Spatial Heterogeneity In Urban Implicit Housing Prices

With the acceleration of China’s urbanization process, and the reform of urban housing system, real estate market has experienced a rapid growth in the last ten years. The current house price, the relationship between housing characteristics and residential housing prices attract a lot of academics’attention. As we all know, to every property buyers, based on their different social economic status, and different activities places (such as work place), their demand of the specific housing structure, neighborhood characteristics, location characteristic is different. Due to the fixity and heterogeneity of residential, it can be expected that imbalance between supply and demand exist, which leads to the spatial heterogeneity of implicit housing prices.The urban housing price is the focus of the city residents, city government and real estate developers, in essence, it is a variety of housing characteristics influencing the housing price. So it is necessary to analyze the implicit prices of housing characteristics, and the spatial heterogeneity of implicit housing prices. It will be advantageous to customers to choose house which has a suitable location and reasonable price, also be advantageous to real estate developers to accurately target market positioning. Besides, it can help government to formulate accurate urban development planning. This study chooses Hangzhou as an example, tries to respectively establish hedonic price model, spatial expansion model and geographically weighted model, combined with GIS technology, to test the spatial heterogeneity of implicit housing prices of Hangzhou, and also summarize its rule of spatial heterogeneity.The main work and conclusions of this paper are as follows:(1) This paper summarized the relevant theoretical models and the progress of Empirical Study on the the spatial heterogeneity of implicit housing prices. This paper briefly review hedonic price theory, focusly summarize the current research status of the spatial heterogeneity of housing prices and implicit housing prices. Besides, this paper systematically elaborate the three main methods which have been widely used to study the implicit housing price currently.(2) Based on the perspective of hedonic price theory, combining the fact of Hangzhou, we confirm the variables of the model. Choosing Hangzhou’s6main administrative as the study area, we collect the data through field community researching, real estate services companies and Internet platforms. Summing up the experience of variables selection of the current academic study, combing with the actual needs of the present study, we selecte13elements covering structure characteristic, neighborhood characteristic and location characteristic as study variables which are quantified and processed then.(3) This paper establish hedonic price model, spatial expansion model and geographically weighted model, empirical study shows that spatial heterogeneity of the implicit price of Hangzhou city truly exist, we find that spatial expansion model can determine the majority of the implicit price of housing characteristics, but it can not get the marginal price of housing characteristics of each sample point. Thus, it can not accurately analyze the spatial distribution of implicit housing prices.While geographically weighted model can provide the implicit housing prices of each sample point, combing with GIS technology, it can also provide visual graphics which can be more clearly reveal the spatial heterogeneity of implicit housing prices.(4) Comparing the results of three models, we find that the estimation effect of geographically weighted model is superior to that of traditional hedonic price model and spatial expansion model. Besides, spatial expansion model is superior to traditional hedonic price model. It indicates that the hypothesis of traditional hedonic price model which assumes that the influence of all the housing characteristics on housing price is spatially homogeneous exists some flaws. The result of this study demonstrate that if you want to analyze the implicit price of housing characteristics in-depth, geographically weighted model will be a better choice.