creatorcontrib>Zhang, Hong’ou</creatorcontrib><creatorcontrib>Liu, Yi</creatorcontrib><creatorcontrib>Yue, Xiaoli</creatorcontrib><title>Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China</title><title>Chinese geographical science</title><addtitle>Chin. Geogr. Sci</addtitle><description>Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations. The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.</description><subject>Built environment</subject><subject>Case studies</subject><subject>Development strategies</subject><subject>Earth and Environmental Science</subject><subject>Geography</subject><subject>Heterogeneity</subject><subject>Housing</subject><subject>Housing policy</subject><subject>Housing prices</subject><subject>Metropolitan areas</subject><subject>Natural environment</subject><subject>Science</subject><subject>Urban areas</subject><subject>Urban development</subject><subject>Urban environments</subject><issn>1002-0063</issn><issn>1993-064X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kc1uEzEUhUeISpSWB2B3JbaY-necYVfSNFPRqkgUiZ3leq4nrho72BOkPAzvikepxIrNvWfxnXMWp2neM_qJUaovCmO67QjlgjAhGVGvmlPWdYLQVv58XTWlnFDaijfN21KeKBWd6NRp8-dmwDgFfwhxhO87OwX7DD1OmNOIEcN0gBBh2iCsvEc3FUge-jBuyAO6DVyHvIUrjGUGU4Q-7cuc9C0Hh-UzrH6Hmu8QfE5bWO9tHIcUR9LXA19ndWedTbDOaGsnfLEHuKz6Iyw3Idrz5sTb54LvXv5Z8-N69bDsye39-mZ5eUucUN1EPFWSsWGQlj22elCoW8XtgHKQWjLFFq313HLFHzvZLoRHPWjtXEtRKGu9FmfNh2PuLqdfeyyTeUr7HGul4XrRKS05k5ViR8rlVEpGb3Y5bG0-GEbNvII5rmDqCmZewajq4UdPqWwcMf9L_r_pLyZ9isw</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Wang, Yang</creator><creator>Wu, Kangmin</creator><creator>Zhang, Hong’ou</creator><creator>Liu, Yi</creator><creator>Yue, Xiaoli</creator><general>Science Press</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>M2P</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20230401</creationdate><title>Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China</title><author>Wang, Yang ; Wu, Kangmin ; Zhang, Hong’ou ; Liu, Yi ; Yue, Xiaoli</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-f05411dd4a1b67d5e7652ade4d47415186af2a252b94683fe7d77cc60e35aaf73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Built environment</topic><topic>Case studies</topic><topic>Development strategies</topic><topic>Earth and Environmental Science</topic><topic>Geography</topic><topic>Heterogeneity</topic><topic>Housing</topic><topic>Housing policy</topic><topic>Housing prices</topic><topic>Metropolitan areas</topic><topic>Natural environment</topic><topic>Science</topic><topic>Urban areas</topic><topic>Urban development</topic><topic>Urban environments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Wu, Kangmin</creatorcontrib><creatorcontrib>Zhang, Hong’ou</creatorcontrib><creatorcontrib>Liu, Yi</creatorcontrib><creatorcontrib>Yue, Xiaoli</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Chinese geographical science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yang</au><au>Wu, Kangmin</au><au>Zhang, Hong’ou</au><au>Liu, Yi</au><au>Yue, Xiaoli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China</atitle><jtitle>Chinese geographical science</jtitle><stitle>Chin. Geogr. Sci</stitle><date>2023-04-01</date><risdate>2023</risdate><volume>33</volume><issue>2</issue><spage>233</spage><epage>249</epage><pages>233-249</pages><issn>1002-0063</issn><eissn>1993-064X</eissn><abstract>Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. 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| source | Alma/SFX Local Collection; SpringerNature Complete Journals |
| subjects | Built environment Case studies Development strategies Earth and Environmental Science Geography Heterogeneity Housing Housing policy Housing prices Metropolitan areas Natural environment Science Urban areas Urban development Urban environments |
| title | Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China |
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