Chapter 1 Introduction
1.3 Research Overview
國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
1.3 Research Overview 1.3.1 Research Framework
This study consists of six chapters as follows. Chapter 1 is “Introduction”, which mainly contains the general background, research motivation and research purpose.
Besides, this part gives a brief introduction to the research scope and research overview of this study. Chapter 2 is “Literature Review”, discussing the definitions of the bubble, the measures of housing bubbles and the factors which influence the housing bubbles. Chapter 3 is “Research Method and Data Information”, presenting the methodology and the data used in this study. Chapter 4 is “Measure of Housing Bubbles”, explaining the status of the housing bubbles in Beijing, Shanghai and Guangzhou. Chapter 5 is “Empirical Results”, which illustrates the empirical results and analyses the practical meanings of these results. Chapter 6 is “Conclusions and Discussion”, which summarizes the implications of this study and provides suggestions for housing policies.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
1.3.2 Research Process
Figure 1- 3 The Research Process General Background and
Research Motivation
Literature Review
Research Method and Data Information
Measure of Housing Bubbles Empirical Results
Conclusions and Discussion The Measure of Housing Bubbles
Cointegration Test VECM/VAR Granger Causality Test The Definition
of the “Bubble”
Review of Housing Bubbles Factors
‧
This chapter mainly reviews the relevant literatures about housing bubbles and is divided into three sections. The first section explores the definition of housing bubbles. The second section discusses the method to measure housing bubbles. At last, the third section explores the relationship between housing bubbles and other factors, as the foundation for the empirical analysis.
2.1 The Definition of the “Bubble”
The researches on asset bubbles are long-standing. And the scholars never stop investigating the definition of “Bubble”. Stiglitz (1990) indicated that the prices of asset would not stop increasing if the investors believed the asset could be sold for a higher price in the future than they had been expecting. In other words, an asset bubble exists when the price is not justified by fundamental factors. Besides, Diba and Grossman (1988) pointed out that a rational bubble reflected a self-confirming belief that an asset’s price depended on a combination of variables that were not part of market fundamentals. In short, an asset bubble could be defined as the difference between the market price and the market fundamental (Tirole, 1985).
The definition of bubble also applies to real estate. A housing bubble can be described as a deviation of the market price from the fundamental value of the house (Krainer, 2003; Smith et al., 2006). According to this definition, it is possible to quantify the value of housing bubbles. As for the causes of housing bubbles, Levin and Wright (1997a, 1997b) indicated that a house price bubble that could differ between actual and mix-adjusted properties would arise when households could take positions in the market by buying larger or higher quality houses in anticipation of expected price rises. In the theory of housing price proposed by Muellbauer and Murphy (1997), the section that housing prices exceed fundamental values is caused by speculation, which can be defined as speculative prices-i.e. housing bubbles.
Briefly speaking, the house purchase for investment makes the price depart from the equilibrium level and then results in housing bubbles.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
In summary, it is widely considered that a housing bubble can be defined as the deviation of the market price from the fundamental value of the house. And the later analyses on housing bubbles in this study would employ this definition.
‧
2.2 The Measure of Housing Bubbles
According to the definition of housing bubbles above, it is necessary to confirm market prices and fundamental values before gauging the situation of housing bubbles.
The housing prices can be obtained directly. But it is complicated to get the fundamental values. In the past literature, there are lots of methods to calculate the house fundamental value. We divide these methods into three types: rental incomes, personal incomes and macroeconomic variables.
(1) Rental Incomes
According to the principle of finance, the fundamental value of an asset equals the total discount of the cash flow in the future. This method is based on the theory above.
Smith et al. (2006) pointed out that rental incomes are the central factor to determine the fundamental value of a house and houses could be valued by discounting the estimated cash flow by the prospective buyer’s required rate of return. The difference between the market price and the fundamental value could measure whether the house is overpriced or underpriced. Chan et al. (2001) defined the fundamental price as the sum of the expected present value of rental income, discounted at a constant rate of return. This study applied the generalized method of moments (GMM) to detect price bubbles with the housing prices of Hong Kong. The result showed that the bubbles in Hong Kong exploded most sharply between 1990 and 1992 and between 1995 and 1997. Hott and Monnin (2008) also indicated that the fundamental house price could be calculated by present and expected future imputed rents and interest rates.
