Research On The Quantiles’ Hedonic Prices Of Second-hand Residential In Beijing

Recently, with the capital and labor constantly flowing to the central city and enormous demand for housing, the development of second-hand housing market has been better and more mature, and the influential factors of second-hand housing prices has been widely studied by many scholars. In this context, the characteristic variable system and corresponding characteristic prices of second-hand residentials in Beijing are researched in this paper, providing theoretical guidance and reference value for the buyers with various preferences, for government to make policy, for intermediary to price reasonably and for real estate assessment agencies to evaluate curately,.The main researches are as follows:(1) A total of 17 characteristic variables were selected to construct the hedonic price model.From 4 aspects of residential’ s heterogeneities including characteristics of construction(5 variables), neighborhood environment(6 variables), location(5 variables),and time(1 variables), thia paper constructs hedonic price model by 17 characteristic variables and the actual transaction price,with 895 samples selected within 5 rings in Beijing, between October 2013 and July 2015 covering 282 plots.(2) Using quantile regression method to obtain coefficient curves of each quantile.The hedonic prices of the 17 characteristic variables for differene levels of secondhand residentials’ prices are researched, the quantile regression method is used to estimate the hedonic price model providing coefficient tables including five quantiles(0.1, 0.3, 0.5, 0.7 and 0.9) and coefficient curves of each quantile finding that compared with the primary school, the junior high school contributes more to the prices and the location characteristics are more sensitive to the price differences is proposed(subway, bus station and highway have greater impact on high prices).(3) Make contrasts of the results of quantile regression with OLS regression.Compared with OLS, exploring the hedonic prices using quantile regression is more comprehensively, giving a deeper insight into the degree of contribution to different levels of house prices.