Real Estate Prices In China Research Articles

The Externality Impact of Urban Rail Transit on Urban Real Estate Prices in China

China has the largest and fastest growing urban rail transit system in the world. The system plays an important role in China's urbanization process, city governance and residents' daily commutes. The development of urban rail transit (URT) has influenced China's urban spatial structure and real estate supply, and has produced externalities on real estate value, including both positive and negative externalities. The research shows a positive correlation between URT and China’s real estate prices in general. The closer rail transit systems are, the greater the premium on real estate prices. In addition, the impact of rail transit on real estate prices shows a trend of first increasing and then decreasing. Different land use types and urban areas will also be affected. The research also found that the three stages of URT construction: planning, engineering construction and traffic operation have different effects on housing prices. At the same time, the impact of URT on land price will also change with China's economic situation and urbanization process. These influencing mechanisms above between URT and real estate prices may be linked to some factors. This research attempts to explain it by introducing urban spatial and land rent transformation, changes in real estate supply and demand, gentrification process, and different employment and housing pattern. This research points out the shortcomings and possible research directions of existing studies. In order to provide a reference for academic research and government policy making.

The Impact of Two-pillar Policies on Real Estate Prices in China

Since the turn of the 20th century, China's real estate industry has been expanding, playing an increasingly vital role in fostering the growth of the national economy. On the one hand, rising housing costs can stimulate the growth of linked companies. Nonetheless, it also causes a number of social issues. Through theoretical research, this study examines the impact of two-pillar policies (monetary policy and macroprudential regulations) on real estate values in China. The analysis reveals that quantitative monetary policy influences real estate prices via credit scale, whereas price-based monetary policy influences real estate prices via loan interest rate. However, there is a tension in monetary policy between "boosting the economy" and "stabilizing property prices". When monetary policies govern housing prices, macroprudential policies, particularly LTV instruments, can avert systemic problems and compensate for the spillover effects on the real economy. Consequently, it is imperative to establish and enhance the regulatory framework for two-pillar policies.

Comparative study on the influence of business cycle and financial cycle on real estate price in China

Comparative study on the influence of business cycle and financial cycle on real estate price in China

Risk Logic and Policy Enlightenment of Real Estate Bubble and Financial Crisis

With the rapid growth of economy, residents’ demand for real estate is not only limited to residential function, but also as an important investment product to buy. Therefore, under the joint action of these two demands, the real estate prices in China have increased rapidly. At present, the predicament of the real estate industry, especially the fluctuation of house prices, can easily cause the illusion that the real estate bubble is squeezed out or reduced, thus neglecting to guard against it and causing great disaster. Both the expansion and bursting of bubbles will lead to wealth transfer and social stratification, and the resulting political risks are more likely to lead to market panic or even financial crisis than financial risks. This paper analyzes the relationship between China’s real estate industry and financial industry, and then puts forward some policy suggestions to prevent the financial crisis caused by the real estate bubble in China.

외국인직접투자가 중국 부동산시장에 미치는 효과에 관한 실증 연구

In this paper, we attempt to examine the effects of foreign direct investment in real estate (FDIRE) on the housing prices in China. We employ ARDL (AutoRegressive Distributed Lag) bound test (Pesaran et al. 2001) to investigate the long-rum relations between macroeconomic variables. We use FMOLS (Phillips and Hansen, 1990) and DOLS (Stock and Watson, 1993) to calculate the individual cointegration vectors. We select four categories of commercial residence buildings as dependent variables, namely, business, commodity, residence and office buildings. Based on existing literature, we employ four macroeconomic variables as independent variables, i.e. interest rate, exchange rate of RMB to US dollars, FDIRE and industrial growth ratio as proxy variable of GDP. We find that the FDIRE has a significant negative effect on the real estate prices in China. This implies the Chinese government should keep providing favorable conditions for more FDIRE to stabilize the booming real estate market in China.

The effect of deed taxes on real estate prices in China

With rapidly accelerating real estate prices in China, the government has used changes in deed tax rates to affect demand. Tax rates have been increased for buyers of large or multiple homes to discourage speculative demand. We analyze the effects of deed tax rates on real estate prices over the period 2004–2013, estimating traditional dynamic models and dynamic spatial models. Results indicate that deed tax rate increases have a negative effect on pre-tax prices for both residential units and land use rights. Spatial spillover effects are also found in the residential market, with larger effects in the long term.