Accordingly, this study proposes rent model to examine the deviation of observed prices from the estimated fundamental values. Besides, the long-term forecasts of the house price became more accurate because of the fundamental value. Regarding a house as an investment vehicle, Mikhed and Zemcik (2009b) applied a present-value formula to derive implications for the relationship between house prices and market tenants’ rents, and illustrate the consequences of bubbles in the metropolitan areas of USA.
In the Chinese literates, Chang et al. (2009) discounted the future rental incomes to capture the market fundamentals and regarded the investors’ required discount rate as the reasonable growth rate of housing bubbles. Based on this, Chang et al.
‧
Space-State model is a good choice for estimating unobservable variables.
Nevertheless, many assumed conditions need to be established before applying Space-state model, which might make the empirical results idealistic. Moreover, Lin (2012) used the Weighted Average Cost of Capital (WACC) to calculate the reasonable capitalization rates and then employed the rental incomes and reasonable capitalization rates to appraise the fundamental value. The bubbles level was measured by comparing the market prices with the fundamental values. This method, which is quite straightforward, directly addresses the problem of discounted rates and averts the problem of duration. And the desired parameters are comparatively easy to obtain.
Briefly speaking, the fundamental house price is widely comprehended as the sum of discounted value of the estimated rental incomes at the required rate of return. It might be noted that the rental incomes must be market-oriented and exclude bubble.
(2) Personal Incomes
It is believed that the cash flow of house basically depends on real disposable incomes. In other words, the reasonable market fundamentals should be determined by the home buyers’ income. Black et al. (2006) pointed out that the fundamental value of residential property could be modeled as the anticipated value of future disposable income discounted at the real discount rate. Using the time-varying present value model, this research explored the existence of house bubbles in the UK market.
Based on the model built up by Black et al. (2006), Chang et al. (2009) also established State-Space model with real income and interest rates to obtain the bubble level.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
building cost, stock market wealth, real house rent and population.
In conclusion, the approach using personal incomes is similar to the one of rental incomes. The market fundamentals can be estimated by real incomes based on some mathematical models.
(3) Macroeconomic Variables
There is a strong view that the fundamental value of a house has a high correlation with macro-economy. After all, the demand and supply in the housing market depend on the macroeconomic environment. Thus the fundamental values can be obtained with the macroeconomic factors. Abraham and Hendershott (1996) used the growth in real income, real construction costs and changes in the real after-tax interest rate to explain changes in the equilibrium price, and then accounts for the changing deviations from the equilibrium price. According to the research above, Bourassa et al (2001) applied primary regression model and error correction model with macroeconomic variables to explore the existence of housing market bubbles in United States, Swedish, Australia and New Zealand. Case and Shiller (2003) performed linear and log-linear reduced-form regressions with macroeconomic variables to explore the relationship between home prices and fundamentals. For examining the existence of a bubble, Hui and Yue (2006) investigated the interactions between housing prices and macroeconomic factors through Granger causality tests, impulse response analysis and the reduced form of housing price determinants.
To sum up, it is a widespread method to establish econometric models with macroeconomic variables to capture the house fundamental values. This method which does not require complex assumptions is very simple and direct.
‧
2.3 Review of Housing Bubble Factors
According to the past literatures, there are plenty of studies exploring the relationship between housing bubbles and other variables. These researches showed that many factors contributed to housing bubbles. This section mainly reviews three kinds of these factors: investment, supply/demand and policies.
(1) Investment
The factors of investment refer to the relevant indexes in the financial market.
The changes of these factors not only influence the cost of real estate investment, but also adjust the investors’ anticipation of the house market. Roche (2001) employed regime-switching models to explore housing bubbles in Dublin. The results found that the economic boom, low interest rates and more returning emigrants would push up the demand for houses in Irish. They also pointed out that once the supply of houses was not adequate to the demand, house prices would inevitably increase. So the Irish government should design policies to raise the supply of houses. Li and He (2011) built two non-linear smooth transition regressions with the money supply M2 and weighted inter-bank rate (IBOR) to examine the non-linear dynamic relation between the house price and the monetary policy in China. The empirical results found that the asymmetric non-linear impact of money supply M2 on house prices was notable but massive money supply could only make house prices change a little. Besides, the weighted inter-bank rate had a substantially weak relationship with housing prices.
