Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Design
2.2.1. Research Idea
2.2.2. Selection of Indicators
Independent Variable
Explanatory Variables
Control Variables
2.3. Data and Data Sources
2.4. Methodology
2.4.1. Spatial Autocorrelation Analysis
2.4.2. Ordinary Least Squares
2.4.3. Spatial Regression Model
3. Results and Discussion
3.1. Spatial Difference Characteristics of Housing Vacancy
3.2. Spatial Difference Pattern of PA and FAR
3.3. Influence of PA and FAR on HVR
3.4. Discussion
4. Conclusions and Policy Implications
4.1. Conclusions
4.2. Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Jin, X.; Long, Y.; Sun, W.; Lu, Y.; Yang, X.; Tang, J. Evaluating cities’ vitality and identifying ghost cities in China with emerging geographical data. Cities 2017, 63, 98–109. [Google Scholar] [CrossRef]
- Williams, S.; Xu, W.; Bin Tan, S.; Foster, M.J.; Chen, C. Ghost cities of China: Identifying urban vacancy through social media data. Cities 2019, 94, 275–285. [Google Scholar] [CrossRef]
- Wang, J.; Yang, Z.; Qian, X. Driving factors of urban shrinkage: Examining the role of local industrial diversity. Cities 2020, 99, 102646. [Google Scholar] [CrossRef]
- Jeon, Y.M.; Kim, S.H. The causes and characteristics of housing abandonment in an inner-city neighborhood-focused on the Sungui-dong Area, Nam-gu, Incheon. J. Urban Des. Inst. Korea 2016, 17, 83–100. [Google Scholar]
- Nadalin, V.; Igliori, D. Empty spaces in the crowd. Residential vacancy in Sao Paulo’s city centre. Urban Stud. 2017, 54, 3085–3100. [Google Scholar] [CrossRef]
- Baba, H.; Hino, K. Factors and tendencies of housing abandonment: An analysis of a survey of vacant houses in Kawaguchi City, Saitama. Jpn. Arch. Rev. 2019, 2, 367–375. [Google Scholar] [CrossRef]
- Zhang, D.; Li, D.; Zhou, L.; Wang, J.; Ma, Y.; Yi, M. Quantitative study on geospacial factors affecting high-precision HVR. Bull. Surv. Mapp. 2022, 100–105. [Google Scholar]
- Baba, H.; Shimizu, C. The impact of apartment vacancies on nearby housing rents over multiple time periods: Application of smart meter data. Int. J. Hous. Mark. Anal. 2022. ahead of print. [Google Scholar] [CrossRef]
- Gu, D.; Newman, G.; Kim, J.-H.; Park, Y.; Lee, J. Neighborhood decline and mixed land uses: Mitigating housing abandonment in shrinking cities. Land Use Policy 2019, 83, 505–511. [Google Scholar] [CrossRef]
- Morckel, V.C. Spatial characteristics of housing abandonment. Appl. Geogr. 2014, 48, 8–16. [Google Scholar] [CrossRef]
- Sargent, J.D.; Bailey, A.; Simon, P.; Blake, M.; Dalton, M.A. Census tract analysis of lead exposure in Rhode Island children. Environ. Res. 1997, 74, 159–168. [Google Scholar] [CrossRef] [PubMed]
- In-A, J.; Woo, S.K. A study on the occurrence pattern of vacant spaces as the decline index in old hillside residential area. J. Archit. Inst. Korea Plan. Des. 2018, 34, 93–104. [Google Scholar]
- Kyoung, H.S. A study on spatial cluster and fixation process of the vacant houses in Iksan. Korea Spat. Plan. Rev. 2018, 97, 17–39. [Google Scholar] [CrossRef]
- Füss, R.; Koller, J.A.; Weigand, A. Determining land values from residential rents. Land 2021, 10, 336. [Google Scholar] [CrossRef]
- Cheng, J. Mathematical model and analysis of residential land leasing for Non-center Districts in Shanghai. Chin. J. Eng. Math. 2020, 37, 403–414. [Google Scholar]
- Takeda, Y.; Kono, T.; Zhang, Y. Welfare effects of floor area ratio regulation on landowners and residents with different levels of income. J. Hous. Econ. 2019, 46, 101656. [Google Scholar] [CrossRef]
- Li, X.; Zhang, D.; Tian, S.; Sun, H.; Wang, M. Spatial and temporal differences of urban residential quarter floor area ratio: A case study of four districts in Dalian. Sci. Geogr. Sin. 2018, 38, 531–538. [Google Scholar]
- Zeng, P.; Sun, Z.; Chen, Y.; Qiao, Z.; Cai, L. COVID-19: A comparative study of population aggregation patterns in the Central Urban Area of Tianjin, China. Int. J. Environ. Res. Public Health 2021, 18, 2135. [Google Scholar] [CrossRef]
- Li, X.M.; Zhu, J.L.; Wang, Y. Spatial differences of residential quarter floor area ratio: A case study of Dalian. Prog. Geogr. 2015, 34, 687–695. [Google Scholar]
