Keywords

1 Introduction

In 1998 China cancelled the welfare-oriented public housing distribution system. With the implement of housing fund and the increasing of residents’ income, China’s real estate market represents a favorable trend and the price, as a problem focused on by the whole society, has been rocketing, especially from 2004. The rocketing housing price triggers a series of social and economic issues. The excessive growth of the house price becomes the important point of the macro-intervention. In market economy, the formation of real estate price depends on the market conditions. The macro-control of the real estate has to be based on the condition of the market. China’s rocketing real estate price cannot be explained by the model of free market economy. The power comparison of supply and demand sides will have a direct impact on the real estate price changes. The market power reflects the ability of the supply and demand sides to affect the price and has significant influence on the growth of real estate price. With the increasing of demand rigidity, the sellers’ pricing power is becoming stronger and the price is largely decided by the sellers (The following market power refers to the sellers’ market power). We choose the real estate market sales data of the nation, 30 provinces and 35 large and medium-sized cities as sample and estimate China’s market power and the average increase of real estate price. We analyze the relationship between the amplitude of real estate price change and the market power. The result shows that China’s real estate price changes and the market power present a positive correlation and the market power is one of the main factors influencing the changes of real estate price.

2 Related Literature

The relevant research of the real estate price theory is adequately systematical and brings about lots of papers and treatise which has already been widely applied in daily practice. The research of price mechanism in foreign real estate market usually pays more attention into the functioning of the price mechanism and the factors which influence the real estate prices. Denise and William [1] used a model which is similar with “cobweb model” to analyze the fluctuation of real estate market. Meanwhile, they proposed a classification which divides the real estate market into capital market and consumption market and described the four quadrant models of the interaction between capital and consumption markets based on these two conceptions; Rosen [2] and Guilloches (1977) gave a systematic summarization of the theoretical frame of Hedonic pricing and proposed house price model which was widely used in the research of house price and living environment. Di Pasquale and Wheaton [3] studied the relationship between house rent and house price and generated the well-known stock-flow model. This model describes how property market and capital market achieve equilibrium by modulating various variables such as rent, price, number of new constructions and house stock. Gabriel et al. [4] established a house price model which contained both market demand and supply factors they figured that the population migration and the evolution of city structure could explain the fluctuation of house price in California in the past 20 years.

The domestic real estate markets have a short history. Because of the more and more intense house price conflict, the analysis of the factors influencing the house price becomes quite abundant. In the aspect of house supply, lots of scholar pay attention into the real estate cost. Zhang Hong and Li Wen dan [5], and Wang Xiazhong [6] believed that cost increase is the main factor of the rise of house price. Hu Ruixian [7] pointed out that the factor which determines the real estate price is nothing except cost and supply push. The demand control policy is hard to be an effective regulation measure. The government should focus on the cost control. Other scholars analyse the factors which influence the house price at the angle of real estate market demand. Wu Jianfeng [8] considered that the demand for better living conditions is the main factor of house price rising, along with the gradual increase of per capita wage; Zhang Daliang and Zhou Limei [9] made a research about how custom demand influences the house price; Qi Zhaozhen [10] had a analysis of the relationship between rent and house price; Zhang Hong and Li Wendan [5] analyzed how GDP influences the house price form economic gross angle.

Domestic scholars’ researches of the market mechanism of the real estate are limited within some qualitative description. Some of the scholars realize that there must be some kind of interaction among land market, stock market and incremental market. Lou Jiang [11] described the theory of the city real estate market and makes a monographic study on the correlation between house price and land price; Gao Meicheng [12] realized that the stock market plays the main role of regulating the incremental market and claimed the opening of the stock market in order to promote the development of the entire real estate market. Zhou Huili [13] discussed that the differentiation of real estate products can enforce the monopoly power of the sellers and enhance their abilities of monopoly pricing. She considered that inner features of the real estate and market structures should be the starting point for restraining the excessively rapid rising-up of the house price. Kuang Weida [14] estimated Lerner index of Beijing and Tianjin. The results showed that the main market power of real estate was quite high in China’s main cities and he concluded that the root cause of high house price is monopoly. Li Hongjin [15] calculated the panal data sectional data, historical data and Lerner index of different provinces. He claimed that the market competition of real estate was quite insufficient. The level of market monopoly was really critical and monopoly power could, to some degree, explain the house price rigidity in recent years. Wu Liping and Ju Fang [16] made an empirical analysis of the monopoly degree of real estate market and its effect in the excessively rapid rising-up of house price on the angle of market structure and market power. In addition, some other scholars studied house price factors based on real estate market intervention. The most important factor is the effect of Tax and macro-control.

