On China's Real Estate Housing Price - Research Paper Example

? Research on China's real e housing price INTRODUCTION Mechanism of pricing helps in balancing supply and demand inthe property market. In addition, it enables commodity price decisions. In order to avoid losses and poor market forces, we find that the pricing decision is undertaken in line with the present market trend. There is variation in price depending on the commodity type to be sold, in the property market. Nevertheless, in any decision-making as regards the property market, it is true that the price mechanism should be taken into consideration as a sales determinant; that is the higher the price the lower the rate of purchasing and the reverse is true. Marketing research statistics that were taken in the past five years indicate that in the property market, role of pricing mechanism is to balance purchase and the rates of selling. In this study, we are attempting to examine the determinants of the housing prices in China, with a particular focus on the monetary variables. There are three main concerns, which motivate this study. These are the exploration of the general relationship that exists between the pricing of houses and the monetary policy, even though the Chinese experience might not be ideal. We also feel that it is important to use the non-linear modeling concept known as NARMAX that selects forms and lags structures in an automatic manner for the individual explanatory variables. Lastly, the study hopes to draw some essential policy implications for the housing prices management in China. BACKGROUND/HYPOTHESIS A fall in the housing prices that started in the year 2007 ultimately resulted to the worst economic recession and financial crisis in the world in nearly eight decades, or eighty years. However, underscoring this important debate is the more complex academic question regarding the relationship between asset price and monetary policy. The maintenance of price stability is largely considered to be the most fundamental monetary policy’s goal. In this framework, the price stability is usually defined as the consumer price index’s stability. In the years ensuing to the world financial crisis, the China experienced a period of fast growth in her Gross Domestic Product with modest consumer price index inflation. Thus, there was no need for the government to increase the rates of interest or even tightening the liquidity conditions. Since the global crisis, we find that there has been rising recognition among policymakers and economists that it is important that the central banks monitor asset prices together with the prices of goods. However, it is still not clear whether it is feasible for the formal incorporation of asset prices into the objective function of monetary policy. Nevertheless, even if this is possible, it is quite difficult to control the asset prices as compared to the regulation of the prices of goods. In trying to establish real estate pricing in China, it is therefore important to consider answering the following question: What is the effect of monetary policy on asset prices? What are the determinants of real estate pricing in China? What are the trends in the house pricing in China? ANNOTATED BIBLIOGRAPHY/ LITERATURE REVIEW Hongli Z. 2011. The Relationship of House Price Rising Rate and GDP Growth Rate. Mason, OH, Cengage Learning. In this context it is well illustrated that the development of the real estate industry is very important to the people`s livelihood and the national economy. The real estate regulation is very significant content of the microeconomic regulation and it is also invariably controversial. The regulation of house price such as controlling the house price`s rising extent within the normal range is a Key to real estate regulation1. According to the calculations of the target range in house price regulation, the house price growth rate/GDP should be controlled within {- 0.31, 0.86} among the first-tier cities and [-0.42, 1.17] among the second inter-cities. China has made real estate regulation laws and regulations in a series for the purpose of alleviating the social and economic contradictions that resulted from the rapid growth in rapid houses. From 2009 in the second half year, the real estate market of China make an adjustment in terms of regulations governing the real estate in the international financial crisis. During that time, the government was facing a difficult situation in regulating the real estate price since it was growing in a very fast way and in a larger volume. However, it is not very clear whether the rate in which the house price is rising in China is a normal situation, whether it is necessary for the government to put regulations on the house growth rate or whether the regulative measures put across by the government have made an achievement on the predicted target2. When the extent to which the house price rises yearly is within the required range, then this is considered as normal and therefore does not require a controlling or regulation from the government. In case it happens that the rising extent of the rising house price is more than the upper limit of a normal range of fluctuation, then it might be considered as an` overheated` real estate market and