- Zong, H.; Ji, X. Spatial characteristics and driving factors of expansion of residential land use in Chongqing Urban Area from 1999 to 2018. Sci. Geogr. Sin. 2021, 41, 1256–1265. [Google Scholar]
- Li, J.; Zheng, B.; Bedra, K.B.; Li, Z.; Chen, X. Effects of residential building height, density, and floor area ratios on indoor thermal environment in Singapore. J. Environ. Manag. 2022, 313, 114976. [Google Scholar] [CrossRef] [PubMed]
- Li, S.Y.; Wu, Z.F.; Li, B.Y.; Liu, Y.L.; Chen, X.Y. The spatial and temporal characteristics of residential floor area ratio in metropolitan at multi-scales based on Internet real estate data: Case study of Guangzhou. Geogr. Res. 2016, 35, 770–780. [Google Scholar]
- Wurm, M.; Goebel, J.; Wagner, G.G.; Weigand, M.; Dech, S.; Taubenböck, H. Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception. Environ. Plan. B Urban Anal. City Sci. 2019, 48, 265–279. [Google Scholar] [CrossRef]
- Cao, G.; Shi, Q.; Liu, T. An integrated model of urban spatial structure: Insights from the distribution of floor area ratio in a Chinese city. Appl. Geogr. 2016, 75, 116–126. [Google Scholar] [CrossRef]
- Barr, J.; Cohen, J.P. The floor area ratio gradient: New York City, 1890–2009. Reg. Sci. Urban Econ. 2014, 48, 110–119. [Google Scholar] [CrossRef]
- McMillen, D.P. A Companion to Urban Economics. Testing for Monocentricity; Arnott, R., McMillen, D.P., Eds.; Blackwell Publishing: Boston, MA, USA, 2006; pp. 128–140. [Google Scholar]
- Wei, W.; Ren, H.Y.; Song, Y.; Chen, T. Study on the influence of built environment of residential community on electricity consumption of different types of houses: The case of Ningbo. Urban Stud. 2021, 28, 107–114. [Google Scholar]
- Wang, Y.; Wu, K.; Qin, J.; Wang, C.; Zhang, H. Examining spatial heterogeneity effects of landscape and environment on the residential location choice of the highly educated population in Guangzhou, China. Sustainability 2020, 12, 3869. [Google Scholar] [CrossRef]
- Wu, K.; Wang, Y.; Zhang, H.; Liu, Y.; Zhang, Y. On innovation capitalization: Empirical evidence from Guangzhou, China. Habitat Int. 2021, 109, 102323. [Google Scholar] [CrossRef]
- Wu, K.; Zhang, H.; Wang, Y.; Wu, Q.; Ye, Y. Identify of the multiple types of commercial center in Guangzhou and its spatial pattern. Prog. Geogr. 2016, 35, 963–974. [Google Scholar]
- Lee, J.; Newman, G.; Lee, C. Predicting detached housing vacancy: A multilevel analysis. Sustainability 2022, 14, 922. [Google Scholar] [CrossRef]
- Wu, C.; Ye, X.; Du, Q.; Luo, P. Spatial effects of accessibility to parks on housing prices in Shenzhen, China. Habitat Int. 2017, 63, 45–54. [Google Scholar] [CrossRef]
- Yue, X.; Wang, Y.; Zhao, Y.; Zhang, H. Estimation of urban housing vacancy based on daytime housing exterior images—A case study of Guangzhou in China. ISPRS Int. J. Geo-Inf. 2022, 11, 349. [Google Scholar] [CrossRef]
- Gatrell, A.C. Autocorrelation in Spaces. Environ. Plan. A Econ. Space 1979, 11, 507–516. [Google Scholar] [CrossRef]
- Wang, Y.; Wu, K.; Zhao, Y.; Wang, C.; Zhang, H. Examining the effects of the built environment on housing rents in the Pearl River Delta of China. Appl. Spat. Anal. Policy 2021, 15, 289–313. [Google Scholar] [CrossRef]
- Anselin, L. Spatial Econometrics: Methods and Models; Springer: Dordrecht, The Netherlands, 1988. [Google Scholar] [CrossRef]
- Anselin, L.; Syabri, I.; Kho, Y. GeoDa: An introduction to spatial data analysis. Geogr. Anal. 2005, 38, 5–22. [Google Scholar] [CrossRef]
- Arbia, G. Spatial Econometrics: Statistical Foundations and Applications to Regional Economic Growth; Springer: Berlin/Heidelberg, Germany, 2006. [Google Scholar]
- Wooldridge, J.M. Introductory Econometrics: A Modern Approach, 6th ed.; Tsinghua University Press: Beijing, China, 2018; pp. 172–173. [Google Scholar]
- Bentley, G.C.; McCutcheon, P.; Cromley, R.G.; Hanink, D.M. Race, class, unemployment, and housing vacancies in Detroit: An empirical analysis. Urban Geogr. 2015, 37, 785–800. [Google Scholar] [CrossRef]
- Liu, Y.; Zhang, Y.; Sun, H.; Fu, H. Spatial-temporal differentiation and influence mechanism of housing vacancy in shrinking cities: Based on the perspective of residential electricity consumption. Sci. Geogr. Sin. 2021, 41, 2087–2095. [Google Scholar]
