A Study Into The Specification And The Modeling Technology In Spatial Hedonic Model

It is the common methodology for quantitative methods to build models directly onstudying target. Generally, the targets are defined as the explanatory variables andinterpreted by a variety of independent variables with an equation. However, sometimesthe target not only depends on its attributes, but is also influenced by its neighbors. Thisphenomenon is applicable in a wide range of areas, and the real estate market is notexcluded. The most prevailing approach to value properties is to use the hedonic pricingmodel, which divide the house price into its attributes and regard the whole price ofproperty the sum of the values of them. But if the prices of the properties are connectedwith each other, merely interpreting the house prices by its attributes would lead to biases,as it overlooks the influence exerted by the adjacent house prices.To quantify the influences from the near observations are the main focus of spatialeconometrics. Worth to be concentrated is that it has been widely proved by empiricalevidences that data samples are likely to present some sort of connections among itsneighborhoods, which we name it spatial effects altogether. In this article, we focus onfour kinds of spatial effects: the spatial dependence, spatial heterogeneity, spatialexternality and the omission of spatially connected variables, and the modelingtechnology to catch these effects and the theoretical analysis of four kinds of spatialmodels: the Spatial Lag model, Spatial Error model, General Spatial model and theSpatial Durbin model.In this article, we studied the different modeling technology for different spatialmodels to catch the trends of the sample data and its different constrains and hypothesesback to it, and then figure out the best model is the Spatial Durbin Model when facingthese four kinds of spatial effects as the constrains of it is the least among the four.