On the use of analytic network process for modeling housing prices : a Chongqing perspective

﻿With the rapid economic growth in China, the housing price, which is affected by many complex factors, has been rising since the issuance of the Decree No. 18 in 2003 by the State Council concerning the healthy development of housing market in China. At present, the housing prices are considered by the general public to be too high even for middle-class families can hardly purchase a reasonable size flats for their own use, especially in some large cities such as Beijing, Shanghai, Hangzhou and Shenzhen. In the circumstance, the government as well as the general public appears to worry that the harmoniousness and stabilization of the society will be affected and reasonable policies to deal with the fluctuation of housing price are demanded. To support the establishment of such policies, a predictive model for housing price that can provide reference information for various parties should be useful for policy-makers. Statistical analyses were regarded as the general approach for predicting the changes of housing prices. However, statistical method has some inherent disadvantages, especially they in general demand for a significant volume of good quality data. A housing price predictive model based on the improved form of T L Saaty's analytic hierarchy process (AHP), the Analytic Network Process (ANP), is thus developed in this study. ANP has broadened the scope of housing price analysis and it is particularly useful for problems with partially available data, qualitative variables and influences among the variables. The proposed model is a three-layer network forecasting model comprising 5 fundamental types of factors, which are natural factor, economic factor, social factors, policy factor and supplying factor and 14 sub-factors. The model provides a general framework to deal with decisions and in that both interaction and feedback are allowed within clusters and between clusters. In the process of building the comparison matrix for the analysis in ANP/AHP, one of the difficulties is envisaged that it is hard to obtain the eigensystem exactly for the order of more than four in the comparison matrix. The conditions of the continuous elements for the third order positive reciprocal comparison matrix of dynamic analytic hierarchy process (DHP) have then been established in this study. Such conditions are useful for acquiring the valid interval of the parameters in the AHP and discretization will then become unnecessary for all third order cases. The ANP based model is applied to analyze the housing price of Chongqing, which is the youngest municipality of China at the up-stream of Yangtze River. The data given in China Statistical Yearbook 2003-2009 and some other published databases have been adopted as the basis for the prediction. Growth rates as well as the housing prices have been deduced from the analysis and compared with the published housing prices. It is found that the computed results match well with the published data. In the study, five major attributes affecting housing prices have also been identified. They are construction cost, land cost, per capital annual Gross Domestic Product (GDP), per capital annual income and investment. Furthermore, an empirical equation for predicting housing prices has been established on the basis of the results given by the ANP based model, i.e. the coefficients of the variables in the equation has been established by the ANP model. By using the empirical equation, the recent housing prices for Chongqing from 2003 to 2008 have been calculated. The predictive results fit well with the published housing prices. Insights of the housing prices in Chongqing have been expressed on the basis of the analysis.