Risk and Predictability of Singapore’s Direct Residential Real Estate Market

This study explores the topic of the predictability of direct real estate prices in the short-run and the risks facing investors via a case study. Two models are estimated using heteroscedastic and autocorrelation robust ML method. Possible structural shifts of the models are examined. The one assuming that the model captures all the economic influences produces slightly better in-sample fitting. The other model assumes that there could be some important information which is not publicly available. Such information can nevertheless be extracted using Kalman filter. The latter has smaller forecast error in general. We found that a rational speculative bubble is an important predictor of short-run price movement, especially when the market is volatile and noisy. Rental is the only fundamental variable which has any important role to play in the short-run price generating process. Further more, the influence of rental is significant only when the market is inactive. Based on the study, we argue that the risk facing market participants comes not from the rational speculative bubble given its predictability, but primarily from unpredictable local policy shifts.Risk; information; rational bubble; Kalman filter

Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

Unit Root Tests with Markov-Switching

Diba and Grossman (1988) and Hamilton and Whiteman (1985) recommended unit root tests for rational bubbles. They argued that if stock prices are not more explosive than dividends, then it can be concluded that rational bubbles are not present. Evans (1991) demonstrated that these tests will fail to detect the class of rational bubbles which collapse periodically. When such bubbles are present, stock prices will not appear to be more explosive than the dividends on the basis of these tests, even though the bubbles are substantial in magnitude and volatility. Hall et al. (1999) show that the power of unit root test can be improved substantially when the underlying process of the sample observations is allowed to follow a first-order Markov process. Our paper applies unit root tests to the property prices of Hong Kong and Seoul, allowing for the data generating process to follow a three states Markov chain. The null hypothesis of unit root is tested against the explosive bubble or stable alternative. Simulation studies are used to generate the critical values for the one-sided test. The time series used in the tests are the monthly price and rent indices of Seoul's housing (1986:1 to 2003:6) and Hong Kong's retail premise (1980:12 to 2003:1). The investigations show that only one state appears to be highly likely in both cases. The switching unit root tests failed to find explosive bubbles in the price series, which might be due to the fact that the power of test is weak in the presence of heteroscedasticityunit root, three states markov switching, explosive rational bubbles

Signal Extraction with Kalman Filter: A Study of the Hong Kong Property Price Bubbles

Since Flood and Garber (1980), the debate surrounding speculative bubbles has never subsided. A key obstacle to resolve this issue is the identification problem. A bubble is usually inferred from some assumed fundamental determinants of a price. These assumptions could be over-simplified. Furthermore, there might be data measurement errors. In this paper, we attempt to capture such errors with a latent state variable. This variable is extracted with Kalman filter. Based on our empirical comparisons, we find that it is possible to attribute the observed large price swings in the property market of Hong Kong during the 1980s and 1990s to a periodically collapsing rational speculative bubble.rational speculative bubble, misspecification or measurement error, Kalman filter

Unit Root Tests With Markov-Switching

Diba and Grossman (1988) and Hamilton and Whiteman (1985) recommended unit root tests for rational bubbles. They argued that if stock prices are not more explosive than dividends, then it can be concluded that rational bubbles are not present. Evans (1991) demonstrated that these tests will fail to detect the class of rational bubbles which collapse periodically. When such bubbles are present, stock prices will not appear to be more explosive than the dividends on the basis of these tests, even though the bubbles are substantial in magnitude and volatility. Hall et al. (1999) show that the power of unit root test can be improved substantially when the underlying process of the sample observations is allowed to follow a first-order Markov process. Our paper applies unit root tests to the property prices of Hong Kong and Seoul, allowing for the data generating process to follow a three states Markov chain. The null hypothesis of unit root is tested against the explosive bubble or stable alternative. Simulation studies are used to generate the critical values for the one-sided test. The time series used in the tests are the monthly price and rent indices of Seoul’s housing (1986:1 to 2003:6) and Hong Kong’s retail premise (1980:12 to 2003:1). The investigations show that only one state appears to be highly likely in all series under investigation and the switching unit root procedure failed to find explosive bubbles in both prices.unit root, bootstrap, Markov-Switching