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The present study used a new perspective that integrates the commonly observed market characteristics to analyse why the ripple effect is formed. This study proposed a model to infer that in a regional housing market with a relatively low trading probability, low trading volume, high price volatility, and low price efficiency coexist and cause the market to lag behind other regions in information response, forming a ripple effect. The empirical tests used data cover four regions of the United States (Northeast, Midwest, South, and West) between January 1999 and May 2020, and showed that the housing market in the South had the greatest liquidity and the lowest housing price volatility, however, housing prices were more informative in the South region. The empirical results also revealed the characteristics of the ripple effect in the US housing market. Changes in house prices occur earlier in the South, and information is transmitted from the South to the Northeast.
I-Chun Tsai. Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading. International Journal of Urban Sciences 2021, 1 -25.
AMA StyleI-Chun Tsai. Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading. International Journal of Urban Sciences. 2021; ():1-25.
Chicago/Turabian StyleI-Chun Tsai. 2021. "Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading." International Journal of Urban Sciences , no. : 1-25.
This study uses theoretical models and empirical research to explain that interest rates affect the structure of housing price formation and correction rather than affect the price alone. In particular, when interest rates are substantially reduced, the correction of housing prices toward fundamentals is absent; in other words, a housing bubble is likely to occur. This study first illustrates a model for explaining home price behavior. Data from the Case-Shiller U.S. National Home Price Index between January 1975 and August 2020 are adopted to observe the behavior of home prices. The empirical results show that the correction and bubble behavior of U.S. home prices have exhibited significant structural changes. Variation in money supply fails to explain the structural changes, however, interest rate variation can significantly affect the structural changes. According to the results, when interest rates rise or fall slightly, the correction of home prices toward the equilibrium value is significant. However, when interest rates fall substantially, the bubble behavior of home prices is significant. For governments that adopt low interest rates to revitalize the economy, the results of this study provide special reference values. Governments should provide additional intervention in housing markets when an extremely low interest rate exists.
Che-Chun Lin; I-Chun Tsai. THE SPECIAL EFFECT OF INTEREST RATE CUTS ON HOUSING PRICES. Journal of Business Economics and Management 2021, 22, 776 -798.
AMA StyleChe-Chun Lin, I-Chun Tsai. THE SPECIAL EFFECT OF INTEREST RATE CUTS ON HOUSING PRICES. Journal of Business Economics and Management. 2021; 22 (3):776-798.
Chicago/Turabian StyleChe-Chun Lin; I-Chun Tsai. 2021. "THE SPECIAL EFFECT OF INTEREST RATE CUTS ON HOUSING PRICES." Journal of Business Economics and Management 22, no. 3: 776-798.
In this paper, we infer that when no excess monetary liquidity exists, people tend to invest available capital in assets associated with a high return or low risk. However, when excess monetary liquidity occurs, capital may successively boost asset markets, and the stock market wealth is thus likely to spill into housing markets, resulting in bubbles in these two markets and therefore in the unsustainable development of both the housing and stock markets. This paper uses relevant data from the United Kingdom from January 1991 to March 2020 to verify whether excess monetary liquidity is a crucial factor determining the relationship between the housing and stock markets. Continuous and structural changes are found to exist between housing price and stock price returns. This paper employs the time-varying coefficient method for estimation and determines that the influence of stock price returns on housing returns is dynamic, and an asymmetrical effect can occur according to whether excess monetary liquidity exists. An excessively loose monetary policy increases asset prices and can thus easily result in a mutual rise in asset markets. By contrast, when excess monetary liquidity does not exist, capital transfer among markets can prevent autocorrelation during excessive market investment and thereby aggravate market imbalance.
Ming-Chu Chiang; I-Chun Tsai. Importance of Proper Monetary Liquidity: Sustainable Development of the Housing and Stock Markets. Sustainability 2020, 12, 8989 .
AMA StyleMing-Chu Chiang, I-Chun Tsai. Importance of Proper Monetary Liquidity: Sustainable Development of the Housing and Stock Markets. Sustainability. 2020; 12 (21):8989.
