Analysis of Factors Influencing Second-hand Housing Prices in Beijing based on Hierarchical Linear Model

In recent years,housing prices have attracted widespread attention,and the fluctuation of housing prices is due to a combination of many factors.In addition to the characteristics of the house itself,the price of a house is also affected by other factors,such as the community in which the house is located.This article used Beijing’s 2017 second-hand housing transaction data (based on second-hand housing transaction records on Lianjia.com),introduced a hierarchical linear model,and employed Stata software to analyze from different levels.It is intended to find the correlation between housing prices and different levels of characteristics,so to pin down the factors that affect prices of the second-hand housing.

基于蝙蝠算法SVR模型的北京市二手房价预测研究

传统SVR模型可预测房价的变化趋势,但不恰当的参数设置会影响预测的精度。本文针对北京市二手房同比价格指数的非线性变化特征,将蝙蝠算法引入到SVR模型中,使其对模型的三个参数进行优化设置,结合网络搜索数据,构建了BA-SVR&WSD混合模型,并给出了该模型算法的预测流程,通过引入多个基准预测模型和预测性能度量指标进行对比研究。结果表明:基于蝙蝠算法的SVR模型具有较好的泛化能力,预测效果更准确且预测精度更高,该预测方法也为北京市二手房价格的监测和调控提供了有价值的参考。

房地产调控政策下北京市住宅价格预测

房地产市场的繁荣与购房能力和需求的不匹配导致了近年来北京市住宅价格呈现病态增长的局面,住宅价格的畸高不利于市场经济的运转。在限购政策的背景下,本文对调控政策对北京市住宅市场的销售、供求和价格的影响进行了量化分析与预测,以分析为基础提出了调控政策效用的"潮汐周期"。最后对房地产调控提出了建议。