香港住宅市场合理价格和泡沫的实证研究【外文翻译】

本科毕业论文外文翻译外文题目：Detecting rational bubbles in the residential housing markets of Hong Kong出处：Economic Modelling，2001，1(18)：61-73作者：Hing Lin Chan Shu Kam Lee Kai Yin Woo原文：AbstractThis paper attempts to conduct an empirical study for detecting misspecification errors and rational bubbles in the residential housing markets of Hong Kong. We focus on a fundamental model that defines market fundamental price as a sum of the expected present value of rental income, discounted at a constant rate of return. Testable implications for detecting misspecification errors and/or price bubbles are explored through the flow and stock approaches. In addition, the paper attempts to identify the amount of misspecification and bubble components in the property price data of Hong Kong.Keywords: Bubbles; Housing market; Modelling1.IntroductionThe total land area of Hong Kong is approximately 1075 km2. However, 80% of the territory is considered too hilly for property development. Therefore, only a tiny portion of the total supply could be used for residential purpose. The need to accommodate a total population of 6 800 000 people on a meager 50 km2of residential land has made Hong Kong one of the most densely populated cities in the world. Without doubt, land is one of the scarcest resources in Hong Kong. How to use the resource efficiently is, therefore, an important question. In a market economy, price is one of the most important pieces of information for formulating government policies. However, if the prices contain bubbles, misguided policies might be made asa result. This problem is particularly important for Hong Kong because the government has, over the years, intervened extensively in the housing sector, despite its well-known reputation as being one of the most ‘laissez faire’ market economies in the world. These intervention measures include, for example, provision of public housing, restriction on supply of residential lands and rent controls in some years. As a result, housing prices are influenced to a significant degree by the government policy. It is, therefore, important to ascertain whether bubbles have existed in the residential housing markets.Apart from the above, there are other reasons that motivate us to study this problem. For example, the Hong Kong property markets play an important role in the economy. Several official figures can illustrate this point. First, more than 45% of all bank loans are directly tied to properties. More than half of those loans are mortgage loans, which totaled in excess of HK$500 billion as at the end of 1997 (Hong Kong Government, 1998a).In additional, the real estate sector contributes approximately 10.2% of the GDP in 1996(Hong Kong Government, 1998b).Finally, income from land auction, rate and stamp duty accounted for approximately 24% of total Government revenue in 1997r1998.1 Because of these important relationships, the devastating effects associated with a bursting bubble, might be quite far-reaching as well as long-lasting. Clearly, there is a need for the authorities to avert the formation of price bubbles in the property markets. Another reason to study this problem is that the prices of residential property in Hong Kong were highly volatile over the last decade. For example, in 1991, the real price for the overall property market rose by 40%. Another drastic change occurred in 1995 when the price fell by 16.2%. It was then followed by remarkable increases of 18.9 and 20% in the next 2 years and a rapid fall of 50% in 1998.Given these drastic fluctuations, it is interesting to investigate whether price bubbles have been formed in this highly volatile period.Despite the importance of this problem, not many studies have been done on detecting rational bubbles in the residential markets of Hong Kong. Most of the earlier papers, such as Peng and Wheaton (1994) and Mok et al. (1995) concentrated on studying the property price without taking the possibility of bubbles into account. In view of this, our paper attempts to fill this gap. To do that, we arrange the discussionin the following manner. Section 2 will discuss the methodology. In particular, we focus on a fundamental model that defines market fundamentals as the sum of the expected present value of rental income, discounted at a constant rate of return. The methods of how to detect misspecification errors an/or speculative bubbles will be discussed. In Section 3, we will present the empirical result. In particular, the magnitudes of misspecification and bubble components will be presented. Section 4 will conclude the major findings of the paper.2. Methodology2.1.Model specificationWe treat property as a good investment, which produces a stream of rental incomes over its lifetime. The current value of a property is therefore determined by the present value of current rental income and next period’s expected market price. The following equation formalizes this relation:P t =)(1t t t P D E Ω++δ (1)where Pt is the real value of property at date t , δ is the constant ex ante real discount rate, E (．∣t Ω) denotes rational expectations based on t Ω , which is a full information set available to the market representatives at time t , and Dt represents the real rental income during the period t .Eq.