Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data
In recent years, the phenomenon of housing vacancy rate (HVR) has attracted considerable attention, especially concerning unjustified expansions of Chinese cities. The aforementioned trend is disadvantageous in that it will ultimately lead to tremendous wastage of valuable land that could otherwise...
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5104578 |
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| author | Zhiru Tan Donglan Wei Zixu Yin |
| author_facet | Zhiru Tan Donglan Wei Zixu Yin |
| author_sort | Zhiru Tan |
| collection | DOAJ |
| description | In recent years, the phenomenon of housing vacancy rate (HVR) has attracted considerable attention, especially concerning unjustified expansions of Chinese cities. The aforementioned trend is disadvantageous in that it will ultimately lead to tremendous wastage of valuable land that could otherwise be more productively utilized. Consequently, the methods for accurately determining the HVR are of great importance. Based on nighttime light data from the Luojia 1-01 nighttime light imagery provided by Wuhan University in June 2018 and the building data obtained from the Resources and Environmental Sciences Data Center, we estimated the HVRs of 49 cities in China by determining the building areas and considering the floor height. The results revealed that (1) the lowest (15%) and highest (24.3%) HVRs occur in Shenzhen and Nanning, respectively. (2) The urban HVR correlates positively with the three production structures (0.3143) but is significantly negatively correlated with population (0.3841), GDP (0.6139), and urban average housing prices (0.5083). (3) The first-tier, new first-tier, and second-tier cities showed the lowest (16.9%), relatively concentrated (20.5%), and highest (21.3%) average vacancy rates, respectively. (4) The vacancy rate is relatively low in the eastern coastal areas, whereas high in the northeast and western inland areas. The proposed method can help urban planners by identifying vacant areas and providing building information. |
| format | Article |
| id | doaj-art-21d01fd659ef426bad852c02f0f47668 |
| institution | Kabale University |
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| language | English |
| publishDate | 2020-01-01 |
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| series | Complexity |
| spelling | doaj-art-21d01fd659ef426bad852c02f0f476682025-02-03T01:03:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/51045785104578Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light DataZhiru Tan0Donglan Wei1Zixu Yin2School of Geographical, Liaoning Normal University, Dalian 116029, Liaoning, ChinaSchool of Geographical, Liaoning Normal University, Dalian 116029, Liaoning, ChinaSchool of Geographical, Liaoning Normal University, Dalian 116029, Liaoning, ChinaIn recent years, the phenomenon of housing vacancy rate (HVR) has attracted considerable attention, especially concerning unjustified expansions of Chinese cities. The aforementioned trend is disadvantageous in that it will ultimately lead to tremendous wastage of valuable land that could otherwise be more productively utilized. Consequently, the methods for accurately determining the HVR are of great importance. Based on nighttime light data from the Luojia 1-01 nighttime light imagery provided by Wuhan University in June 2018 and the building data obtained from the Resources and Environmental Sciences Data Center, we estimated the HVRs of 49 cities in China by determining the building areas and considering the floor height. The results revealed that (1) the lowest (15%) and highest (24.3%) HVRs occur in Shenzhen and Nanning, respectively. (2) The urban HVR correlates positively with the three production structures (0.3143) but is significantly negatively correlated with population (0.3841), GDP (0.6139), and urban average housing prices (0.5083). (3) The first-tier, new first-tier, and second-tier cities showed the lowest (16.9%), relatively concentrated (20.5%), and highest (21.3%) average vacancy rates, respectively. (4) The vacancy rate is relatively low in the eastern coastal areas, whereas high in the northeast and western inland areas. The proposed method can help urban planners by identifying vacant areas and providing building information.http://dx.doi.org/10.1155/2020/5104578 |
| spellingShingle | Zhiru Tan Donglan Wei Zixu Yin Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data Complexity |
| title | Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data |
| title_full | Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data |
| title_fullStr | Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data |
| title_full_unstemmed | Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data |
| title_short | Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data |
| title_sort | housing vacancy rate in major cities in china perspectives from nighttime light data |
| url | http://dx.doi.org/10.1155/2020/5104578 |
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