Abstract
The housing vacancy rate (HVR) is an important index in assessing the healthiness of residential real estate market. In China, it is hardly to take advantage of the basic data of real estate information due to the opaque of those data. In this paper, the HVR is estimated to two scales. At the grid level, urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method, and built-up areas in each pixel were extracted from the land-cover data. Then, the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions. At the administrative scale, the GCI (‘ghost city’ index) is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city. The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed. The following conclusions were drawn: vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development. Cities based on exhausted resources, some mountainous cities, and cities with relatively backward economic development more typically showed high levels of housing vacancy. The GCI of prefecture-level administrative units gradually declined from north to south, whereas the east-west distribution showed a parabolic shape. As city level decreased, the GCI registered a gradual upward trend. China’s urban housing vacancy can be divided into five categories: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment, social economic development level, and population density into consideration.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bentley G C, McCutcheon P, Cromley R G et al., 2015. Race, class, unemployment, and housing vacancies in Detroit: an empirical analysis. Urban Geography, 37(5): 785–800. doi: https://doi.org/10.1080/02723638.2015.1112642
Chen Z Q, Yu B L, Hu Y J et al., 2015. Estimating house vacancy rate in metropolitan areas using NPP-VIIRS nighttime light composite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): 2188–2197. doi: https://doi.org/10.1109/JSTARS.2015.2418201
CHFS, 2019. Analysis of Housing Vacancy in China’s Cities in 2017. Available at: https://chfs.swufe.edu.cn/thinktank/resultsreport.html?id=1664. Cited 26 June 2019. (in Chinese)
Chi G H, Liu Y, Wu H S, 2015. Ghost cities analysis based on positioning data in China. Computer ence, 68(12): 1150–1156. doi: https://doi.org/10.1136/thoraxjnl-2012-202917
Couch C, Cocks M, 2013. Housing vacancy and the shrinking city: trends and policies in the UK and the city of Liverpool. Housing Studies, 28(3): 499–519. doi: https://doi.org/10.1080/02673037.2013.760029
Du M Z, Wang L, Zou S Y et al., 2018. Modeling the census tract level housing vacancy rate with the Jilin1-03 satellite and other geospatial data. Remote Sensing, 10(12): 1920. doi: https://doi.org/10.3390/rs10121920
Gabriel S A, Nothaft F E, 1988. Rental housing markets and the natural vacancy rate. Real Estate Economics, 16(4): 419–429. doi: https://doi.org/10.1111/1540-6229.00465
Gabriel S A, Nothaft F E, 2001. Rental housing markets, the incidence and duration of vacancy, and the natural vacancy rate. Journal of Urban Economics, 49(1): 121–149. doi: https://doi.org/10.1006/juec.2000.2187
Gentili M, Hoekstra J, 2019. Houses without people and people without houses: a cultural and institutional exploration of an Italian paradox. Housing Studies, 34(3): 425–447. doi: https://doi.org/10.1080/02673037.2018.1447093
Hoekstra J, Vakili-Zad C, 2011. High vacancy rates and rising house prices: the Spanish paradox. Tijdschrift voor Economische en Sociale Geografie, 102(1): 55–71. doi: https://doi.org/10.1111/j.1467-9663.2009.00582.x
Jin X B, Long Y, Sun W et al., 2017. Evaluating cities’ vitality and identifying ghost cities in China with emerging geographical data. Cities, 63: 98–109. doi: https://doi.org/10.1016/j.cities.2017.01.002
Jing W, Gyourko J, Deng Y H, 2016. Evaluating the risk of Chinese housing markets: what we know and what we need to know. China Economic Review, 39: 91–114. doi: https://doi.org/10.1016/j.chieco.2016.03.008
Konomi S, Sasao T, Hosio S et al., 2019. Using ambient WiFi signals to find occupied and vacant houses in local communities. Journal of Ambient Intelligence and Humanized Computing, 10(2): 779–789. doi: https://doi.org/10.1007/s12652-018-0899-8
Li C, Ye J, Li S C et al., 2016. Study on radiometric intercalibration methods for DMSP-OLS night-time light imagery. International Journal of Remote Sensing, 37(16): 3675–3695. doi: https://doi.org/10.1080/01431161.2016.1201232
Li J T, Ji J Y, Guo H W et al., 2018. Research on the influence of real estate development on private investment: a case study of China. Sustainability, 10(8): 2659. doi: https://doi.org/10.3390/su10082659
Liang Q, 2013. Ministry of Land and Resources: urbanization scale should be controlled by ‘land use limit’. http://politics.people.com.cn/n/2013/0401/c70731-20981478.html. Cited 26 June 2019. (in Chinese)
Liu F, Zhang Z X, Wang X, 2016. Forms of urban expansion of Chinese municipalities and provincial capitals, 1970s–2013. Remote Sensing, 8(11): 930. doi: https://doi.org/10.3390/rs8110930
Lu H L, Zhang C R, Liu G F et al., 2018. Mapping China’s ghost cities through the combination of nighttime satellite data and daytime satellite data. Remote Sensing, 10(7): 1037. doi: https://doi.org/10.3390/rs10071037
