Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Index System Construction
- (1)
- Real estate price (RSP): Real estate price is the explained variance obtained by dividing the sales volume of commercial housing in each city by the sales area of commercial housing in the year. To reduce the impact of the model on heteroscedasticity, logarithms were used for processing.
- (2)
- Infrastructure development level (IDL): This is weighted using the improved entropy method.
- (3)
- Other control variables. Industrial structure (IS): As upgrading industrial structure is closely related to urban housing prices [56], this study adopts the ratio of the output value of secondary and tertiary industries to the GDP of the current year to represent the change in industrial structure. City size (CZ): Population density was selected as the measurement index of city size considering two significant characteristics of large differences in urban areas and significant differences in population size. Land transfer price (LTP): This indicator reflects the cost of real-estate land development. It is calculated based on the amount of land concessions and the area of land concessions in each city in the current year and is taken in logarithmic form. Per capita disposable income of urban residents (INC): This variable measures the impact of urban residents’ income on real estate prices from the perspective of demand and reflects residents’ purchasing ability in logarithmic form. Credit scale (CS): Credit means can directly regulate the demand and supply of the real estate market [57]. It is usually measured by the ratio of year-end loans of financial institutions in cities to the GDP of the year.
2.3. Data Source and Processing
2.3.1. Data Source
2.3.2. Improved Entropy Method
- (1)
- Set r as years, n as cities and m as indicators, and is the j-th index value of city i in the t year.
- (2)
- Index standardization processing: All indexes need to be standardized science different indexes have different dimensions and units.
2.3.3. Spatial Correlation Test
2.3.4. Construction of Spatial Weight Matrices
2.4. Data Model
2.4.1. Spatial Panel Model
2.4.2. Spatial Total Effect Decomposition
3. Results
3.1. Spatial-Temporal Evolution of Urban Infrastructure Development in the Yangtze River Delta
3.2. Spatial Autocorrelation Test
3.2.1. Global Spatial Autocorrelation Test
3.2.2. Local Spatial Autocorrelation Tests
3.3. Spatial Effect Test
3.4. Analysis of the Model Regression Effect
- (1)
- The first column is individual fixed-effect regression and the second column is the difference GMM method that considers dynamic characteristics of real estate prices. No significant difference was found between regression results in the two methods, which indicates that overall regression results qualified. Urban infrastructure development can significantly promote an increase in real estate prices. Among controlled variables, real estate price in the lagging phase has a significant impact on the current housing price. Considering the regression result of difference GMM method as an example, housing prices increase by 1% in the previous period and by 0.407% in the current period.
- (2)
- From regression results of the spatial panel model, the spatial autoregressive coefficients in the adjacency, geographic, and nested matrices are all significant. This confirms that real estate prices within urban agglomerations have evident spatial spillover effects. Additionally, the autocorrelation coefficient in the nested matrix model with the introduction of economic factors was the largest, at 0.530, followed by the geographical matrix. Moreover, the regression coefficient of the adjacency matrix is the smallest. The degree to which real estate prices among cities influence each other will be deeper if geographical and economic factors are fully considered. The close exchange of economic activities among cities with close geographical distance will drive the speed of capital circulation and increase industrial transaction frequency. When coactivity of the real estate market in close areas is enhanced, housing prices of the surrounding cities will promote significant fluctuations in housing prices in the city.
- (3)
- Regression results between the panel data and the spatial panel models contain several differences. By comparing the regression results of the fixed effect and spatial panel models in the geographical matrix, the regression coefficient of the urban infrastructure development level decreased from 0.641 to 0.612, and other variables also decreased to different degrees. Moreover, the panel data model does not consider the spatial spillover of variables, which includes the influence of explanatory variables in the surrounding areas on local housing prices.
- (4)
- When the lag term of the variables was included in the regression equation of the spatial econometric model, the model’s regression coefficient did not accurately reflect the influence of controlled variables on dependent variables. Despite this, correlations can be drawn between urban infrastructure development and housing prices. In both the geographical and nested matrices, the regression coefficients of the infrastructure development level are significantly positive, which indicates that urban infrastructure development can significantly promote the increase of local real estate prices, considering the geographical distance and spatial attributes of economic exchange among cities.
