Research On The Impact Of Housing Characteristics On Urban Housing Prices

Not only does housing price relate to the economic development of the country,but also affects people's daily life.Under the background of high urban housing price,housing price has become an important topic in academic research and residents' life.At present,the research on housing price involves many subjects.Based on Econometrics and Geostatistics,this thesis takes the housing price of Xiamen as the research object,discusses the factors that affect the housing price of Xiamen from the community level and single housing level,and provides theoretical basis and information reference for the government to formulate relevant policies and residents to carry out housing transactions.In this thesis,starting from the residential area level,the spatial distribution characteristics of housing prices in Xiamen are understood through the geographical statistical analysis.Based on the hedonic price theory,the spatial lag model,spatial error model and mixed geographically weighted regression model are used to analyze the spatial effect of residential prices,and the spatial distribution of the coefficients is displayed visually with the help of ArcGIS.Further,from the perspective of single housing,Lasso method is used to screen the housing characteristics,and the possible impact of various housing characteristics on the housing price is studied from the perspective of the housing supplier and the transaction parties.The conclusions are as follows:(1)The housing prices in Xiamen have positive spatial correlation and local aggregation,and show a trend of multi-core distribution.In the island,Wuyuan Bay and Binnan Binbei area are the high points,decreasing towards the surrounding circle;in the outside of the island,the housing prices are gradually transiting to the inland with the Bay as the center.(2)Through the establishment of spatial lag model and spatial error model,it is found that there is a significant spatial dependence of housing price in Xiamen,and under different spatial weight matrix,the influence of housing year,distance to large shopping mall,distance to key primary and secondary schools and lakeside marine landscape on housing price is significant at the level of 5%.(3)The results of the mixed geographically weighted regression model show that the influence factors of the year of completion,the distance to the subway station,the distance to the BRT station,the distance to the tertiary hospital,the distance to the large shopping mall and the distance to the key primary and secondary schools on the housing price are significantly different,and show different distribution rules.(4)From the perspective of single housing,no matter from the perspective of housing suppliers or trading parties,the influence of location characteristics and neighborhood characteristics on housing price is more significant than the architectural characteristics of housing itself.Among the neighborhood characteristics,large shopping malls and key primary and secondary schools play a major role in the impact of housing prices.