3 Theoretical Analysis of Market Power Influences on House Price

In the real estate market, the price is decided by both sides of the market. With the increasing of demand rigidity, the sellers’ pricing power is becoming stronger and the price is largely decided by the sellers. Hull Fanta Hector Seaman Index, Lorenz curve, and Lerner Index can be used to measure the level of market power. In this paper we adopt Lerner Index to measure the real estate market power. Lerner Index reflects the level of the monopoly through measuring the degree that the price (P) deviates from the margin cost (MC). The expression to calculate Lerner Index (L) is:

$$ \mathrm{ L}=\frac{{\mathrm{ P}-\mathrm{ MC}}}{\mathrm{ P}}=-\frac{1}{\mathrm{ E_d}} $$
(39.1)

Supposed \( P=f(L) \), the rate of house price increase is:

$$ g=\frac{{\Delta p}}{p}=\frac{{f\left( {L+\Delta L} \right)-f(L)}}{f(L) } $$
(39.2)

According to the definition of differential coefficient we can get the simplified formula:

$$ {g}^{\prime}=\frac{{\frac{{f(L+2\Delta L)-f(L+\Delta L)}}{{f(L+\Delta L)}}-\frac{{f(L+\Delta L)-f(L)}}{f(L) }}}{{\Delta L}}=\frac{{f(L)f(L+2\Delta L)-{f^2}(L+\Delta L)}}{{\Delta Lf(L)f(L+\Delta L)}} $$
(39.3)

We can get formula 39.4 from formula 39.1:

$$ P=\frac{MC }{1-L } $$
(39.4)

In formula 39.3, as the denominator must be positive number, we just need to take consideration with the numerator. We can get the numerator through the formula of price and the Lerner Index.

$$ f(L)f(L+2\Delta L)-{f^2}(L+\Delta L)=\frac{MC }{(1-L)}\frac{MC }{{(1-L-2\Delta L)}}-\frac{{M{C^2}}}{{{{{(1-L-\Delta L)}}^2}}} $$
(39.5)

We can get the simplified formula 39.6:

$$ {g}^{\prime}=\frac{{M{C^2}{{{(\Delta L)}}^2}}}{{{{{(1-L-\Delta L)}}^2}f(L)f(L+\Delta L)\Delta L}}>0 $$
(39.6)

Therefore we can conclude that the amplification of price positively correlates with market power. The stronger the market power is, the faster the price increases.

4 Estimation of Domestic Real Estate Market Power

4.1 Selection of Relevant Variables and Data

According to the above-mentioned deduction, we can estimate the Lerner Index by the demand elasticity(Ed), which concerns with two statistics of the model: \( \log {y_{it }}=a+b\log {x_{it }}+u \), the demand amount Y and the price X.. In the model, we can proved that b is equal to \( {1 \left/ {{{E_d}}} \right.} \). In process of Positive versus Normative analysis, we use the sales of commercial housing and the mean price of the commercial housing to replace. In the research process of this paper, we collect the real estate market saleroom and the mean price data of the nation, 30 provinces and 35 large and medium-sized cities.

The data of sales of national commercial housing as follows in the Table 39.1:

Table 39.1 The data of sales of national commercial housing from 1987 to 2010
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We also collect the data of the sales of the nation, 30 provinces and 35 large and medium-sized cities. All the data comes from China’s National Bureau of Statistics, CEInet and China’s economic and information statistical data base. Since the amount of the data is too large, we do not list it in detail.

4.2 The Analysis of the Calculation Results f Market Power

4.2.1 The National Calculation Results in Different Time Interval

Because the land access can be exchanged in 1987 and housing distribution system reform begin in 1998, we separately calculate the market power from 1987 to 1998 and from 1999 to 2010 and make a comparison in this paper.

This paper uses Eviews software through the model based on Least Squares method to regression analysis and result is in the Table 39.2:

Table 39.2 The result of the national real estate market’s Lerner Index
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From the upper table, we can see that both coefficient determinations of the two durations are significant. Besides, the Regression coefficients are significant and the margin of Regression errors are small. So the results of the regression of the model are comparatively ideal.

4.2.2 The Calculation Results of the Data from 30 Provinces

According to the statistics of provinces of China’s real estate sales from 2000 to 2010, this paper uses Eviews software processing data based on Least Squares method. The following table is about the Lerner Index obtained in some provinces’ real estate market and is based on the regression (Table 39.3).

Table 39.3 The result of 30 provinces’ real estate market’s Lerner Index
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According to T-statistics we can see that regression coefficients are significant and the root mean squared error is small. Therefore the regression result of selected sample is quite ideal from this model and can be used to analyze the market power.