3.5. Direct Effects and Indirect Effects
3.5.1. The Analysis of Direct Effects
3.5.2. The Analysis of Indirect Effects
3.6. Analysis of Regional Heterogeneity
- (1)
- In the eastern region, spatial autoregressive coefficients of urban real estate prices are not significant except for the adjacency matrix. In the central region, spatial autoregressive coefficients of the three matrices were significant. In nested matrix, the spatial autoregression coefficient reached its maximum (0.350). This is because spatial distance between cities in the east is relatively greater and housing prices of some cities are too high owing to urban policies. For example, the correlation of the urban real estate market is weak owing to the higher housing price of Wenzhou in Zhejiang Province compared with surrounding cities. Similarly, central region cities, which are geographically closer, belong to the same province. Hence, their real estate market regulation policies are highly consistent, resulting in stronger mutual influence among real estate prices.
- (2)
- The direct and indirect effects of urban infrastructure development on real estate prices in eastern China are significantly positive. The degree of impact is higher than that of the entire Yangtze River Delta’s urban agglomeration. However, this effect is not significant in Central China. The amount of infrastructure investment in eastern China increased, and infrastructure development was rapid from 2006 to 2018, which had a strong driving effect on real estate prices. However, the level of economic development in the central region is low. Low investment in infrastructure leads to slow development, which has little impact on housing prices.
- (3)
- In addition to urban infrastructure, land transfer price, industrial structure, credit scale, and other factors also have a considerable influence on eastern China’s real estate market. Fluctuations in the real estate market in central China mainly depend on the land market. Jiangsu, Zhejiang, and Shanghai, as economic development centers, have gradually transformed their industrial structures into secondary and tertiary industries. Hence, China’s financial market is more developed, and various factors adjust the real estate market. Anhui Province, an important grain-producing area in China, is more inclined to develop industrial structures for agriculture. The local government’s land revenue from finance is so large that the land transfer price has a significant impact on the local real estate market.
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Wang, S.J.; Wang, J.Y.; Wang, Y. Effect of land prices on the spatial differentiation of housing prices: Evidence from cross-county analyses in China. J. Geogr. Sci. 2018, 28, 725–740. [Google Scholar] [CrossRef] [Green Version]
- Nam, S.J. An Empirical Analysis on Determining Factors of Housing Prices in China. China Sinol. 2018, 33, 23–47. [Google Scholar]
- He, X.; Cai, X.J.; Hamori, S. Bank Credit and Housing Prices in China: Evidence from a TVP-VAR Model with Stochastic Volatility. J. Risk Financ. Manag. 2018, 11, 90. [Google Scholar] [CrossRef] [Green Version]
- Wang, X.R.; Hui, E.C.M.; Sun, J.X. Population Aging, Mobility, and Real Estate Price: Evidence from Cities in China. Sustainability 2018, 10, 3140. [Google Scholar] [CrossRef] [Green Version]
- Gan, L.; Ren, H.; Xiang, W.; Wu, K.; Cai, W. Nonlinear Influence of Public Services on Urban Housing Prices: A Case Study of China. Land 2021, 10, 1007. [Google Scholar] [CrossRef]
- Zhang, L.; Wang, H.; Song, Y.; Wen, H.Z. Spatial Spillover of House Prices: An Empirical Study of the Yangtze Delta Urban Agglomeration in China. Sustainability 2019, 11, 544. [Google Scholar] [CrossRef] [Green Version]
- Yang, Y.; Rehm, M. Housing prices and speculation dynamics: A study of Auckland housing market. J. Prop. Res. 2021, 38, 286–384. [Google Scholar] [CrossRef]
- Liu, F.; Min, M.; Zhao, K.; Hu, W.Y. Spatial-Temporal Variation in the Impacts of Urban Infrastructure on Housing Prices in Wuhan, China. Sustainability 2020, 12, 1281. [Google Scholar] [CrossRef] [Green Version]
- Wu, L.C.; Sun, P.Y. China’ s Infrastructure Development’ s Impact on China’ s Urbanization Process. China Popul. Resour. Environ. 2010, 20, 121–125. [Google Scholar]