therefore needs a contractionary regulation. The declining extent of the house price is more than the upper limit of a fluctuation range that is normal, then it is considered to be an `overcolded ` real estate market and therefore requires an expansionary regulation. Moreover, running states of the real estate market can as well be divided in to three groups as follows; the normal, abnormal and basically-normal. In recent times, the main objective of the Chinese house price regulation is preventing the aspect of extreme rise in house price. This paper reflects on two significant indexes for the purpose of bringing about a convenient study of the target range in price regulation. The selected indexes are the house price rising rate / the urban GDP growth rate and the house price rising rate. The calculations of normal range between the two indexes are also presented in Composite Simulation Method. This method was mainly established on a basis of the Japanese experience that determined critical values of indexes according to statistical laws. In addition to that, the method also played a big role in giving judgment to the running state of the real estate market3. However there are other traditional methods that can be used in calculations whereby the critical values are determined. In this case, the traditional method used to determine the critical values is the Empirical Data Method. Due to the fact that this method is not accurate, two additional methods can be used to determine the critical values. These methods are the neutral network method and the theory method that is mainly based on the postulation of the normal distribution. The application of the neutral network method is at the start, it is not mature and perfect enough at the recent times4. China business guide: gateway to the land of opportunities. 2005. Singapore: China Knowledge Press. From the China Business Guide, the aspect real estate price in China is well illustrated. Since the year 1998, investment in China`s real estate has maintained a double digit in terms of its growth-rate. The central government of China made multiple policies on land, the buyer qualification and finance in 2010. This was mainly to regulate the real estate market. Some of the main cities in the country such as Shanghai and Beijing also came up with restriction orders to govern the investment on real estate. However, the house prices in Beijing and Shanghai continued to rise sharply despite of the restrictions throughout the year with a growth rate percentage of 42% and 44%. As a result of the fast growth in the house prices, several questions have been asked by scholars to explain as to why the prices are growing rapidly. Some of the factors that were suggested by the scholars to have led to the changes in house price are; the urbanization growth, increased income among the consumers and the excessive speculation. In order to specify the housing bubbles, scholars have used several methods from different angles that are categorized in to three groups; the index method, the direct test and the indirect test. In the direct test category, it has a very good theoretical basis whereby the existence of bubbles can easily be determined through a comparison between the actual price and the fundamental price. However, in this method, it is not an easy task to obtain the rent and the discount rate. In the indirect category, the features of statistical indicators are analyzed; however, the defect is the inability to measure the price bubble sizes. In the Index method, the existence of the bubbles is obtained through the process of analyzing the indicators belonging to the real estate industry. This method is relatively simple and straightforward though the choices of indicators are mainly based on the subjective experience. Zhou, Qingyuan. 2011. Applied Economics, Business and Development International Symposium, Isaebd 2011, Dalian, China, August 6-7, 2011, Proceedings. Springer-Verlag New York Inc. This source provides brand new theoretical references for the real estate price in China. When theory is applied to study the degree of real estate bubbles in China and Hong Kong, it is observed that the degree of real estate price in china is not as high as that of Hong Kong during the duration that the real estate price is at the peak5. This despite the fact that real estate price in China has been on a rapid growth since the year 2004. In general, the degree of real estate bubbles in China is low in the inland of China. However, it is indicated that in some of the coastal areas such as Xiamen City, it is relatively very low. Since 1990s, the government of China has been on the upper front in trying to control the real estate market thus enabling it to continue improving its ability in that sector. The level of income among the Chinese citizens undergone through a faster growth and the demand for the houses has also increased thus leading to an increase in housing price6. The real estate market in China is said to be laggard but its level of development is high at the recent times. Due to the above reasons, the degree of real estate bubble in China is taken to be less serious and will therefore bring less harm towards the healthy development of the real estate market in China at the recent time. From the view of a prediction, a given case such as the Hong Kong real estate market in the year 1997 has a very low probability to happen again if there are no external factors to intervene. Wang, ShouQuing. 