House Market in Chinese Cities: Dynamic Modeling, In-Sampling Fitting and Out-of-Sample Forecasting

This paper attempts to contribute in several ways. Theoretically, it proposes simple models of house price dynamics and construction dynamics, all based on forward-looking agents’ maximization problems, which may carry independent interests. Simplified version of the model implications are estimated with the data from four major cities in China. Both price and construction dynamics exhibit strong persistence in al cities. Significant heterogeneity across cities is found. Our models out-perform widely used alternatives in in-sample-fitting for all cities, although similar success only limited to highly developed cities in out-of-sample forecasting. Policy implications and future research directions are also discussed.pre-sale, production constraint, collateral constraint, cross-city heterogeneity, fundamental versus policy

UK Housing Market: Time Series Processes with Independent and Identically Distributed Residuals

The paper examines whether a univariate data generating process can be identified which explains the data by having residuals that are independent and identically distributed, as verified by the BDS test. The stationary first differenced natural log quarterly house price index is regressed, initially with a constant variance and then with a conditional variance. The only regression function that produces independent and identically distributed standardised residuals is a mean process based on a pure random walk format with Exponential GARCH in mean for the conditional variance. There is an indication of an asymmetric volatility feedback effect but higher frequency data is required to confirm this. There could be scope for forecasting the index but this is tempered by the reduction in the power of the BDS test if there is a non-linear conditional variance process

House Market in Chinese Cities: Dynamic Modeling, In0 Sample Fitting and Out-of- Sample Forecasting

This paper attempts to contribute in several ways. Theoretically, it proposes simple models of house price dynamics and construction dynamics, all based on the maximization problems of forward-looking agents, which may carry independent interests. Simplified versions of the model implications are estimated with the data from four major cities in China. Both price and construction dynamics exhibit strong persistence in all cities. Significant heterogeneity across cities is found. Our models out-perform widely used alternatives in in-sample-fitting for all cities, although similar success is only limited to highly developed cities in out-of-sample forecasting. Policy implications and future research directions are also discussed.

Capital appreciation potentials of Chinese residential market : identification of investment opportunities

Thesis (S.M.)--Massachusetts Institute of Technology, Program in Real Estate Development in Conjunction with the Center for Real Estate , 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from PDF version of thesis.Includes bibliographical references (p. 85).The mission of our thesis is to assist residential real estate investors and developers in making more systematic investment decisions when selecting Chinese cities. In particular, our thesis has three major objectives, (1) to understand the residential price appreciation with respect to economic growth among 35 core Chinese cities, (2) to understand the dynamics of the residential market fluctuation, and (3) to predict the residential market movement. Our models have suggested that the residential markets of Tier II Chinese cities shall outperform those of the other tiers in terms of capital appreciation under a sustainable economic growth condition, with Tier I Chinese cities experiencing the least collective growth. Interestingly, our models have suggested that historical performance is a relatively good indicator of medium-term performance, in terms of capital appreciation potentials, under an up-market cycle. Our results have indicated that the capital appreciation performance ranking of our 5-year prediction period to 2012 are relatively consistent with the capital appreciation performance ranking of the historical 9-year trend between 1999 and 2007. In particular, our top five cities with the highest capital appreciation for the 5-year period to 2012 are Xiamen, Ningbo, Nanchang, Taiyuan, and Fuzhou, respectively; in comparison, the top five cities with highest capital appreciation for the 9-year period to 2007 are Ningbo, Xiamen, Qingdao, Nanchang, and Xian, respectively.(cont.) In terms of residential market dynamics, our models have revealed that the increase in sales transaction volume, the decline in real prime rate, and the loose mortgage policy have all contributed to the overheating of the Chinese residential market in 2007. But as the monetary policy and lending standards tighten, the sales volume was curbed and prices lost its steam. We observed that the policy change was not the only cause to the slowdown in sales transaction volume, but also the continued sales price growth; in fact, the policy change was a cause of the over-heated market. If the current pattern continues and supported by favorable policy, we expect the market shall show signs of relief in 2010; however, if prices over-shoot in the coming months, the market performance may actually reverse.by Philip Gin Shun Wang and Jia Qian.S.M

What drives urban consumption in mainland China? The role of property price dynamics

This paper adds to the literature on wealth effects on consumption by disentangling house price effects on consumption for mainland China. In a stochastic modelling framework, the riskiness, rate of increase and persistence of house price movements have different implications for the consumption/housing ratio. We exploit the geographical variation in property prices by using a quarterly city-level panel dataset for the period 1998Q1 – 2009Q4 and rely on a panel error correction model. Overall, the results suggest a significant long run impact of property prices on consumption. They also broadly confirm the predictions from the theoretical model.consumption; house prices; China; panel data

Important Lessons from Studying the Chinese Economy

In 1979 the United States and China established normal diplomatic relations, allowing me to visit China and study the Chinese economy. After doing so for thirty years since and advising the government of Taiwan in the 1960s and the 1970s and the government of the People’s Republic of China in the 1980s and the 1990s this is an opportune moment for me to summarize the important lessons that I have learned. The lessons will be summarized in four parts: on economic science, on formulating economic policy and providing economic advice, on the special characteristics of the Chinese economy and on the experience of China’s economic reform. At the beginning I should comment on the quality of Chinese official data on which almost all quantitative studies referred to in this article were based. Chow (2006(a)) has presented the view that by and large the official data are useful and fairly accurate. The main justification is that every time I tested an economic hypothesis or estimated an economic relation using the official data the result confirmed the well-established economic theory. It would be a miracle if I had the power to make the Chinese official statisticians fabricate data to support my hypotheses. Even if I had had the power, most of the data had already been published for years before I conceived the ideas of the studies reported in this article.China, Chinese economy, Taiwan, economic reforms, data

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Search of Attention in Financial Market

This study employs correlation coefficients and the factor-augmented vector autoregressive (FAVAR) model to investigate the relationship between the stock market and investors’ sentiment measured by big data. The investors’ sentiment index is constructed from a pool of relative keyword series provided by the Baidu Index. We target two composite stock indices, namely the Hang Seng Index and the Shanghai Composite Index. We first compute the Pearson product-moment correlation coefficient to find the degree of correlation between keywords and composite stock price indices. Then, we apply the FAVAR model to obtain the impulse response of stock price to the investors’ sentiment index. Finally, we examine the leading effects of keywords on stock prices using lagged correlation coefficients. We obtain two main findings. First, a strong correlation exists between investors’ sentiment and composite stock price: Second, before and after the launch of the Shanghai-Hong Kong Stock Connect, the keywords affecting the fluctuation of the Hang Seng Index are different

An Economic Analysis of Housing Market instability and Affordability in China

Applying an intertemporal optimization model proposed by Aizenman and Marion (1991), this research quantifies instability in the Chinese housing market. Although the Chinese government established numerous real estate policies to ensure the stability of the housing market, the regression analyses indicate that housing policies had no significant impact on the stabilization of the Chinese housing market. Alternatively, macroeconomic factors are identified as significant explanatory variables to the instability of housing prices. In addition, this research computes the median multiple for major cities in China and provides an alternative means of investigating the abnormal housing price situation in China