- Kong, J.J.; Simonovic, S.P.; Zhang, C. Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies. Sustainability 2019, 11, 6552. [Google Scholar] [CrossRef] [Green Version]
- Lu, Y.J.; Jiang, S.H. National Level Infrastructure Condition Assessment: Principles and Metrics. Appl. Mech. Mater. 2011, 71–78, 1622–1627. [Google Scholar]
- Xiang, Y.H.; Li, C.Y.; Li, P.C.; Zhang, T.T. The Infrastructure Security Evaluation of China’s Big Cities. Environ. Technol. Resour. Util. II 2014, 675–677, 1257–1261. [Google Scholar]
- Xu, Q.S.; Shi, J.G.; Zhang, Y.G. Evaluation on the Development of Shanghai Suburban Infrastructure Based on AHP-Entropy Method. Adv. Mater. Res. 2014, 919–921, 1451–1456. [Google Scholar]
- He, X.H.; Cao, Z.C.; Zhang, S.L.; Liang, S.M.; Zhang, Y.Y.; Ji, T.B.; Shi, Q. Coordination Investigation of the Economic, Social and Environmental Benefits of Urban Public Transport Infrastructure in 13 Cities, Jiangsu Province, China. Int. J. Environ. Res. Public Health 2020, 17, 6809. [Google Scholar] [CrossRef] [PubMed]
- Belke, A.; Keil, J. Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions. Int. Adv. Econ. Res. 2018, 24, 25–45. [Google Scholar] [CrossRef] [Green Version]
- Abidoye, R.B.; Fam, F.; Oshodi, O.S.; Oyetunji, A.K. Impact of light rail line on residential property values–A case of Sydney, Australia. Int. J. Hous. Mark. Anal. 2021, 15, 691–708. [Google Scholar] [CrossRef]
- Stover, M.E. The Role of Infrastructure in The Supply of Housing. J. Reg. Sci. 1987, 27, 255–267. [Google Scholar] [CrossRef]
- Weisbrod, G.; Ben-Akiva, M.; Lerman, S. Tradeoffs in residential location decisions: Transportation versus other factors. Transp. Policy Decis.-Mak. 1997, 1, 55–145. [Google Scholar]
- Suen, I.S. Residential Development Pattern and Intraneighborhood Infrastructure Provision. J. Urban Plan. Dev. 2005, 131, 1–9. [Google Scholar] [CrossRef]
- Liang, J.; Koo, K.M.; Lee, C.L. Transportation infrastructure improvement and real estate value: Impact of level crossing removal project on housing prices. Transportatiom 2021, 48, 2969–3011. [Google Scholar] [CrossRef]
- Xu, Z.; Liu, X.; Hang, J.; Yao, D.; Shi, G. Do Urban Rail Transit Facilities Affect Housing Prices? Evidence from China. Sustainability 2016, 8, 380. [Google Scholar]
- Yang, L.; Chau, K.W.; Szeto, W.Y.; Cui, X.; Wang, X. Accessibility to transit, by transit, and property prices: Spatially varying relationships. Transp. Res. Part D Transp. Environ. 2020, 85, 102387. [Google Scholar] [CrossRef]
- Zhang, D.; Jiao, J. How Does Urban Rail Transit Influence Residential Property Values? Evidence from An Emerging Chinese Megacity. Sustainability 2019, 11, 534. [Google Scholar] [CrossRef] [Green Version]
- Zhang, B.; Li, W.D.; Lownes, N.; Zhang, C.R. Estimating the Impacts of Proximity to Public Transportation on Residential Property Values: An Empirical Analysis for Hartford and Stamford Areas, Connecticut. Int. J. Geo-Inf. 2021, 10, 44. [Google Scholar] [CrossRef]
- Szczepanska, A.; Senetra, A.; Wasilewicz-Pszczolkowska, M. The Influence of Traffic Noise on Apartment Prices on the Example of a European Urban Agglomeration. Sustainability 2020, 12, 801. [Google Scholar] [CrossRef] [Green Version]
- Riccioli, F.; Fratini, R.; Boncinelli, F. The Impacts in Real Estate of Landscape Values: Evidence from Tuscany (Italy). Sustainability 2021, 13, 2236. [Google Scholar] [CrossRef]
- Jim, C.Y.; Chen, W.Y. Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Landsc. Urban Plan. 2006, 78, 422–434. [Google Scholar] [CrossRef]
- Wu, C.; Ye, X.Y.; Du, Q.Y.; Luo, P. Spatial effects of accessibility to parks on housing prices in Shenzhen, China. Habitat Int. 2017, 63, 45–54. [Google Scholar] [CrossRef]
- Zhang, Y.L.; Dong, R.C. Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing. Int. J. Geo-Inf. 2018, 7, 104. [Google Scholar] [CrossRef] [Green Version]
- Sohn, W.; Kim, H.W.; Kim, J.H.; Li, M.H. The capitalized amenity of green infrastructure in single-family housing values: An application of the spatial hedonic pricing method. Urban For. Urban Green. 2020, 49, 126643. [Google Scholar] [CrossRef]
- NRTEE. Cleaning Up the Past, Building the Future: Government of Canada Public Works & Government Services Canada; NRTEE: Ottawa, ON, Canada, 2003.