Evidently, the housing policies only depending on M2 and IBOR would not be effective. Tsai and Peng (2011) applied the panel cointegration test to discuss the relationship of the bubble with mortgage rate, money supply, inflation rate, homeownership rate and user cost of housing. This study found that expansionary monetary policy lead to speculations and also resulted in bubbles in Taiwan housing market.
Liang and Gao (2007) used error correction model and panel data model to explore the factors which determined real estate price fluctuation. The conclusion indicated that the effect of credit policy on house prices were stronger in the east and west of China. And the impact of interest rate policy is significant but small.
Furthermore, GDP was positively related to house prices, which meant that the
‧
development of real estate market depended on the economic conditions. Agnello and Schuknecht (2011) employed Multinomial Probit model to detect booms in housing prices in eighteen industrialized countries from 1980 to 2007. The estimates suggested that domestic credit and interest rates had a significant impact on the probability of booms and bust occurring.
(2) Supply/Demand
As mentioned above, the supply and demand of houses are determined by macroeconomic environment. By the factors of supply/demand we mean population, unemployment and so on. The mismatch in supply and demand influenced house price directly. Thus lots of researches explore how the macroeconomic factors affect housing prices or housing bubbles. For example, Quigley (1999) studied the linkages between house prices and general economic conditions by basic regression models.
The results suggested that the housing prices would become higher when household income, construction permits and the number of household increased. However, if the owner-occupied vacancy rates were higher, the housing prices decreased. Malpezzi (1999) establish a simple error correction model to investigate housing price changes.
This study found that price changes depended on the measured disequilibria in previous periods. Moreover, the rapid-growing population and income resulted in higher conditional price changes while higher mortgage rates lowered price changes.
Chen and Patel (2002) attempted to explain the strong investment demand and short-run variability in housing prices. And then they indicated that the household income, construction cost and house supply were important house price determinants in the long-run equilibrium as well as the money supply and stock prices affected the house prices effectively in the short-run dynamic. Besides, the non-linear model that they used helped to estimate the short-run fluctuations in house prices but the forward-looking expectations mechanism could not help.
(3) Polices
The exorbitant housing price is unfavorable for steady economic development.
Therefore the government will try to control real estate market through housing polices, which also affects housing bubbles. But it is difficult to quantize the housing
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
policies. Few researches employ econometric model to investigate the relationship between price bubbles and housing policies. Wang and Huang (2013) attempted to explore the long-run impacts of the house purchase restrictions and property tax on housing prices. The theoretical prediction showed that the house purchase restrictions might curb housing prices but the effect was limited. Also, it was indicated that property tax might reduce the short-run housing prices but increase the long-run house prices possibly. At last, they employed 70 cities’ panel data to examine the impact of the house purchase restrictions on house prices and the empirical result was consistent with the theoretical one.
From the literatures above, the researches that investigate the factors affecting the housing bubbles usually focus on the perspectives of investment and supply/demand.
According to the results of these studies, the relations of housing bubbles to investment variables and supply/demand variables are easy to see. This is explained by the fact that the housing bubbles are pushed up mainly by speculation which has a high correlation with these factors.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Chapter 3
Research Method and Data Information
This chapter is divided into two parts. The first part shows the methods for the housing bubbles measurement and the empirical analysis. And the second part describes the variables selected in the empirical analysis.
