Real Estate Prices Affecting Factors based on Grey Relational Analysis

Abstract

This paper selected seven factors which affect the housing price from macro economic environment, market demand, and living stands of people, and then analyzed the influence of key factors on the price. Based on gray correlation analysis model, the case study is employed in Dalian, and the the importance degree of factors affecting housing prices and the rank can be calculated. It can be concluded that the importance order of factors affecting the real estate prices in are: per capita consumption expenditure, Resident per capita disposable income, Land transaction price index, the area of real estate sales, the consumer price index, per capita GDP, GDP. This paper can provide a scientific basis for engineering project decisions, market planning and feasibility studies. Introduction In recent years, the real estate industry develops fast and changes quickly, the real estate price in some cities was too high for people. Although the has issued a series of policy such as five of the state control measures [1], local control measures as Beijing eight policies and Shanghai six policies are introduced accordingly, but the real estate price does not seem to stop the pace of rising. Real estate price is always a focus that many scholars payed attention to, and most of the study used statistics method to establish the model concerning with housing price. Grey Relational Analysis provide a novel way to analysis the changing real estate price. In 1982, Professor Julong Deng proposed grey theory. The theory focus on uncertainty system of information is known, and information is unknown, and is fit for small sample, poor information. It mainly uses the partial information known to generate, develop and extract valuable information to achieve operational behavior of the system, the correct description and effective monitoring of the evolution [2]. In the process of system development, if the changing trend of two factors is consistent, that is a high degree of synchronous changing, indicating the two factors have high correlative degree, on the other hand, the two factors have low correlative degree [3]. The application of Grey relational analysis in the real estate field obtains good results, which is mainly used in house price prediction, demand forecasting, the real estate investment analysis, forecasting and so on [4]. While more and more research appearing in this area, most of them point out what in the end affect the price only, but few of them analysis the importance degree of various factors [5]. This paper selected seven factors from economic, market, and living stands of people that affect housing price, took the city of for example, applied gray relational analysis to calculate the relative importance of each factor and analysis the reason for it. Select the main factors affecting the price of housing There are many factors that affect housing prices, but mainly are economic factors, market factors ,policy factors and living stands of people [6]. After review of references, this paper selected seven major factors to study as follows: GDP, per capita GDP, residents per capita disposable income, per capita consumption expenditure, consumer price index, the area of real estate sales and land transaction price index, as shown in table 1. We chose the data form 2007 to 2012 from Dalian Yearbook [7]and the basic data are shown in table 2. Grey relational analysis model Determine analysis of sequence. Table 2 have been given reference sequence X0 of system, the average selling price of commercial housing. Comparative sequence is the Influencing factors of real estate prices Xi,i=1,2......7. Therefore, Table 2 constitute a sequence matrix. Non-dimensional treatment. In general, we often use the mean or initialization to deal with sequence matrix with dimensionless. In most cases, in order to a more stable social and economic systems we usually use the initialization conversion.The calculation formula is as follows [8]: m k n i X k X k X i i , , 2 , 1 ; , , 2 , 1 , ) 1 ( ) ( ) (