- Finger, E.M.; Yanar, N. The Elgar Companion to Urban Infrastructure Governance: Innovation, Concepts; Edward Elgar: Cheltenham, UK, 2022. [Google Scholar]
- Bertolini, L.; Spit, T. Cities on Rails. The Redevelopment of Railway Station Areas; E & FN Spon: London, UK; New York, NY, USA, 1998. [Google Scholar]
- Garcia-Mayor, C.; Marti, P.; Castano, M.; Bernabeu-Bautista, A. The unexploited potential of converting rail tracks to greenways: The Spanish Vias Verdes. Sustainbility 2020, 12, 881. [Google Scholar] [CrossRef] [Green Version]
- Degioanni, A.; Ferretti, V. How to support the design and evaluation of redevelopment projects for disused railways? A methodological proposal and key lessons learned. Transp. Res. Part D Transp. Environ. 2017, 52, 29–48. [Google Scholar]
- Davidson, M.; Lees, L. New-build ‘gentrification’ and London’s riverside renaissance. Environ. Plan. 2005, 37, 1165–1190. [Google Scholar] [CrossRef] [Green Version]
- Way, T. Landscapes of industrial excess: A thick sections approach to Gas Works Park. J. Lands. Archit. 2013, 8, 28–39. [Google Scholar] [CrossRef]
- Zhou, Y. The Beauty of The Ruins: An Analysis of The Northern Landscape Park in Duisburg. Archit. Cult. 2017, 3, 179–181. [Google Scholar]
- Bowman, A.; Folkman, P.; Froud, J.; Johal, S.; Law, J.; Leaver, A.; Moran, M.; Williams, K. The Great Train Robbery: Rail Privatisation and After; Centre for Research on Socio-Cultural Change: Manchester, UK, 2013. [Google Scholar]
- Hess, D.B.; Almeida, T.M. Impact of proximity to light rail rapid transit on station-area property values in Buffalo, New York. Urban Stud. 2007, 44, 1041–1068. [Google Scholar] [CrossRef]
- Liang, X.J.; Liu, Y.L.; Qiu, T.Q.; Jing, Y.; Fang, F.G. The effects of locational factors on the housing prices of residential communities: The case of Ningbo, China. Habitat Int. 2018, 81, 1–11. [Google Scholar] [CrossRef]
- Lan, F.; Wu, Q.; Zhou, T.; Da, H.L. Spatial Effects of Public Service Facilities Accessibility on Housing Prices: A Case Study of Xi’an, China. Sustainability 2018, 10, 4503. [Google Scholar] [CrossRef] [Green Version]
- Haider, M.; Miller, E.J. Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Techniques. Transp. Res. Rec. J. Transp. Res. Board 2000, 1722, 1–8. [Google Scholar] [CrossRef]
- Dorantes, L.M.; Paez, A.; Vassallo, J.M. Analysis of house prices to assess economic impacts of new public transport infrastructure: Madrid metro line 12. Transp. Res. Rec. J. Transp. Res. Board 2011, 2245, 131–139. [Google Scholar] [CrossRef] [Green Version]
- Efthymiou, D.; Antoniou, C. How do Transport Infrastructure and Policies Affect House Prices and Rents? Evidence from Athens. Transp. Res. Part A Policy Pract. 2013, 52, 1–22. [Google Scholar] [CrossRef]
- Ouyang, Y.Y.; Chen, L.N.; Ll, Z.J. Infrastructure, urban and rural housing prices, rents: A micro-study based on Bayesian model averaging. Syst. Eng.—Theory Pract. 2020, 40, 2825–2838. [Google Scholar]
- Qin, Q.; Feng, W.B.; Yang, R.; Liang, Z.M. Study on evaluation characteristics of ecological infrastructure in four municipalities directly under the central government of China. Huazhong Shifan Daxue Xuebao 2008, 42, 471–476. [Google Scholar]