Chicago/Turabian StyleMing-Chu Chiang; I-Chun Tsai. 2020. "Importance of Proper Monetary Liquidity: Sustainable Development of the Housing and Stock Markets." Sustainability 12, no. 21: 8989.
This paper proposes a new explanation for housing rent price rigidity. When high inflation or low inflation occurs, the bargaining process for new rent price represents negotiations representing increasing or diminishing utility for landlords. Based on framing effect theory, this study hypothesized that utility increasing-bargaining causes landlords to choose to give greater concessions and prefer short-term contracts. Although the income obtained from single contracts is comparatively lower, the high transaction volume (number of lease contracts) causes a reduction in the number of vacant properties and a higher frequency of price adjustments. Conversely, when low inflation occurs, landlords face utility decreasing-bargaining, reduce their concessions, and exhibit a preference for long-term contracts, thereby leading to an increase in the number of vacant houses and a lower frequency of price adjustments. Using US rental market data, this study explains asymmetric rent volatility and changes in the vacancy rate, and provides related evidence supporting the hypothesis that this rental market phenomenon is caused by an inflation illusion.
I-Chun Tsai. Price Rigidity and Vacancy Rates: The Framing Effect on Rental Housing Markets. The Journal of Real Estate Finance and Economics 2020, 1 -18.
AMA StyleI-Chun Tsai. Price Rigidity and Vacancy Rates: The Framing Effect on Rental Housing Markets. The Journal of Real Estate Finance and Economics. 2020; ():1-18.
Chicago/Turabian StyleI-Chun Tsai. 2020. "Price Rigidity and Vacancy Rates: The Framing Effect on Rental Housing Markets." The Journal of Real Estate Finance and Economics , no. : 1-18.
In this study, a theoretical framework was constructed to verify whether the inflation level determines the presence of money illusion. The unexpected occurrence of low inflation is typically taken as an indication that the economy has entered or is entering a recession, and people expect the Federal Reserve to rapidly intervene to recover the economy. Because rental contracts facilitate rent price rigidity, rent prices may not decline immediately in the presence of unexpected low inflation, causing house owners in general to consider their rent returns as still lucrative. The excess profits from the rent returns of house owners can be used to compensate potential loss incurred from falling housing prices in the future, subsequently reducing their risk of property ownership. Therefore, when encountering unexpected low inflation, house owners tend to lower their expectations of housing return risk and overestimate the housing price, thus resulting in money illusion effects. A long-term data set of the US housing market (sample period: 1960 Q1 – 2016 Q1) was employed to comprehensively estimate the biases of the price–rent ratio. The estimation results revealed that money illusion effects only exist in conditions with minimal commodity price increases or decreases, which comply with the hypothesis of the present study. Finally, this study evaluated expected housing risk and housing premiums and confirmed that unexpected low inflation increases housing premiums, consequently leading to a higher rise in housing prices than in rent prices and contributing to the mispricing of price–rent ratios.
I-Chun Tsai. Alternative explanation of the money illusion: The effect of unexpected low inflation. International Review of Economics & Finance 2020, 69, 110 -123.
AMA StyleI-Chun Tsai. Alternative explanation of the money illusion: The effect of unexpected low inflation. International Review of Economics & Finance. 2020; 69 ():110-123.
Chicago/Turabian StyleI-Chun Tsai. 2020. "Alternative explanation of the money illusion: The effect of unexpected low inflation." International Review of Economics & Finance 69, no. : 110-123.
This study investigates the housing market in Taiwan, an emerging market with relatively severe housing price inflation. Using data from the first quarter of 1991 to the second quarter of 2017 for four cities in Taiwan, this study compares the risk transmission and sources of their housing prices. The results reveal that Taipei−Taiwan’s main financial hub−has the highest house prices among the four cities but maintains the lowest risk. Thus, in terms of price volatility risk, Taipei has the safest housing market among the studied cities. Other studies have discussed the potential housing price bubbles in regions with high housing prices but have been unable to explain the continual overheating of the housing markets. The findings of this study reveal that despite having the highest housing prices and the greatest potential bubble, the Taipei housing market has the lowest fluctuation risk, making it the safest market in terms of housing investment. The results of this study imply that Taiwan’s economic development is excessively concentrated in Taipei, causing people to bear low returns and high risk when purchasing real estate in other areas, in turn increasing the continual imbalance between regional housing markets.
