Comparing two financial crises: the case of Hong Kong real estate markets

Hong Kong is no stranger to bubbles or crisis. During the Asian Financial Crisis(AFC), the Hong Kong housing price index drops more than 50% in less than a year. The same market then experiences the Internet Bubble, the SARS attack, and recently the Global Financial Crisis (GFC). This paper attempts to provide some “stylized facts” of the real estate markets and the macroeconomy, and follow the event-study methodology to examine whether the markets behave differently in the AFC and GFC, and discuss the possible linkage to the change in government policies (“learning effect”) and the flow of Chinese consumers and investors to Hong Kong (“China factor”).regime switching, structural change, small open economy, bounded rationality, banking policy

Testing Alternative Theories of the Property Price-Trading Volume Correlation

This article examines the correlation between the real housing price and trading volume. Contrary to the predictions of standard rational expectation models, a robust positive correlation between the two variables is identified. While no clear lead-lag relationship is found in the raw data, which is more consistent with the downpayment effect model, the medium-run component of the trading volume tends to lead (and Granger cause) the corresponding component of the property price, which is more consistent with the search theoretic model. An explanation for this difference in behavior is suggested and several future research directions are provided.

Comparing two financial crises: the case of Hong Kong real estate markets

Hong Kong is no stranger to bubbles or crisis. During the Asian Financial Crisis(AFC), the Hong Kong housing price index drops more than 50% in less than a year. The same market then experiences the Internet Bubble, the SARS attack, and recently the Global Financial Crisis (GFC). This paper attempts to provide some “stylized facts” of the real estate markets and the macroeconomy, and follow the event-study methodology to examine whether the markets behave differently in the AFC and GFC, and discuss the possible linkage to the change in government policies (“learning effect”) and the flow of Chinese consumers and investors to Hong Kong (“China factor”)

Perception of nonnative tonal contrasts by Mandarin-English and English-Mandarin sequential bilinguals

This study examined the role of acquisition order and crosslinguistic similarity in influencing transfer at the initial stage of perceptually acquiring a tonal third language (L3). Perception of tones in Yoruba and Thai was tested in adult sequential bilinguals representing three different first (L1) and second language (L2) backgrounds: L1 Mandarin-L2 English (MEBs), L1 English-L2 Mandarin (EMBs), and L1 English-L2 intonational/non-tonal (EIBs). MEBs outperformed EMBs and EIBs in discriminating L3 tonal contrasts in both languages, while EMBs showed a small advantage over EIBs on Yoruba. All groups showed better overall discrimination in Thai than Yoruba, but group differences were more robust in Yoruba. MEBs’ and EMBs’ poor discrimination of certain L3 contrasts was further reflected in the L3 tones being perceived as similar to the same Mandarin tone; however, EIBs, with no knowledge of Mandarin, showed many of the same similarity judgments. These findings thus suggest that L1 tonal experience has a particularly facilitative effect in L3 tone perception, but there is also a facilitative effect of L2 tonal experience. Further, crosslinguistic perceptual similarity between L1/L2 and L3 tones, as well as acoustic similarity between different L3 tones, play a significant role at this early stage of L3 tone acquisition.Published versio

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

What account for the differences in rent-price ratio and turnover rate? A search-and-matching approach

We build an on-the-house-search model and show analytically that the rent-to-price ratio (or rental yield) and turnover rate, which are frequently used metrics for the housing market, are jointly determined in equilibrium. We therefore adopt a simultaneous equation approach on matched sale-rental pairs in our empirical investigation, as a housing unit cannot be owner-occupied and renter-occupied at the same time. Our empirical results confirm a higher turnover rate is associated with a lower rent-to-price ratio, as predicted by the model. Furthermore, our results suggest a form of “dichotomy” in the empirical determinants of rental yield and turnover at the real-estate-development (RED) level: the demographic structure, and past return performance affect its turnover rate, while popularity, human capital environment, mortgage burden, and long run rent growth determine the rental yield. No evidence of “thick market effect” is found. The robustness of our results are established through a series of tests. In addition to these findings, our tractable search-theoretic model, a ranking of more than 130 RED in Hong Kong based on the popularity index we construct, and the estimated brand-premium for different major real estate developers may also carry independent research and practical interests

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NSO7040)National Institutes of Health (Grant 5 R01 NS04332)National Institutes of Health (Grant 5 R01 NS21183)National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 1 PO1-NS23734)National Science Foundation (Grant BNS 8418733)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0290)National Institutes of Health (Grant RO1-NS21183), subcontract with Boston UniversityNational Institutes of Health (Grant 1 PO1-NS23734), subcontract with the Massachusetts Eye and Ear Infirmar