- Chen, Y.E.; Sun, Q. A Study on Calculation of Development of China’s Infrastructure and its Influencing Factors—The Empirical Research Based on Provincial Panel Data. Econ. Geogr. 2016, 36, 23–30. [Google Scholar]
- Tian, S.Z.; Sun, Y.G.N.; Zhan, Q.Y. Evaluation of lnfrastructure Development Level Based on Principal Component Analysis—Panel Data from 30 Provinces from 2007 to 2016. J. Chongqing Technol. Bus. Univ. (Nat Sci Ed) 2017, 34, 41–49. [Google Scholar]
- Lu, Y.F.; Zheng, F.H. Comprehensive evaluation on the municipal infrastructure development from the regional integration perspective: An empirical analysis based on the nine cities of the Pearl River Delta Area. Urban Problems 2014, 10, 2–9. [Google Scholar]
- Aycart Luengo, C. Vías verdes: La experiencia española. Proy. Rever. Ing. Territ. 2004, 69, 28–37. [Google Scholar]
- Foster, J. Off Track, In Nature: Constructing Ecology on Old Rail Lines in Paris and New York. Nat. Cult. 2010, 5, 316–337. [Google Scholar] [CrossRef]
- Keil, U.; Heimann, J. Berlins erster Naturerfalrungsraun: Ein Pilotprojekt im Park am Gleisdreieck. Stadt Grun 2012, 61, 39–44. [Google Scholar]
- Fu, Q.C.; Zheng, X.D. Regional Brownfields Regeneration Strategies Driven by the International Building Exhibition in the Ruhr. Chin. Landsc. Archit. 2018, 35, 21–26. [Google Scholar]
- Feng, S.; Hou, W.; Chang, J. Changing Coal Mining Brownfields into Green Infrastructure Based on Ecological Potential. Sustainbility 2019, 11, 2252. [Google Scholar] [CrossRef] [Green Version]
- Zhang, H.Y.; Wang, X.Y. The impact of structural adjustment on housing prices in China. Asian-Pac. Econ. Lit. 2018, 32, 108–119. [Google Scholar] [CrossRef]
- Jiang, Y.S.; Zhao, D.; Sanderford, A.; Du, J. Effects of Bank Lending on Urban Housing Prices for Sustainable Development: A Panel Analysis of Chinese Cities. Sustainbility 2018, 10, 642. [Google Scholar] [CrossRef]
- Yang, L.; Sun, Z.C. The Development of Western New-lype Urbanization Level Evaluation Based on Entropy Method. Econ. Probl. 2015, 3, 115–119. [Google Scholar]
- Tobler, W.R. A computer movie simulating urban growth in the Detroit Region. Econ. Geogr. 1970, 46, 115–146. [Google Scholar] [CrossRef]
- Zhao, Y.; Wei, R.; Zhong, C.W. Research on Spatial Spillover Effects and Regional Differences of Urban Housing Price in China. Econ. Comput. Econ. Cyb. 2021, 55, 211–228. [Google Scholar]
- James, L.; Robert, K.P. Introduction to Spatial Econometrics; CRC Press: Boca Raton, FL, USA, 2009. [Google Scholar]
- Wang, H.; Zhou, S.J. The Analysis of the “Direct Effect” and “Indirect Effect” of Urbanization Affects on Real Estate Prices: Based on the Dynamic Spatial Durbin Model of Prefecture-level Cities. Nankai Econ. Stud. 2017, 2, 3–22. [Google Scholar]
- Lean, H.H.; Smyth, R. Regional House Prices and the Ripple Effect in Malaysia. Urban Stud. 2013, 50, 895–922. [Google Scholar] [CrossRef]
- Meen, G. Regional house prices and the ripple effect: A new interpretation. Hous. Stud. 1999, 14, 733–753. [Google Scholar] [CrossRef]
- Gray, D. District house price movements in England and Wales 1997–2007: An exploratory spatial data analysis approach. Urban Stud. 2012, 49, 1411–1434. [Google Scholar] [CrossRef]
- Chen, M.H.; Wang, S.; Liu, W.F.; Liu, Y.X. Measurement and Analysis of Correlation Effect of Urban Housing Price under the Nonlinear Perspective. China Soft Sci. 2020, 10, 96–106. [Google Scholar]
