An Study On The Influencing Factors Of Housing Price In Beijing Based On GTWR Model

As a necessity in people’s lives,housing issues and their prices have been receiving widespread attention in recent years.As the real estate industry becomes more and more heated,the research on house prices has received increasing attention.The factors that influence house prices and their time and space are explored.The rules of change on the micro-level can help reveal the distribution patterns,intrinsic characteristics,and formation mechanisms that affect house price changes,and can also provide scientific decision-making guidance basis for related departments and institutions to formulate policies and strengthen management on a macro level.This article takes Beijing as the research area,and the data of second-hand housing in Beijing from 1980 to 2015 as the research object.It selects three variables of attributes,space and time as explanatory variables,and introduces a spatial-temporal geographical weighted regression model.Based on this,it proposes a The space-time geographically weighted regression model with distance constraints for the type of road network is compared with the spatial-temporal geographical weighted regression method,and the characteristics of spatialtemporal differentiation and its variation are analyzed.The main contents and conclusions of this article are as follows:(1)Conduct comparative analysis of commonly used house price research models,choose spatial-temporal geographic weighted regression method as a basic model,and then introduce road network distances to build spatial-temporal geographical weighted regression models of road network distance constraints.(2)According to the data of second-hand house prices in Beijing,variables are selected from three aspects of attributes,space,and time as factors that affect house prices.The comparison of the two models confirms the optimization of space-time geographical weighted regression models for road network distance constraints.Sex.(3)The spatial-temporal geographical weighted regression model based on road network distance constraints combined with Beijing’s real estate information to test the non-stationary factors of Beijing’s house prices,verifying that the Beijing house price data has a nonstationarity in time and space,and combined with the regression of various influencing factors.The coefficient analyzes and explains its temporal and spatial variation.