Study On The Spatial Distribution Of Urban Housing Price And Its Influencing Factors Based On GIS And SEM

The price of urban housing is closely related to the healthy development of society and people’s basic life. Urban housing price is the result of a variety of factors in the city,can reflect the comprehensive development level of city. Due to the large and complex urban housing price influence factors, each factor is always with the development of social economy changing, which leads to the urban housing prices are different not only in the city, but also in the urban internal spatial distribution also have great differences. And we believe that the impact of commercial housing in a variety of comprehensive factors, the price of the spatial distribution of a certain law, such as the city center price is higher than the price of the suburbs. But to make an accurate description of the housing price spatial pattern and influence factors need strong data and methods to support. Research on the application of GIS to the spatial distribution of house price, based on the structural equation modeling method, the research results can be quantified. In depth analysis of the spatial distribution of urban housing prices, analysis of the factors affecting the real estate development and urban construction have reference significance.The Nanchang Qingshan Lake as the study area, first of all is to Qingshanhu District Housing Price spatial distribution were studied.Collect finishing the 2015 March to November 2015 Nanchang Qingshanhu District 155 real estate transactions fold, using market comparison approach the price correction to November 2015 node, taking GIS technology as the research platform, to establish prices database for exploratory data analysis. Through using the ordinary Kriging interpolation method, to generate the contour map of the Castle Peak Lakes prices, Qingshanhu District residential price distribution were analyzed by isoline maps of prices.Through the structural equation model to make quantitative analysis on the influence factors of Qingshan Lake District of Nanchang city housing price, which is in reference to isoline analysis of previous research achievements and prices on the basis of the results,the establishment of housing price volatility as endogenous latent variables, real estate property, property location, accessibility, accessibility, public environmental facilities living facilities accessibility as exogenous latent variable structural equation model of latent variables collected each observation variables from the perspective of accessibility of a total of 19 observed variables, and puts forward five hypotheses, analyzes the reliability and validity of the questionnaire data using SPSS software, fitting and correctionof recognition, the use of AMOS model, the 17 observation variables, the optimal model with 6 latent variables, and then verify the five assumptions, chart and path influence coefficient divided by path analysis The relationship between the residential price and the influencing factors and the influence factors to the housing price.