- Guo, G.L.; Su, X.R. Research on Mediating Effect of Municipal Infrastructure Construction on Housing Price—Based on Empirical Analysis of 35 Big and Medium-sized Chinese Cities. Future Dev. 2015, 39, 54–58. [Google Scholar]
- Boquet, Y. The renaissance of tramways and urban redevelopment in France. Misc. Geogr. 2017, 21, 5–18. [Google Scholar] [CrossRef] [Green Version]
- Reis, A.C.; Lovelock, B. Linking tourism products to enhance cycle tourism: The case of the Taieri Gorge Railway and the Otago Central Rail Trail, New Zealand. Tour. Rev. Int. 2014, 18, 57–69. [Google Scholar] [CrossRef]
- Camerin, F. From “Ribera Plan” to “Diagonal Mar”, passing through 1992 “Vila Olímpica”. How urban renewal took place as urban regeneration in Poblenou district (Barcelona). Land Use Policy 2019, 89, 104226. [Google Scholar] [CrossRef]
- Franz, M.; Nathanail, P.; Okuniek, N. Sustainable development and brownfield regeneration. What defines the quality of derelict land recycling. Environ. Sci. 2006, 3, 135–151. [Google Scholar] [CrossRef] [Green Version]
- Offner, J. ‘Territorial deregulation’: Local authorities at risk from technical networks. Int. J. Urban Reg. 2000, 24, 165–182. [Google Scholar] [CrossRef]
- Burinskiene, M.; Bielinskas, V.; Podviezko, A.; Gurskiene, V.; Maliene, V. Evaluating the Significance of Criteria Contributing to Decision-Making on Brownfield Land Redevelopment Strategies in Urban Areas. Sustainbility 2017, 9, 759. [Google Scholar] [CrossRef] [Green Version]
- Ahmad, A.; Zhu, Y.M.; Shafait, Z.; Sahibzada, U.F.; Waheed, A. Critical barriers to brownfield redevelopment in developing countries: The case of Pakistan. J. Clean. Prod. 2019, 212, 1193–1209. [Google Scholar] [CrossRef]
The Evaluation Index System | First Grade Index | Second Grade Index |
Transportation facility | Railway network density(km/km2) | |
Road area per capita | ||
Bus per 10,000 people in municipal district (car/10,000 person) | ||
Water supply and drainage facility | Density of drainage pipeline (km/km2) | |
Density of water supply line (km/km2) | ||
Communication facility | Mobile phone subscriber per 100 people (household/100) | |
Internet access broadband users per 100 people (household/100) | ||
Energy and power facility | Gas and natural gas usage (m3/P) | |
Gas penetration rate (%) | ||
Per capita household electricity consumption (kwh/P) | ||
Ecological environment facilities | Average per capita garden green space area (m2/P) | |
Green coverage rate of built district (%) | ||
Treatment rate of domestic sewage (%) |
Year | Adjacency Matrix | Geographic Matrix | Nested Matrix | |||
---|---|---|---|---|---|---|
Real Estate Price | Infrastructure Development Level | Real Estate Price | Infrastructure Development Level | Real Estate Price | Infrastructure Development Level | |
2006 | 0.442 [0.000] | 0.207 [0.009] | 0.326 [0.000] | 0.177 [0.000] | 0.141 [0.000] | 0.068 [0.000] |
2007 | 0.409 [0.000] | 0.226 [0.004] | 0.305 [0.000] | 0.189 [0.000] | 0.126 [0.000] | 0.072 [0.000] |
2008 | 0.409 [0.000] | 0.249 [0.002] | 0.310 [0.000] | 0.220 [0.000] | 0.137 [0.000] | 0.086 [0.000] |
2009 | 0.430 [0.000] | 0.225 [0.003] | 0.330 [0.000] | 0.218 [0.000] | 0.146 [0.000] | 0.079 [0.000] |
2010 | 0.317 [0.000] | 0.252 [0.001] | 0.268 [0.000] | 0.237 [0.000] | 0.114 [0.000] | 0.087 [0.000] |