Fang-Ni Chu; I-Chun Tsai. DO HIGHER HOUSE PRICES INDICATE HIGHER SAFETY? PRICE VOLATILITY RISK IN MAJOR CITIES IN TAIWAN. International Journal of Strategic Property Management 2020, 24, 165 -181.
AMA StyleFang-Ni Chu, I-Chun Tsai. DO HIGHER HOUSE PRICES INDICATE HIGHER SAFETY? PRICE VOLATILITY RISK IN MAJOR CITIES IN TAIWAN. International Journal of Strategic Property Management. 2020; 24 (3):165-181.
Chicago/Turabian StyleFang-Ni Chu; I-Chun Tsai. 2020. "DO HIGHER HOUSE PRICES INDICATE HIGHER SAFETY? PRICE VOLATILITY RISK IN MAJOR CITIES IN TAIWAN." International Journal of Strategic Property Management 24, no. 3: 165-181.
Chien-Wen Peng; I-Chun Tsai. The long- and short-run influences of housing prices on migration. Cities 2019, 93, 253 -262.
AMA StyleChien-Wen Peng, I-Chun Tsai. The long- and short-run influences of housing prices on migration. Cities. 2019; 93 ():253-262.
Chicago/Turabian StyleChien-Wen Peng; I-Chun Tsai. 2019. "The long- and short-run influences of housing prices on migration." Cities 93, no. : 253-262.
Wen-Yuan Lin; I-Chun Tsai. Trader differences in Shanghai’s A-share and B-share markets: Effects on interaction with the Shanghai housing market. Journal of Asian Economics 2019, 64, 101128 .
AMA StyleWen-Yuan Lin, I-Chun Tsai. Trader differences in Shanghai’s A-share and B-share markets: Effects on interaction with the Shanghai housing market. Journal of Asian Economics. 2019; 64 ():101128.
Chicago/Turabian StyleWen-Yuan Lin; I-Chun Tsai. 2019. "Trader differences in Shanghai’s A-share and B-share markets: Effects on interaction with the Shanghai housing market." Journal of Asian Economics 64, no. : 101128.
The goal of this paper is to observe the interregional correlations in the housing market at three price tiers (low, middle, and high) and examine differences in ripple effects at each housing price tier and the factors that influence these effects in each tier. Ripple effects in housing prices of 10 major metropolitan areas in the USA between January 1993 and March 2016 are examined, and this study finds that intercity ripple effects are larger in lower-cost houses. In addition, ripple effects continue to increase after February 2015 only for the low-tier sample. This indicates that intercity systemic risk is currently highest for low-tier houses. Further estimations reveal that money supply is the most crucial variable for determining the level of systemic risk. However, because high-price-tier properties are prone to disposition effects when house prices fall, ripple effects tend to drop sharply during housing market downturns, except for during the subprime mortgage crisis.
I-Chun Tsai. Interregional correlations in the US housing market at three price tiers. The Annals of Regional Science 2019, 63, 1 -24.
AMA StyleI-Chun Tsai. Interregional correlations in the US housing market at three price tiers. The Annals of Regional Science. 2019; 63 (1):1-24.
Chicago/Turabian StyleI-Chun Tsai. 2019. "Interregional correlations in the US housing market at three price tiers." The Annals of Regional Science 63, no. 1: 1-24.