我国汇率与房地产价格的波动关系研究

我国汇率与房地产价格的波动关系研究

Tail causalities between monetary supply and real estate prices in China

The asymmetric and segmented causality between the monetary policy and real estate market in China is crucial but remains mystery. With the application of quantile causality test, this article investigates nonlinear dependence between property prices and money supply. Our results show that the tail causality exists in many cities in China. Moreover, we find that small-sized cities and inland cities are more sensitive to the broad money (M2) changes when the housing market return is in the tail quantile intervals. These findings can help the Chinese government formulate appropriate monetary policies regarding their implications in the real estate market.

Study on the Influence of Monetary Policy on Real Estate Price in China

The real estate industry has been in a stage of rapid development since 2000, and its pulling effect on economic growth is very significant, but it has continued to develop at a rapid pace over the years. The rising prices that follow have also had a lot of impact on all aspects of society and economy. However, a country’s macroeconomic control policy, especially monetary policy, has a very important impact on the development of the real estate industry. For example, the expansionary monetary policy since 2008 has increased the money supply, so that the real estate market investment and consumption rising, house prices accelerated. Although there are also a lot of restrictions of macroeconomic policy on the rise of house prices, the effect is not obvious. Therefore, it is necessary to make use of the policy means to regulate the real estate industry in order to achieve healthy development. This paper first analyzes the mechanism of monetary policy influencing real estate price, and then discusses the effect of monetary policy on real estate market by using empirical research method. It is found that both money supply and interest rate will have an impact on real estate price. Finally, combined with the conclusion of the analysis, some policy suggestions are put forward to improve the monetary policy system for the healthy development of the real estate industry.

城市化与工业化对我国房价影响路径差异的研究

城市化与工业化对我国房价影响路径差异的研究

Effects of Short-Term International Capital Flows on China’s Real Estate Prices

Nowadays, the real estate prices in China have shown a continuous upward trend. Prices in the first-tier cities such as the north and the Guangzhou-Shenzhen are getting higher and higher, and ordinary people are becoming increasingly unaffordable. High housing prices have not only put pressure on people’s lives but also have had many adverse effects on China’s economic development. Many scholars at home and abroad think that the continuous rise of house prices is closely related to the flow of international short-term capital. International short-term capital when entering the Chinese market, because of its speculative easy to create a bubble, bringing huge risks. Through theoretical and empirical research, this paper analyzes whether the international short-term capital flows have an impact on house prices. Finally, it concludes that the inflow of international short-term funds to the real estate market in China has a positive impact on the real estate market in China, and to a certain extent, has promoted the real estate prices. The entry and exit of hot money have a great volatility effect on the real estate market, which has a great impact on the long-term, healthy and stable development of China’s real estate market.

Particulate air pollution and real estate valuation: Evidence from 286 Chinese prefecture-level cities over 2004–2013

Whether, and to what extent the air pollutions depress the real estate prices in China have increasingly become to be important issues yet to be well addressed. By applying a very unique panel data set on PM2.5 concentrations from 286 Chinese prefecture-level cities for 2004–2013, this study quantitatively assesses the impact of PM2.5 concentrations on real estate prices in China. The preferred empirical results demonstrate that: (1) PM2.5 pollutions have negatively significant effects on real estate prices in China. Specifically, 1μg/m3 increase in PM2.5 concentrations is associated with a decrease of 46RMB/m2 in real estate prices on average. (2) The estimated effects differ widely across Chinese cities, with higher-ranking cities being more substantially impacted. Additionally, the negative effects become increasingly statistically significant and quantitatively large over time. (3) Finally, 1μg/m3 increase in PM2.5 is responsible for about 5200 hundred million RMB losses in real estate valuations, approximately accounting for 0.9% GDP in 2013 of China. The findings are rather robust to various alternative settings.This article is part of a Virtual Special Issue entitled 'Comparing Sustainable Energy Policies in Northeast Asia: toward the harmonized Public-Private Partnership'.

Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy

In a similar way to the stock market, the housing market in China has often been portrayed as highly speculative, giving rise to “bubble” concerns. Over the last decade, residential prices increased every year on average by double digits in Beijing or Shanghai. However many observers and researchers argue that fundamentals of the housing sector, both sector-specific and macroeconomic, may have been the driving force behind housing price volatility. While existing empirical work exclusively relies on the government housing prices which may suffer from the well-documented downward bias, this paper uses original high frequency unit price as well as transaction series for the residential resale housing markets of Beijing and Shanghai between January 2005 and December 2010 to test alternative hypotheses about housing prices volatility.We propose a sequential strategy in five steps integrating several techniques previously developed in a piecemeal and scattered way. First, we construct daily hedonic prices. Second, in order to search for the possible presence of bubbles on such high-frequency data, we propose using recently developed tests of an explosive root as an alternative to the unit root hypothesis. The third step is generated by the necessity of handling microstructure noise present at a daily frequency, thus filtering the raw data to extract a random walk component. The fourth step extracts a slowly changing monthly volatility component from the filtered daily hedonic real estate data. Finally, in so far as the presence of bubbles does not seem to characterize the residential housing market in major Chinese cities, such as Beijing and Shanghai, in a fifth step we show that fundamentals are able to explain slowly changing volatility, as well as transaction volumes in these first‐tier cities.

Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy

Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy

Combining Online News Articles and Web Search to Predict the Fluctuation of Real Estate Market in Big Data Context

The real estate price is of paramount importance in both economic and social fields. It is a key indicator of the operation of real estate market and its prediction is essential in the decision-making process of both average people and official governments. Past researchers on this topic have already proposed several prediction methods including linear regression models, nonlinear regression models and machine learning models. Nevertheless, those models have generally neglected the impact of human behavior, which we believe is a significant factor of the real estate price prediction. What’s more, past studies have shown that news sentiments could improve the prediction performance of real estate price. Search engine query data were studied to reflect web users’ behavior by analyzing the frequency of words searched by online users. Researchers have already used the news sentiments and query data for prediction, respectively. But none have combined them together as an integrated model. In this paper, we propose an integrated method that throws new light on the prediction of real estate price in China by integrating these two factors into the forecasting model. In our method, we extract sentiment series from both news data and search engine query data by adding weights to original sentiment series that are produced by news data alone. Then both the weighted series and original ones are used as inputs of several well-acknowledged data mining models, including SVR, RBFNN and BPNN, to produce prediction results. To validate the integrated model, we apply it to four representative cities in China respectively, and compare the results produced by the integrated model using weighted inputs with non-integrated ones using original inputs. The results show that for every one of the four cities, the integrated model generally leads to lower prediction errors than the non-integrated ones. This not only validates the model’s accuracy and universality, but also proves the hypothesis that human searching behavior as a strong impact in typical Chinese cities’ real estate market and can enhance the prediction accuracy of real estate prices.

Monetary Policy's Influence on Real Estate Price in China: An Analysis Based on VAR Model

Monetary policy has a significant effect on real estate price, and monetary policymakers need to have quick response. Based on the assumptions that monetary policy and real estate price influence each other and variables affect one another with a lag, A VAR model is designed and modified. Through impulse-response analysis and variance analysis, the influence of money supply and that of interest rate on real estate price are tested and compared. We found that: both money supply and interest rate could affect the real estate price; interest rate has a bigger influence that money supply does; as time goes on, the influence of money supply changes little, but that of interest rate enhances; interest rate policy is not easy to control and it will lead to a fluctuation of economy and the fluctuation may enhance, money supply is a better method to regulate real estate industry instead.

Application of Support Vector Machine in Determination of Real Estate Price

The problem of real estate prices are the current focus of the community's concern. Support Vector Machine is a new machine learning algorithm, as its excellent performance of the study, and in small samples to identify many ways, and so has its unique advantages, is now used in many areas. Determination of real estate price is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine (SVM) is proposed to forecast the price of real estate price in China. The experimental results indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

Trend in Real Estate Prices in China, by Region

Trend in Real Estate Prices in China, by Region