Research On Price Prediction Model Of Residential Property Along URRT Line

Our country is in a transition period of accelerated urbanization and socio-economic development, rapid economic growth has accelerated the pace of development of urban transport. Urban rail transit has become long-term development strategy to solve China’s urban transportation problems, and set off a wave of construction in major cities. The impact of urban rail transit on land use makes the demand of residential land and commercial land which in the influenced region along the rail soared, improving the land development intensity. These changes make the growth potential of real estate along the urban rail transport improved greatly.Currently, most quantitative research on the prices of real estate along urban rail traffic are basically used transportation cost model and the hedonic price model. However, due to pooring availability of a lot of basic data required for modeling, the use of these models in the study of the prices of real estate along urban rail transit has certain limitations. Therefore, in the basis of limited data, this article combined with multivariate statistical analysis methods, uses BP neural network and Markov chain establish a price prediction model of residential property along URRT line.This paper studied the relationship between land use and urban rail transport, and combined a lot of research achievement research, analyzes the dimensions that affect the price of city’s residential property along the rail traffic, considers the dimensions mainly including location dimensions, structural dimensions, and neighborhood dimensions. Then, this paper selected seventeen indicators as impact indicators of price of residential property along urban rail traffic from the initial three dimensions, and maked factor analysis of the indicators, Reduced the seventeen indicators to six common factors as input layer nodes of BP neural network.Then, based on the process of establish BP neural network model, this paper build a price prediction model of residential property along urban rail traffic, and use Markov chains as correction methods to amend the predict results to improve prediction accuracy.Finally, this paper using the model analysis ten residential property, and predict their price. Through comparisoning with the actual price and predict result, this paper verify that the model could effectively predict the price of residential property along the urban rail traffic, and could provides an effective scientific basis for public on their purchase or investment of residential property, as well as provide a basis of decision making on residential real estate project investment for real estate enterprises, and has a certain significance for urban planning administration on urban rail line selection.This paper has8figures,17tables, and54references.