4.2.3 The Results of the Data from 35 Large and Medium-Sized Cities

According to 35 large and medium-sized cities’ annual data of commercial housing selling, we can get the coefficient of elasticity from this model by OLS method and then we can get Lerner index of real estate markets in different cities. The table below shows the reciprocal of elasticity of demand by regression through this model-the Lerner index of real estate markets in different cities (Table 39.4).

Table 39.4 The result of 35 cities’ real estate market’s Lerner Index
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According to the regression analysis of sample data, the results of Haikou, Chongqing and Lanzhou only have a fitting degree of 0.5. The regression results are not appropriate to be used as standards for judging real estate market power.

5 Correlation Between Market Power and Fluctuation of House Price

5.1 Analysis Based on the Domestic Market Data

In order to analyze the relationship between the variation of domestic real state’s price and the market power, We chose the consumption data of 1999–2010. We calculated the range of every year’s house price and the annual Lerner Index according to different sales and different average price. The data is shown below Table 39.5:

Table 39.5 The analysis data of the domestic market’s house price of 1999–2010
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According to the result of the sheet above, we make the inspection of the linear logarithmic of the range of variation of house price and the annual Lerner Index, the results are presented below:

$$ \mathrm{ L}\mathrm{ og}\left( \mathrm{ g} \right)=-1.25178+1.062260\mathrm{ Log}\left( \mathrm{ L} \right) $$
$$ \mathrm{ t}=\left( {-2.128489} \right)\left( {3.455575} \right)\;\;{{\mathrm{ r}}^2}=0.598816 $$

In the linear regression of the price variation and the Lerner Index time sequences, deflective error coefficient measures the elasticity of the variation of price to the Lerner Index. According to the results above, deflective error coefficient 1.062260 shows that when the Lerner Index increases one percent, the range of price variation will rise 106.23 %. indicating that the annual Lerner Index of the whole domestic market has marked effects on the house price variation

5.2 The Analyses Based on the Data of 30 Provinces

In order to analyze the relationship between house price variation and the market power more deeply, authors chose the real estate sales data of 1999–2010 of 30 provinces. After calculating each province’s increasing range of average price and Lerner Index of 1999–2010, we get the following data Table 39.6:

Table 39.6 The analysis data of the 30 provinces’ house price of 1999–2010
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According to the results above, the authors made the inspection of the linear logarithmic of the range of variation of house price and the annual Lerner Index, the results are presented below:

$$ \mathrm{ Log}(\mathrm{ g})=-1.708539+0.532438\mathrm{ Log}(\mathrm{ L}) $$
$$ \mathrm{ t}=(-16.47822)(5.477311)\ {{\mathrm{ r}}^2}=0.508482 $$

According to the results above, deflective error coefficient 0.532438 shows that when the Lerner Index increases 1 %, the range of price variation will rise 53.24 % indicating that the annual Lerner Index of the 30 provinces’ market has marked effects on the house price variation.

5.3 The Analysis Based on the Data of 35 Cities

Regional characteristics of the real estate market is obvious, the data of cities’ real state reflects the true characteristics of the real estate market better. The authors chose the real estate sales data of 1999–2010 of 35 cities. After calculating each city’s increasing range of average price and Lerner Index of 1999–2010, we got the following data Table 39.7:

Table 39.7 The analysis data of the 35 cities’ house price of 1999–2010
Full size table

According to the results above, the authors made the inspection of the linear logarithmic of the range of variation of house price and the annual Lerner Index, the results are presented below:

$$ \mathrm{ Log}(\mathrm{ g})=-1.300423+0.693153\mathrm{ Log}(\mathrm{ L}) $$
$$ \mathrm{ t}=\left( {-8.027268} \right)\left( {3.401730} \right){{\mathrm{ r}}^2}=0.259621 $$

According to the results above, deflective error coefficient 0.693153, shows that when the Lerner Index increases one percent, the range of price variation will rise 69.31 % indicating that the annual Lerner Index of the 35 cities’ market has marked effects on the house price variation

6 Summary

In market economy, the formation of real estate price depends on the market conditions. The power comparison of supply and demand sides will have a direct impact on the real estate price changes. We proved theoretically that the magnitude of the house price changes is an increasing function of the Lerner index. Based on it, we choose the real estate market sales data of the nation, 30 provinces and 35 large and medium-sized cities as samples and estimate the effect of the nation’s Lerner Index on the range of house price variation. It proved out that China’s real estate price changes and the real estate market power present a positive correlation and the market power is one of the main factors influencing the changes of real estate price.