In addition to evaluating the long‐term and short‐term relationships between housing markets on the regional and nationwide scales, this study also explores risk diffusion associated with market integration. Data from January 1995 to July 2017 in nine regions in England and in the overall England housing market are adopted. Through simultaneous estimates of volatility in various regions, the systemic risk and overall risk of each region are evaluated. The results reveal that housing markets in the central regions diffuse risks easily, and their volatility is easily affected by stock market return and money supply changes. By contrast, the volatility of housing markets in the southern regions is easily affected by changes in the interest rate, and these markets demonstrate high transmission with the overall housing market in the short term. Notably, except for times when financial crises occurred, housing market risks in the southern regions have been lower than those of other regions. In particular, London exhibits significantly low risk and high return in its housing market and high informativeness in its house prices. The results of this paper show the patterns and the factors influencing risk diffusion.
I‐Chun Tsai. Market Integration and Volatility Transmission in England’s Housing Markets. The Manchester School 2019, 88, 119 -155.
AMA StyleI‐Chun Tsai. Market Integration and Volatility Transmission in England’s Housing Markets. The Manchester School. 2019; 88 (1):119-155.
Chicago/Turabian StyleI‐Chun Tsai. 2019. "Market Integration and Volatility Transmission in England’s Housing Markets." The Manchester School 88, no. 1: 119-155.
This study analyzes the dynamic price–volume causality in the American housing market using the average price and transaction volume of existing houses in the United States from January 1999 to December 2015. A rolling window sample is used for estimation in the bootstrap Granger causality test. The results reveal that the transaction volume tends to be informative during price rigidity. In particular, the housing price tends to lag the volume when the information on housing price decreases is required for market correction. The housing price tends to be informative during volume rigidity, particularly during the substantial increase in housing price and the reduced transaction volume caused by the sellers’ reluctance to sell. The dynamic causality estimation explains that the price–volume relationship varies according to market conditions. Under normal circumstances, both the price and volume efficiently react to the information without a lead–lag relationship between the two. However, during a housing market boom or downturn, a lead–lag relationship between price and volume exists. This paper infers that the existence of a lead–lag relationship between price and volume can be a signal of housing market conditions.
I-Chun Tsai. Dynamic price–volume causality in the American housing market: A signal of market conditions. The North American Journal of Economics and Finance 2019, 48, 385 -400.
AMA StyleI-Chun Tsai. Dynamic price–volume causality in the American housing market: A signal of market conditions. The North American Journal of Economics and Finance. 2019; 48 ():385-400.
Chicago/Turabian StyleI-Chun Tsai. 2019. "Dynamic price–volume causality in the American housing market: A signal of market conditions." The North American Journal of Economics and Finance 48, no. : 385-400.
Over the last few decades, exuberance (bubble) and spillovers (ripple effects) have both been observed in the overheated housing market. However, surprisingly few attempts have so far been made to integrate these two concepts to further explore China's housing market frenzies. According to growth poles, the causality between exuberance and spillovers in real estate markets is that capital is initially concentrated in first-tier cities, but the housing-price exuberance then leads to spillovers to second-tier cities. Using housing price and rental data encompassing four first-tier and 6 s-tier cities on a month-by-month basis, we apply recursive unit root tests to examine the degree and timing of housing booms. At the same time, a rolling-window spillover index is used to evaluate ripple effects among these cities. Our estimates indicate that Beijing as a first-tier city first exhibits episodes of exuberance, which are then transmitted to second-tier cities.
I-Chun Tsai; Shu-Hen Chiang. Exuberance and spillovers in housing markets: Evidence from first- and second-tier cities in China. Regional Science and Urban Economics 2019, 77, 75 -86.
AMA StyleI-Chun Tsai, Shu-Hen Chiang. Exuberance and spillovers in housing markets: Evidence from first- and second-tier cities in China. Regional Science and Urban Economics. 2019; 77 ():75-86.
Chicago/Turabian StyleI-Chun Tsai; Shu-Hen Chiang. 2019. "Exuberance and spillovers in housing markets: Evidence from first- and second-tier cities in China." Regional Science and Urban Economics 77, no. : 75-86.
I-Chun Tsai. European house price deviation: infectivity and the momentum effect. Economic Research-Ekonomska Istraživanja 2019, 32, 1521 -1541.