(1) can be solved by recursively substituting forward for E(P 1+t )and using the law of iterated expectations. The solution is given in Eq.2.2. Noise detectionIn this paper, we follow the signal extraction method of Durlauf and Hall (1989a,b) and Durlauf and Hooker (1994) to investigate the existences of St and B t by the use of the flow and stock test. To carry out the flow test, let us first consider the perfect foresight fundamental price P.Because of Eq., r is known as one period excess return on holding property. Once the information of r is available, it is possible to conduct the flow test. To begin with, let Lt ( x )be the information set available at time t, which is a subset of t Ω .Projection of r onto Lt (x ) captures the fitted value of St . since t 4 and _et 4 are, by definition, orthogonal to Lt (x ). Therefore, under the null hypothesis, r is orthogonal to Lt (x ). Failing to reject this hypothesis implies that St is zero.The implementation of the stock test, however, relies on Eq. from which we knowthat the projection of P onto L(x).can capture the fitted values of B t and St. Therefore, the null hypothesis assumes that P is orthogonal to Lt( x). Failing to reject the null hypothesis implies that the total model noise,Bt + St, is zero.To sum up, there are three possible outcomes when these two tests are taken together. Firstly, if both flow and stock orthogonal conditions are not rejected, price follows fundamental price solution. That is, the total model noise is zero.Secondly, in the case where the flow projection is zero but the stock projection is non-zero, the model noise contains Bt only. In other words, the price sequence is consistent with the general solution Eq. Thirdly, if both orthogonal conditions are rejected, we can confirm that St is present but further analysis for the existence of Bt is required.3.Empirical studies3.1.Noise detectionThe implementation of the flow and stock tests requires the construction of r and P. Since we deal with moment (orthogonal) conditions, the generalized method of moments (GMM) is needed to construct r. To do that, we first estimate δvia GMM, using the flow projections themselves as given by the following orthogonality conditions,The role of the GMM estimator is to help select an optimal δso that the sample correlations between the Lt(x) and r are as close to zero as possible. This can be done by varying δto minimize the following criterion function (Hansen, 1982):where m is the sample moment condition, W is a weighting matrix and N refers to the number of sample observations. A necessary (though not sufficient) condition to obtain an asymptotically efficient estimate of δis to set W equal to the inverse of the covariance matrix of the sample moments, m. However, since the GMM’s estimates are sensitive to the choice of the bandwidth parameter, we therefore use heteroskedasticity and autocorrelation covariance matrix estimators that make use of automatic data-dependent band-width selectors (Newey and West, 1994).The data for this study are taken from the Hong Kong Government Property Review. They contain data on monthly averaged rentals and quarterly averaged prices of the private domestic properties within the class A, which is defined as thoseapartments whose sizes are less than 39.9 m2. The rentals are exclusive of rates, management and other charges. Since the urban areas of Hong Kong consist of three main regions: Hong Kong Island, Kowloon and New Kowloon, we will include all of them for analysis. The sample period runs from the first quarter of 1985 to the third quarter of 1997 for empirical testing, with additional data from the fourth quarter of 1997 to the third quarter of 1998 for the construction of the perfect foresight price. 3.2.Estimation of S t and B tSince we have obtained the estimates of B t, we can further examine the time series property of the Bˆt by using the N1and N2test statistics of Bhargava (1986).The purpose of conducting N1and N2tests is to examine whether the bubble estimates follow, without and with drift, respectively, random walk against the alternative of explosive patterns. The results of these tests, summarized in Table 5, accept the null hypothesis for all the three regions in Hong Kong. In other words, the bubble estimates, Bˆt , do not exhibit linearly explosive property, which indicates that the price bubbles in Hong Kong residential housing markets are not deterministic. This is not surprising since bursting bubbles are more commonly observed than deterministic bubbles. In order to illustrate how the bubbles explode and burst over time, we plot Bˆt of the three urban regions in Figs. 4-6. In general, Bˆt moved from 1985 towards the peaks in 1991-1992 but burst afterwards. Starting from 1995, however, Bˆt exploded again reaching the peaks in 1997.4.ConclusionThis paper reports empirical studies for the existence of unobservable misspecification errors and explosive rational bubbles in the property markets of the three urban regions in Hong Kong. We assume that the property price is composed of fundamental, rational bubble and misspecification error components. The signal extraction approach of Durlauf and Hall (1989a, b) is employed for uncovering the unobservable model noise. Since both the nulls of the flow and stock tests are rejected, this means that the misspecification error exists in the model noises. To measure the sizes of each component in the model noise, we have used the formula of Hansen and Sargent (1980) to calculate the misspecification error. On the other hand, the bubble component is calculated by the projection of P. The paths of the bubbles show that thebubbles exploded most sharply between 1990 and 1992 and between 1995 and 1997. 译文：香港住宅市场合理价格和泡沫的实证研究摘要这篇文章倾向于指导揭开香港住房市场的设定误差和合理泡沫的实验研究。