Ma S, Yan W, Huang Y X et al., 2015. Vicarious calibration of S-NPP/VIIRS day-night band using deep convective clouds. Remote Sensing of Environment, 158: 42–55. doi: https://doi.org/10.1016/j.rse.2014.11.006
Ma X L, Tong X H, Liu S C et al., 2018. A multisource remotely sensed data oriented method for ‘ghost city’ phenomenon identification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7): 2310–2319. doi: https://doi.org/10.1109/JSTARS.2018.2824302
Malleson N, Andresen M A, 2015. The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science, 42(2): 112–121. doi: https://doi.org/10.1080/15230406.2014.905756
McLeod A I, Hipel K W, Bodo B A, 1991. Trend analysis methodology for water quality time series. Environmetrics, 2(2): 169–200. doi: https://doi.org/10.1002/env.3770020205
Mittal J, Kashyap A, 2015. Real estate market led land development strategies for regional economic corridors: a tale of two mega projects. Habitat International, 47: 205–217. doi: https://doi.org/10.1016/j.habitatint.2015.01.026
Molloy R, 2016. Long-term vacant housing in the United States. Regional Science and Urban Economics, 59: 118–129. doi: https://doi.org/10.1016/j.regsciurbeco.2016.06.002
Newman G, Lee R J, Gu D et al., 2019. Evaluating drivers of housing vacancy: a longitudinal analysis of large U.S. cities from 1960 to 2010. Journal of Housing and the Built Environment, 34(3): 807–827. doi: https://doi.org/10.1007/s10901-019-09684-w
Niu X, 2018. Estimating housing vacancy rate in Qingdao city with NPP-VIIRS nighttime light and geographical national conditions monitoring data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3. Beijing, China: ISPRS. doi: https://doi.org/10.5194/isprs-archives-XLII-3-1319-2018
Ortalo-Magné F, Rady S, 2006. Housing market dynamics: on the contribution of income shocks and credit constraints. The Review of Economic Studies, 73(2): 459–185. doi: https://doi.org/10.1111/j.1467-937x.2006.383_1.x
Sorace C, Hurst W, 2016. China’s phantom urbanisation and the pathology of ghost cities. Journal of Contemporary Asia, 46(2): 304–322. doi: https://doi.org/10.1080/00472336.2015.1115532
Tencent Real Estate Research Institute, 2015. Survey report on housing vacancy rate in China in 2015. Available at: https://ty.house.qq.com/a/20150604/038491.htm#p=4. Cited 2 July 2019. (in Chinese)
Vakili-Zad C, Hoekstra J, 2011. High dwelling vacancy rate and high prices of housing in Malta a mediterranean phenomenon. Journal of Housing and the Built Environment, 26(4): 441–455. doi: https://doi.org/10.1007/s10901-011-9232-y
Wang L Y, Fan H, Wang Y K, 2019. An estimation of housing vacancy rate using NPP-VIIRS night-time light data and OpenStreetMap data. International Journal of Remote Sensing, 40(22): 8566–8588. doi: https://doi.org/10.1080/01431161.2019.1615655
Wood G, Yates J, Reynolds M, 2006. Vacancy rates and low-rent housing: a panel data analysis. Journal of Housing and the Built Environment, 21(4): 441–458. doi: https://doi.org/10.1007/s10901-006-9059-0
Wu J S, He S B, Peng J et al., 2013. Intercalibration of DMSP-OLS night-time light data by the invariant region method. International Journal of Remote Sensing, 34(20): 7356–7368. doi: https://doi.org/10.1080/01431161.2013.820365
Xiao P F, Wang X H, Feng X Z et al., 2014. Detecting China’s urban expansion over the past three decades using nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10): 4095–4106. doi: https://doi.org/10.1109/JSTARS.2014.2302855
Yao Y L, Li Y, 2011. House vacancy at urban areas in China with nocturnal light data of DMSP-OLS. In: Proceedings of 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. Fuzhou, China: IEEE, 457–468. doi: https://doi.org/10.1109/ICSDM.2011.5969087
Yu L, Wang J, Clinton N et al., 2013. FROM-GC: 30 m global cropland extent derived through multisource data integration. International Journal of Digital Earth, 6(6): 521–533. doi: https://doi.org/10.1080/17538947.2013.822574
Zhang C C, Jia S, Yang R D, 2016. Housing affordability and housing vacancy in China: the role of income inequality. Journal of Housing Economics, 33: 4–14. doi: https://doi.org/10.1016/j.jhe.2016.05.005
Zheng Q M, Zeng Y, Deng J S et al., 2017. ‘Ghost cities’ identification using multi-source remote sensing datasets: a case study in Yangtze River Delta. Applied Geography, 80: 112–121. doi: https://doi.org/10.1016/j.apgeog.2017.02.004
Zhou Y Y, Smith S J, Elvidge C D et al., 2014. A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147: 173–185. doi: https://doi.org/10.1016/j.rse.2014.03.004
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item
Under the auspices of National Natural Science Foundation of China (No. 2071216, 41661025), Research Capacity Promotion Program for Young Teachers of Northwest Normal University (No. NWNU-LKQN-16-7)
Rights and permissions
About this article
Cite this article
Pan, J., Dong, L. Spatial Identification of Housing Vacancy in China. Chin. Geogr. Sci. 31, 359–375 (2021). https://doi.org/10.1007/s11769-020-1171-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11769-020-1171-7