2005. Chinese management reflections, trends and opportunities. Bradford, England: Emerald Group Pub. As it is indicated in this book, China has undergone through a rapid economic growth in the last two decades which is mainly accompanied with the rapid development in the real estate market. The Asian financial crisis that hit the real estate markets in the East Asia and the South East, had a very little impact on the real estate markets of Shanghai and Beijing. The increase in house prices in China was as a result of certain emerging issues such as; the urbanization, the rapid economic growth, the increased demand for the lands that were d in the urban areas and the increased number of dwellers in towns. The Shanghai Housing Price Index (SHHPI) in China Real Estate index System (cries) had only 656 points in the month of January in the year 2001 but later rose by a high percentage of 63% to become 1084 points in the month of December in 20037. This fluctuation in the housing prices affects many households, fortunes of corporations and also plays a significant role in the level of microeconomic. The observed changes in the house prices were reported to have the capability to generate and reinforce the fluctuations in an a given economy on a wider context. It is also very significant to put a relationship that is much reasonable between the microeconomic and housing prices. This paper provides foreign investors with necessary information so as to properly understand the Chinese housing markets . This would enable them to make proper decisions on matters to do with investing in the real estate sector Tan, Honghua. 2012. Technology for education and learning. Heidelberg: Springer. The author of this book presents various issues in reference to the situation in China whereby the housing price correlated with the already completed real estate investment in a very positive way. To support this point, the as the completed investment in Chinese real estate increases, the housing price also increases as a result of the newly created estates. However, the housing price negatively correlates with the land area developed in China. To illustrate this further, the increase in land area developed leads to an increase in supply of real estate thus resulting to decrease in house prices8. In China, the introduced demand factor in to model was the per capita GDP; however, the variable that was representing the population did not match with it. This therefore meant that the population had no important positive influence towards the house price. In addition, there was no necessary difference the housing price in Western district and in the central district since the housing price in the Eastern district was much higher than those of the central and Western districts by approximately 1494.67 Yuan per square meters on average. The main reason for this might be because of the fact that there was disequilibrium of the development of economy in different districts of China. In China, the more improved infrastructure is located in the Eastern districts. As a result, more people in China prefer to live in the Eastern districts thus increasing the demand for houses as compared to other districts with little population. This enables the house price to rise more significantly since the demand is also high. In terms of earning, the people working in the Eastern districts usually earn more money as compared to the people living in other districts. At the end of all, the regional difference in terms of prices arises thus leading to unequal increase in prices in the country. For the government to boost the housing prices in other districts, it has to coordinate regional development between different districts and also to reduce the income gap that emerges in between the districts9. Ashvin Ahuja, Lillian Cheung and Nathan Porter. 2010. IMF Working Paper, Hong Kong, EIA Publ. The IMF Working Paper makes a comparison in the movements of China`s residential property prices to those that that are implied by market fundamentals. Two different approaches that are used in measuring the benchmark prices are also applied. The first approach is based on panel regression that links prices the long-term fundamentals while the second one is mainly based on the relationship between the rent, price and the ownership cost. The latter measure is then used to characterize the price deviations in the comparator countries and also in measuring the differences and similarities with the China`s experience. However, as the prices run ahead of fundamentals in some of the market segments, it does not seem to be the case nationally. As it is compared to the property markets in other different countries, the pricing misalignments in the country of China seem to have relatively been short lived relatively through10. Hypotheses H1- The rise in house prices in China cover structural shocks to both the supply and demand sideds of the Chinese housing market. H2- A huge demand for the residential housing was brought about by the 1998 housing reform, which