Research On Impacts Of Urban River Restoration On The Housing Price Based On Spatial Hedonic Model

Urban rivers and related river environments play a vital ecological and social role in the urban system,and affect the well-being of urban residents.With the rapid development of China's economy,the housing demand of urban residents has also shifted from the purely rigid demand for survival to the improvement of living conditions and the demand for enjoyment,the value of the river is increasing day by day,and is reflected in the housing price.However,over the decades,uncontrolled industrialization and urbanization have greatly changed and deteriorated the hydrological forms and the quality of waterscapes of urban rivers in China.The Chinese government has made great efforts for the ecological restoration of urban rivers from the country to the localities in the past few years.On the one hand,we need to capitalize residents' preference for urban rivers,quantify the benefits of a large number of river remediation projects,and provide better theoretical support for policies;On the other hand,when summarizing the global surveys,we found that most of the existing domestic research is still on the analysis of the external benefits of river landscapes,and there is little evidence to assess the impact of river pollution and even the restoration of rivers.There are fewer studies on the crosseffects of residential variables(such as riverside,floor height,etc.).In addition,most studies use the traditional Hedonic Price Model,only few studies consider the spatial effects of housing prices.Therefore,this study takes individual housing as the main research object,builds a characteristic price model,and uses SPSS software to explore the relevant attributes of rivers,especially the impact of river restoration on residential prices.On this basis,considering the uniqueness of the spatial location,the spatial feature price model is introduced at the community level,and the estimated difference between the spatial HPM and the traditional HPM is compared with the help of GIS and Geo Da software.In this paper,18 feature variables,including 5 river-related variables are selected,820 residential data of 324 communities crawled by Python in November 2019 and 58 river data are used to research the impact of river restoration on the price of second-hand houses in three districts in Wuxi City.Main conclusions are listed as followings:(1)The improvement of water quality,close to higher-grade rivers,and the restoration of rivers can all bring positive externalities and effectively increase the prices of surrounding houses,while the riverside of houses shows a price disadvantage.In the horizontal dimension,the distance between the residence and the river is not significant,and the adjacent river shows a significant negative externality,indicating that the residential price in the study area is only affected when it is near the river,and is not sensitive to the distance of the river.(2)Restored rivers can reduce the price disadvantage of low-rise residences,and the unit price difference between houses located below the 10 th floor and apartments above will be reduced by 8.3%(about 1191.13 RMB);Higher-grade rivers can bring higher premiums after restoration,the premium of the price of second-hand houses around large rivers can rise by 6.4%(about 918.46 RMB);The restoration of the rivers can significantly reduce the negative externalities caused by river pollution in riverside houses.,the housing price can increase by 10.4%(about 1492.51 RMB);There is a supplementary effect between water quality improvement and river restoration.When the polluted rivers around the house are comprehensively renovated and the water quality is improved,purchasers are willing to pay an additional 5.5%(about 789.31 RMB)Premium.(3)The influence of spatial lag and spatial error in the study area coexist.If the spatial effect is not considered,the traditional characteristic price model will overestimate the hidden value of the characteristic variable,including the influence of the river,the effect of water quality improvement,and the restoration of the river and so on,the overestimation range is between 0.51%-3.96%.The spatial autocorrelation analysis of residential community prices shows that there is obvious spatial agglomeration in the study area.The effects of spatial lag and spatial error mean that there is a strong correlation between the average price of each community,and some ignored variables or error terms will also affect the price in the study area.For this study,the Spatial Lag Model(SLM)is more suitable than the Spatial Error Model(SEM).