2011 | 0.319 [0.000] | 0.267 [0.000] | 0.255 [0.000] | 0.253 [0.000] | 0.108 [0.000] | 0.091 [0.000] |
2012 | 0.437 [0.000] | 0.262 [0.001] | 0.330 [0.000] | 0.239 [0.000] | 0.144 [0.000] | 0.082 [0.000] |
2013 | 0.438 [0.000] | 0.248 [0.001] | 0.338 [0.000] | 0.233 [0.000] | 0.153 [0.000] | 0.076 [0.000] |
2014 | 0.411 [0.000] | 0.245 [0.002] | 0.312 [0.000] | 0.230 [0.000] | 0.135 [0.000] | 0.076 [0.000] |
2015 | 0.367 [0.000] | 0.279 [0.001] | 0.286 [0.000] | 0.243 [0.000] | 0.125 [0.000] | 0.085 [0.000] |
2016 | 0.324 [0.000] | 0.376 [0.000] | 0.254 [0.000] | 0.333 [0.000] | 0.105 [0.000] | 0.138 [0.000] |
2017 | 0.369 [0.000] | 0.396 [0.000] | 0.289 [0.000] | 0.343 [0.000] | 0.134 [0.000] | 0.144 [0.000] |
2018 | 0.364 [0.000] | 0.403 [0.000] | 0.290 [0.000] | 0.348 [0.000] | 0.131 [0.000] | 0.144 [0.000] |
Test | Adjacency Matrix | Geographic Matrix | Nested Matrix | |||
---|---|---|---|---|---|---|
Statistic | p Value | Statistic | p Value | Statistic | p Value | |
Lagrange multiplier-Spatial error test | 144.299 | 0.000 | 224.143 | 0.000 | 263.530 | 0.000 |
Robust Lagrange multiplier-Spatial error test | 64.011 | 0.000 | 141.154 | 0.000 | 187.400 | 0.000 |
Lagrange multiplier-Spatial lag test | 104.635 | 0.000 | 109.456 | 0.000 | 115.550 | 0.000 |
Robust Lagrange multiplier- lag test | 24.346 | 0.000 | 26.466 | 0.000 | 39.420 | 0.000 |
Likelihood ratio—Spatial lag test | 35.210 | 0.000 | 35.240 | 0.000 | 37.540 | 0.000 |
Likelihood ratio—Spatial error test | 49.240 | 0.000 | 44.140 | 0.000 | 36.620 | 0.000 |
Hausman test | 70.370 | 0.000 | 65.520 | 0.000 | 54.530 | 0.000 |
Variable | Fix-Effect Regression | Difference GMM | Adjacency Matrix | Geographic Matrix | Nested Matrix |
---|---|---|---|---|---|
ln RSP | 0.515 *** | 0.407 *** | 0.457 *** | 0.469 *** | 0.530 *** |
ρ | 0.220 *** | 0.287 *** | 0.250 ** | ||
IDL | 0.641 *** | 0.728 *** | 0.521 *** | 0.612 *** | 0.596 *** |
CZ | 0.046 | 0.055 | 0.042 | 0.043 * | 0.033 |
IS | 0.003 | 0.006 | 0.003 | 0.004 | 0.006 * |
ln LTP | 0.068 *** | 0.173 *** | 0.049 *** | 0.040 *** | 0.037 *** |
ln INC | −0.080 | −0.240 | −0.091 | 0.053 | 0.134 |
ln CS | 0.039 *** | 0.053 *** | 0.036 *** | 0.030 *** | 0.023 *** |
Wx*IDL | 0.663 | 0.939 | 2.338 *** | ||
Wx*CZ | 0.176 ** | 0.313 ** | 0.356 * | ||
Wx*IS | 0.009 | 0.015 ** | 0.046 * | ||
Wx*ln LTP | 0.066 *** | 0.079 ** | 0.054 | ||
Wx*ln INC | −0.349 ** | −0.673 *** | −1.297 *** | ||
Wx*ln CS | −0.004 | −0.001 | 0.024 | ||
ε | 0.007 *** | 0.007 *** | 0.007 *** | ||
cons | 2.681 *** | ||||
AIC | −974.816 | −1000 | −1000 | −1000 | |
BIC | −945.427 | −862.674 | −874.653 | −889.076 |
Adjacency Matrix | Geographic Matrix | Nested Matrix | |||||
---|---|---|---|---|---|---|---|
Variable | Coefficient | t-Statistic | Coefficient | t-Statistic | Coefficient | t-Statistic | |
Direct effects | IDL | 0.552 *** | [3.516] | 0.635 *** | [3.697] | 0.616 *** | [3.747] |
CZ | 0.054 * | [1.830] | 0.055 ** | [2.342] | 0.040 * | [1.755] | |
IS | 0.003 | [0.890] | 0.005 | [1.293] | 0.007 ** | [2.015] | |
ln LTP | 0.053 *** | [3.657] | 0.042 *** | [3.203] | 0.038 *** | [3.002] | |
ln INC | −0.106 | [−0.773] | 0.041 | [0.360] | 0.127 | [0.949] | |
ln CS | 0.036 *** | [3.683] | 0.030 *** | [3.124] | 0.023 *** | [3.025] | |