can be justified by the increase in house or even personal disposable income following thirty years of fast growth. Data – Dependent Variable Dummy Variable for the rise in housing prices To have a successful examination of the probability of a rise in the housing price a particular year, however, the dependent variable must be a dummy variable equal to one the rise in housing price during that year, zero if otherwise. Immediately the importation of data into the imported stata is done, I can code to create a dummy dependent variable somehow easily. Data – Independent Variables; rate of exchange China’s policy of exchange rate has played a critical role in the international trade and FDI roar and improved the country’s competitiveness in the attracting or encouraging of the FDI flows to the country and also the creation of favorable conditions for the maximization of exports. It is true that such fluctuations in the rate of exchange might influence changes in the prices of housing. In the case that the probability of rising in housing prices is not correlated with changes in the fluctuations in the exchange rates, the data can be manipulated and then changed into a dummy variable. This enables the model to determine if increase in the housing prices is more likely to happen. H1- Positive correlation between the fluctuations in the currency and the probability of rising housing prices H2- Positive correlation between the increase in the housing prices and the possibility of the prices rising Data – Independent Variables; inefficient housing supply Inefficient housing means that in trying to achieve fast growth in GD, the government of China encouraged commercial housing investment and housing, consequently no motivation to have enough supply of enough economic affordable housing. H1- Positive correlation between insufficient housing and the probability of rise in housing prices DATA / METHODOLOGY To describe the unit of analysis, the data source and the strengths and weaknesses of the data, the following has to be considered; In the term selection and the parameter estimation, the impacts of several microeconomic variables on the aggregate house price movement has been a very difficult task to establish. Frequent interactions between several factors have come in to being and as the determinants impact on house price changes necessarily not only in clear-cut ways or direct ways but also in a more subtle ways11. To illustrate this further, over the last ten years there has been a tremendous pressure for the RMB appreciation that has left a very little space for the China`s central bank to adopt the interest rate policies independently. The decision marking is done in the international policy coordination whereby if the spreads between the interest rates of China or other nations widen, then speculative money in greater volumes would seek its way in to China. The rate of the exchange rates would also affect inflation expectations on a great deal in China. As it is the case with other systems of real world, mechanism that are behind the house price movement are at many times not well known and be rendered as nonlinear or time-variant. However, the most difficult part of the process of identification is the determining the structure of house pricing model12. The parameter method of estimation Orthogonal Least Square (LS) that is commonly used has no capability to determine any form of importance from the possible terms of NARMAX model. The usually employed information criteria for the purpose of selecting the parsimonious models basing on a data set that is the same may involve burdens that are computational. This is due to the presence of large candidate variables numbers and the large computational requirements of the standard method when it is applied to large models that are non-linear. The algorithm of Orthogonal Least Square (OLS) was developed by Billings, Korenberg and Liu in the year 1988 for the purpose of overcoming these difficulties. The OLS allows for the significance of model terms be decided upon basing on values of the Error Reduction Ratio (ERR) of each of the terms. Moreover, OLS algorithm that is original has a major drawback which is a choice of initial orthogonalized position. VARIABLES/MODEL The mist fundamental variables identified as ones that drive the house price dynamics are price variables and the monetary policy, which include the rate of mortgage, the price of producer and the real effective rate of exchange. Surprisingly, key real economic variables like the individual or personal disposable income, the Gross Domestic Product, the value added industrial output as well as the international trade only shows weak independent explanatory strength or power for the dynamics of house price, and some of them should be joined or put together with the price or monetary variables to show non-linear effects13. This is very important information that helps in the understanding of the Chinese housing prices formation, and also should offer important insights on the best way of using monetary policies in the management of asset prices. ANALYSIS AND FINDINGS As we focus on the analysis, the following has to be considered; In summary, the results for linear formulations shows that most important