3.1 Research Method
The purpose of this study is to measure the status of the housing bubbles in the three selected cities and to explore the equilibrium relationship between the housing bubbles and other variables. Based on the literature review，this study defines housing bubbles as the comparison between fundamental values and market prices. According to Lin (2012), the fundamental value of real estate can be calculated by annual rents and reasonable capitalization rate. And the funds which are used to purchase houses usually consist of equity and mortgage. Thus, weighted average cost of capital can be regarded as the reasonable capitalization rate which is adopted by most market participants. In the empirical analysis section, we employ Johansen cointegration method to analyze the housing bubbles of these three cities and other variables respectively. If they have the cointegration relationship, Vector Error Correction Model (VECM) is applied for further study. Otherwise, they should be interpreted through Vector Autoregressive (VAR) model. Finally, we use Granger Causality test to find out the lead/lag relationship between the housing bubbles and the variables. In short, the general process of empirical analysis is as follows:
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Figure 3- 1 Process of Research Method
3.1.1 Capitalization Rate
In real estate appraisal, the capitalization rate is commonly used to value an income generating property. The capitalization rate, which is sometimes referred to as the “cap rate”, is the ratio between the net operating income (NOI) and the transaction price or the current value (Brueggeman and Fisher, 2011). The formula is:
𝑟 =𝑃𝑟𝑖𝑐𝑒(𝑉𝑎𝑙𝑢𝑒)𝑁𝑂𝐼 (3.1)
Generally, NOI is calculated by rental income from operating expenses. According to the equation above, we can estimate the value of real estate by determining the NOI and dividing by an appropriate 𝑟.
3.1.2 Weighted Average Cost of Capital
In general, the investors use both equity and loan to finance their investments.
They must take into account the loan interest they need to pay and the opportunity cost of keeping the money. Accordingly, they expect that their total earnings are more than the costs of capital. To evaluate an investment project, the investors usually discount the future cash flows at the weighted average cost of capital (WACC). The WACC is the minimum return that a project must satisfy its investors (Ross et al., 2009). The formula of WACC works out to be:
Structural Change Unit Root Test Cointegration Test VECM
Granger Causality Test
VAR
Yes No
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
𝑅0 =𝐸𝑉× 𝑟𝐸+𝐷𝑉× 𝑟𝑀 (3.2)
In this expression, the cost of equity is 𝑟𝐸 and the cost of loan is 𝑟𝑀; 𝐸/𝑉 represents the proportion of equity and 𝐷/𝑉 represents the loan-to-value ratio.
The income tax base is calculated by NOI from the expenses of interest and depreciation. In other words, interest is tax deductible which can cut the average cost of capital. So the formula of after-tax WACC is as follow：
𝑅0 =𝐸𝑉× 𝑟𝐸 +𝐷𝑉× 𝑟𝑀× (1 − t) (3.3)
where t is the income tax rate. Since the WACC is the minimum acceptable return, NOI divides by WACC is the reasonable price accepted by most investors.
3.1.3 Cointegration Test
In 1987, Engle and Granger proposed the theory of cointegration, which provides a new way to build up models for non-stationary series. Although the non-stationary time series can be rendered stationary through differencing the series, the differencing series does not have direct economic meanings. However, cointegration which refers to linear combinations of non-stationary time series is an alternative method of achieving stationary. It is noticeable that the series must be integrated of the same order. Furthermore, cointegration implies that there exist long-run equilibrium relationships between the variables.
Engle and Granger also proposed a testing procedure to confirm whether the non-stationary series are cointegrated. The Engle-Granger test employs the residuals of regression to perform the unit root test. Suppose {yt} and {xt} are I(1), we build up a regression model as follow:
𝑦𝑡 = 𝛽0+ 𝛽1𝑥𝑡+ 𝑒𝑡 (3.4)
In this equation, yt is the dependent variable; xt is the independent variable and et is the error term. Then, we use the {et} sequence which is the estimated residuals from the regression equation above to perform the unit root test. The regression of the
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
residuals can be expressed as:
∆𝑒̂𝑡= 𝑎1𝑒̂𝑡−1+ 𝜀𝑡 (3.5)
If the estimated result can reject the null hypothesis |𝑎1| = 0, then we can make a conclusion that the variables are cointegrated.
However, the Engle-Granger procedure cannot make the separate estimation of multiple cointegrating vectors. In this two-step method, since the coefficient 𝑎1 is obtained by the residuals of another regression, the errors produced in step1 are carried into step 2. Fortunately, Johansen cointegration test which is proposed in 1991 can avoid the defects above.
Johansen cointegration test, which is based on VAR approach, uses the maximum likelihood estimation to examine cointegration relationships between the non-stationary time series. This approach can interpret the multiple long-run equilibrium relationship more robustly. Assuming a VAR model of order p and n
Johansen cointegration test, which is based on VAR approach, uses the maximum likelihood estimation to examine cointegration relationships between the non-stationary time series. This approach can interpret the multiple long-run equilibrium relationship more robustly. Assuming a VAR model of order p and n