Analysis Of Secondary House Price In Kunming Based On Spatial Statistical Analysis

With the acceleration of China’s urbanization process,the real estate market has developed rapidly in many cities,and the spatial differentiation of housing prices in some large cities has become apparent.This topic has attracted the attention of many scholars.As the capital city of Yunnan Province,Kunming understands its real estate development Law is of great significance for improving people’s livelihood.Traditional research models ignore the spatial location properties of housing,and believe that housing prices are independent of each other in spatial distribution.Such assumptions may lead to bias.Since house prices are a type of spatial data,their spatial dependence and heterogeneity have become the focus of attention.This article takes Kunming second-hand housing as the research object,applies spatial data analysis and modeling technology,combines spatial data analysis,statistical analysis,cluster analysis,spatial regression model,geographic weighted regression model and Kriging interpolation method to study the distribution of housing prices in various areas of Kunming Analysis of characteristics and influencing factors.Through the analysis of second-hand housing prices in various districts and counties of Kunming,the following conclusions are obtained: 1.There is a spatial correlation in the overall second-hand housing price in Kunming.At the same time,through local spatial autocorrelation analysis,it is found that the "low-low" aggregation Distributed in Xundian,Fumin,Songming,Shilin,Anning,Yiliang and other areas,"high-high" clusters are mainly distributed in Xishan,Wuhua,Panlong,Guandu and other areas.2.Through visual analysis,it is found that the current second-hand housing in Kunming is mainly concentrated in Wuhua,Xishan,Guandu,Panlong and Chenggong areas.3.Through correlation analysis,it is found that the convenience of transportation,shopping,living environment,living facilities,and school convenience are the main considerations when people buy a house.4.By kmeans clustering and combining geographical location factors,when k = 4,the classification results obtained are as follows: Wuhua,Panlong,Guandu,Chenggong,Fumin,Songming belong to the same category,Xishan,Anning,Jinning It belongs to the second category,Yiliang,and Shilin belongs to the third category.Luquan and Dongchuan belong to the fourth category.When k = 5,the classification results are as follows: Wuhua,Panlong,Guandu,Chenggong,Songming belong to one class;Luquan,Fumin belong to one class;Xishan,Anning,Jinning belong to one class;Yiliang,Shilin They belong to one category;Dongchuan and Xundian belong to one category.5.By using ordinary linear regression model,spatial lag model,and spatial error model for the analysis of second-hand housing prices in Kunming,the number of large-scale supermarkets,office buildings,the total number of hospitals,and the number of top three hospitals on the second-hand housing prices in Kunming were compared Big.At the same time,it was found that the model fitting effect was the best when the spatial error model was used,and the spatial regression coefficient α = 0.79 in the model passed the significance test,indicating that the price of second-hand housing in Kunming has a strong structural effect.6.Through the establishment of geographic weighted regression models for the four major urban areas and Chenggong New District,it is found that the degree of influence of each influencing factor on house prices has significant differences,that is,the plot ratio,greening rate,community management fee,and distance from elementary schools Influencing factors such as distance,distance from the subway and so on have different degrees of influence on house prices.