- Ely, T.L.; Teske, P. Implications of public school choice for residential location decisions. Urban Aff. Rev. 2014, 51, 175–204. [Google Scholar] [CrossRef]
- Chu, Y.-L.; Deng, Y.; Liu, R. Impacts of new light rail transit service on riders’ residential relocation decisions. J. Public Transp. 2017, 20, 152–165. [Google Scholar] [CrossRef]
- Kim, H.N.; Boxall, P.C.; Adamowicz, W. Analysis of the economic impact of water management policy on residential prices: Modifying choice set formation in a discrete house choice analysis. J. Choice Model. 2019, 33, 100148. [Google Scholar] [CrossRef]
Variable | Evaluation Method or Index Composition | Expected Impact Direction |
---|---|---|
Dependent variable | ||
Housing vacancy rate (HVR) | Housing vacancy rate of residential quarters | |
Explanatory Variables | ||
Plot area (PA) | The land area of the residential quarter | + |
Floor area ratio (FAR) | The floor area of the residential quarter | + |
Control Variables—Building and Location Characteristics | ||
Office accessibility | POI data were generated for kernel density and positive standard deviation value examination of the kernel density distribution of office space divided into five levels: residential quarter located outside the mean (1) or residential quarter located at the mean–1 sd (3), 1–2 sd (5), 2–3 sd (7), or 3 sd (9) | − |
Basic educational convenience | There are provincial key primary schools with-in the community (9 points), municipal key primary schools within the community (7 points), other communities without provincial key primary schools within 500 m from provincial key primary schools (5 points), other communities without provincial key primary schools within 500 m from provincial and municipal ordinary primary schools (3 points), and other communities without provincial key primary schools within 500 m from all primary schools (1 point). | − |
Business services convenience | Performed kernel density analysis and grading using a standard deviation mean plane. Scores were assigned in the same way as for office accessibility | − |
Road density | Calculate the road density of the subdistrict where the residential quarter is located. | − |
Waterfront accessibility | Calculate the nearest distance (m) from the residential area to the mainstream of the PearlRiver. | − |
Distance from the CBD | Distance from the Guangzhou International Finance Center (IFC) (km). | + |
Variable | Tolerance | VIF |
---|---|---|
Plot area (PA) | 0.059 | 1.629 |
Floor area ratio (FAR) | 0.288 | 1.518 |
Office accessibility | 0.148 | 6.721 |
Basic educational convenience | 0.600 | 1.650 |
Business services convenience | 0.195 | 5.097 |
Road density | 0.373 | 2.673 |
Waterfront accessibility | 0.804 | 1.237 |
Distance from the CBD | 0.413 | 2.350 |
Model | R2 | AIC | Log Likelihood | LM | Robust LM |
---|---|---|---|---|---|
OLS | 0.5186 | 536.154 | −259.077 | — | — |
SLM | 0.5444 | 528.396 | −254.198 | 0.0047 | 0.0085 |
SEM | 0.5366 | 531.221 | −256.610 | 0.2070 | 0.4642 |
Variables | Coefficient | Std. Error | t/z-Value | p |
---|---|---|---|---|
W_Y | 0.4080 *** | 0.1170 | 3.4862 | 0.0005 |
Constant | 2.2980 ** | 1.0021 | 2.2933 | 0.0218 |
Plot area (PA) | 0.0935 ** | 0.0447 | 2.0914 | 0.0365 |
Floor area ratio (FAR) | 0.2238 ** | 0.1135 | 1.9724 | 0.0486 |
Office accessibility | −0.2833 | 0.1824 | −1.5531 | 0.1204 |
Basic educational convenience | −0.2874 *** | 0.0935 | −3.0726 | 0.0021 |
Business services convenience | 0.2126 | 0.1662 | 1.2793 | 0.2008 |
Road density | −0.6601 *** | 0.1927 | −3.4249 | 0.0006 |
Waterfront accessibility | −0.1006 * | 0.0514 | −1.9588 | 0.0501 |
Distance from the CBD | 0.1644 | 0.1276 | 1.2884 | 0.1976 |
R2: 0.5444; AIC: 528.396; Log likelihood: −254.198 |
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Yue, X.; Wang, Y.; Zhang, H. Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China. Buildings 2022, 12, 1197. https://doi.org/10.3390/buildings12081197
Yue X, Wang Y, Zhang H. Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China. Buildings. 2022; 12(8):1197. https://doi.org/10.3390/buildings12081197
Chicago/Turabian StyleYue, Xiaoli, Yang Wang, and Hong’ou Zhang. 2022. "Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China" Buildings 12, no. 8: 1197. https://doi.org/10.3390/buildings12081197