factors that are explanatory for the house price dynamics are the mortgage rate, broad money supply, PPI, house rental and the real exchange rate14. The variables of real economic activities such as the personal disposable income, the GDP, exports, imports and value-added in the industrial output and other variables such as CPI, land price, stock index, foreign reserves and stock index have a very weak power of explanation from a statistical view point. Generally, the second degree of non linear house price reference model is believed to be containing over 20000 terms when all the combinations are included. The empirical exercise lays on the table the importance of nonlinearity in the process of house price determination. Generally, the obtained results of the nonlinear modeling shows that a total of nine microeconomic variables such as; the mortgage rates, exchange rate, GDP, PPI, value-added industrial output, stock index, exchange rate, hot money and the past house price have nonlinear important effects on the house price dynamics. The combination of the mortgage rate and the industrial output contains the most explanatory power. Other variables of macroeconomics such as the land, changes in the personal disposable income, CPI, house rent, foreign services, imports and the RMB appreciation expectations have no important nonlinear impact. To summarize the results obtained by the research on three linear and nonlinear formulations in China such as the PPI, REER and the mortgage rate, they all exhibit both the nonlinear and linear impacts on the house prices. There are five factors which do not show the independent importance of linear effects such as; the GDP, VAI, hot money, export and stock market index. Other variables like the personal disposal income, the land price and the CPI have no explanatory power for the house price dynamics. CONCLUSION In conclusion, the main aim of this paper is to critically analyze various effects of different microeconomic variables on the movements of house price in China. The OSL approach has been adopted to investigate whether the recent surging in house prices in China have been quantitatively justified by changes in the fundamental factors. These fundamental factors revolve around the monetary phenomenon such as the personal disposal income and land price. Many findings that are interesting have also been obtained such as the non-linear and linear estimation results. This helped in identifying the number of monetary variables in Chinese housing price as the most significant explanatory factor. The paper also illustrates that factors such as GDP, VAI, stock market index and export have nonlinear effects on the house price dynamics that are very significant when they are linked to the price or monitory value. The personal disposable income and the key income variables have no explanatory power on the Chinese housing price. The testing of both out-of-sample and in-sample forecasting performance in all the three OSL models has also been made. Both the nonlinear and linear models that were estimated by using the OSL models approach, proved further to be very powerful tools to be used in predicting the Chinese housing price in the near future. In a close comparison, the linear mode is seen to be more robust towards the parameter of out-of-sample changes, on the other hand, the nonlinear model is has been observed to have the best in-sample in terms of prediction. The act of adding a long-run relationship that is cointegration and restrictive to the OSL specifications, does not improve either the out-of sample or the in-sample predictions on a great deal. Finally, this study involves both the policy and academic importance. However, there is a continual debate with concern to the relationship between monetary policies in China and the boom of house price. The case study from the experience of China indicates that price variables and the monetary policies are most likely to be the main factors that influence the house price in the country. This therefore means that other economic variables have very low important impact on the housing price. The findings also indicate how the policy makers influence the real estate housing price on a great deal. References Hongli Z. 2011. The Relationship of House Price Rising Rate and GDP Growth Rate. Mason, OH, Cengage Learning. China business guide: gateway to the land of opportunities. 2005. Singapore: China Knowledge Press. Zhou, Qingyuan. 2011. Applied Economics, Business and Development International Symposium, Isaebd 2011, Dalian, China, August 6-7, 2011, Proceedings. Springer-Verlag New York Inc. Wang, ShouQuing. 2005. Chinese management reflections, trends and opportunities. Bradford, England: Emerald Group Pub. Tan, Honghua. 2012. Technology for education and learning. Heidelberg: Springer. Ashvin Ahuja, Lillian Cheung and Nathan Porter. 2010. IMF Working Paper, Hong Kong, EIA Publ. Mckenzie, D. J., Betts, R. M., & Jensen, C. A. 2011. Essentials of real estate economics. Mason, OH, Cengage Learning. Gonzalez, I. 2003. California real estate economics. Chicago, Dearborn Real Estate Education. TSE, Y. -C. R. 1994. Real estate economics: theory and policy ; with reference to Hong Kong, Singapore and Taiwan. Hong Kong, EIA Publ. Read More