Indirect effects | IDL | 0.934 * | [1.907] | 1.539 * | [1.936] | 3.301 *** | [3.783] |
CZ | 0.234 *** | [2.790] | 0.464 ** | [2.524] | 0.508 ** | [2.040] | |
IS | 0.011 | [1.311] | 0.023 ** | [2.037] | 0.066 * | [1.720] | |
ln LTP | 0.093 *** | [3.422] | 0.122 *** | [3.127] | 0.081 | [1.540] | |
ln INC | −0.447 *** | [−2.692] | −0.907 *** | [−3.578] | −1.686 *** | [−4.862] | |
ln CS | 0.004 | [0.329] | 0.009 | [0.510] | 0.037 ** | [2.497] | |
Gross effects | IDL | 1.485 *** | [2.819] | 2.174 *** | [2.868] | 3.917 *** | [4.543] |
CZ | 0.289 *** | [3.135] | 0.519 *** | [2.819] | 0.548 ** | [2.155] | |
IS | 0.149 * | [1.687] | 0.027 ** | [2.268] | 0.073 ** | [1.965] | |
ln LTP | 0.146 *** | [4.902] | 0.164 *** | [4.174] | 0.118 ** | [2.359] | |
ln INC | −0.553 *** | [−3.082] | −0.866 *** | [−3.447] | −1.558 *** | [−4.534] | |
ln CS | 0.041 *** | [5.150] | 0.039 *** | [3.205] | 0.061 *** | [4.650] |
The Eastern Region | The Central Region | ||||||
---|---|---|---|---|---|---|---|
Variable | Adjacency Matrix | Geographic Matrix | Nested Matrix | Adjacency Matrix | Geographic Matrix | Nested Matrix | |
ρ | 0.113 * | 0.096 | 0.033 | 0.274 *** | 0.333 *** | 0.350 *** | |
ε | 0.007 *** | 0.007 *** | 0.007 *** | 0.006 *** | 0.006 *** | 0.006 *** | |
Direct effects | IDL | 0.705 *** | 0.717 *** | 1.062 *** | 0.218 | 0.285 | 0.062 |
CZ | 0.054 | 0.042 | 0.063 | 0.043 | 0.041 | 0.028 | |
IS | 0.298 | 0.706 | 0.886 | 0.611 | 0.371 | 0.914 ** | |
ln LTP | 0.085 *** | 0.080 *** | 0.093 *** | 0.035 *** | 0.029 ** | 0.036 *** | |
ln INC | −0.229 | 0.078 | −0.055 | 0.176 | 0.116 | 0.082 | |
ln CS | 0.051 *** | 0.044 *** | 0.043 *** | 0.013 * | 0.012 | 0.012 | |
Indirect effects | IDL | 1.808 *** | 2.635 *** | 1.039 *** | −0.648 | −1.426 * | −1.286 |
CZ | 0.083 | 0.129 | 0.165 | 0.341 *** | 0.531 *** | 0.187 | |
IS | 2.380 ** | 3.321 * | 1.325 | −0.904 | −1.168 | −3.621 ** | |
ln LTP | 0.081 *** | 0.094 *** | 0.050 ** | 0.057 ** | 0.111 *** | 0.084 ** | |
ln INC | −0.617 *** | −1.271 *** | −0.636 *** | −0.010 | 0.074 | 0.279 | |
ln CS | −0.0005 | 0.007 | 0.01 | 0.0005 | 0.007 | 0.008 | |
Gross effects | IDL | 2.513 *** | 3.351 *** | 2.101 *** | −0.429 | −1.142 | −1.223 |
CZ | 0.137 | 0.171 | 0.228 ** | 0.383 *** | 0.572 *** | 0.214 | |
IS | 2.678 *** | 4.027 *** | 2.212 ** | −0.293 | −0.797 | −2.708 * | |
ln LTP | 0.166 *** | 0.174 *** | 0.143 *** | 0.092 *** | 0.141 *** | 0.120 *** | |
ln INC | −0.846 *** | −1.193 *** | −0.691 *** | 0.166 | 0.19 | 0.361 | |
ln CS | 0.051 *** | 0.051 *** | 0.053 *** | 0.014 | 0.019 | 0.020 |
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Chen, H.; Zhang, Y.; Zhang, N.; Zhou, M.; Ding, H. Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta. Sustainability 2022, 14, 7569. https://doi.org/10.3390/su14137569
Chen H, Zhang Y, Zhang N, Zhou M, Ding H. Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta. Sustainability. 2022; 14(13):7569. https://doi.org/10.3390/su14137569
Chicago/Turabian StyleChen, Hanli, Yu Zhang, Ningxin Zhang, Man Zhou, and Heping Ding. 2022. "Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta" Sustainability 14, no. 13: 7569. https://doi.org/10.3390/su14137569
APA StyleChen, H., Zhang, Y., Zhang, N., Zhou, M., & Ding, H. (2022). Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta. Sustainability, 14(13), 7569. https://doi.org/10.3390/su14137569