AMA StyleI-Chun Tsai. European house price deviation: infectivity and the momentum effect. Economic Research-Ekonomska Istraživanja. 2019; 32 (1):1521-1541.
Chicago/Turabian StyleI-Chun Tsai. 2019. "European house price deviation: infectivity and the momentum effect." Economic Research-Ekonomska Istraživanja 32, no. 1: 1521-1541.
Since 2006, China has issued a series of policies to regulate foreign investments (FI) in the attempt to control housing prices. In the present study, the association between foreign direct investments (FDI) and China’s housing price bubble (HPB) was examined to elucidate the rationality of foreign investment controls. Structural changes in the influence of FDI on China’s HPB were observed. FDI and China’s HPB exhibited an increased association between 1985 and 2015, particularly following Q3 of 1997. This may have resulted from the relative stability of the Renminbi during the Asian Financial Crises, which attracted an increased inflow of FDI to China, and consequently aggravated China’s HPB. A threshold regression model was employed to uncover the reasons for the structural changes exhibited in the influence of FDI on China’s HPB. Findings indicated that FDI only influenced China’s HPB during the Renminbi appreciation, and that increased FDI was not correlated to HPB during the Renminbi depreciation or when the value of Renminbi remained unchanged against the U.S. dollar. The empirical results obtained in the present study suggest that the Chinese government should first observe changes in the exchange market prior to controlling the housing market.
I-Chun Tsai. Structural Changes in the Relationship between Foreign Direct Investments and China’s Housing Price Bubble. The Chinese Economy 2018, 51, 503 -521.
AMA StyleI-Chun Tsai. Structural Changes in the Relationship between Foreign Direct Investments and China’s Housing Price Bubble. The Chinese Economy. 2018; 51 (6):503-521.
Chicago/Turabian StyleI-Chun Tsai. 2018. "Structural Changes in the Relationship between Foreign Direct Investments and China’s Housing Price Bubble." The Chinese Economy 51, no. 6: 503-521.
China's stock market crash on August 24, 2015 affected global stock markets, indicating a possible black swan event. This study looked at trading days when sudden rises and drops occurred in 2015 in its stock markets to examine the intraday fluctuation behaviors of stock prices in order to answer several questions: (a) How did the market resume stability after volatility in stock prices occurred? (b) Did the corrections after days of sudden rises differ from those after days when sudden drops occurred? (c) Which of the investigated trading days could be classified as black swan events? Five trading days with the highest and lowest daily returns in 2015 in the Shanghai and Shenzhen stock markets were selected as the days of volatility. The quantile autoregression unit-root test was used to test whether stock price indices converged or dispersed on days of high volatility as time passed. The results reveal that a black swan event was identifiable only for the sudden drop on May 28. On other trading days, China's stock markets exhibited notable corrections of mean reversion, and the speed of market recovery increased with the extent of price volatility. In addition, wave patterns of slow rises and rapid falls in intraday stock price fluctuations and corrections were observable after occurrences of stock price volatility, showing that overreactions and underreactions happened consecutively.
Wen-Yuan Lin; I-Chun Tsai. Black swan events in China's stock markets: Intraday price behaviors on days of volatility. International Review of Economics & Finance 2018, 59, 395 -411.
AMA StyleWen-Yuan Lin, I-Chun Tsai. Black swan events in China's stock markets: Intraday price behaviors on days of volatility. International Review of Economics & Finance. 2018; 59 ():395-411.
Chicago/Turabian StyleWen-Yuan Lin; I-Chun Tsai. 2018. "Black swan events in China's stock markets: Intraday price behaviors on days of volatility." International Review of Economics & Finance 59, no. : 395-411.
High regional house prices relative to income may result in residents moving to other regions with lower housing burden; this generates relationships among regional housing markets. From this perspective, this study employed Markov-switching models to examine housing affordability in 10 regional housing markets in the UK. The results show that levels of housing burden among regions are related, thereby proving that a high cost of housing burden in one region may result in residents buying houses in other regions. Moreover, this study found that house prices in most regions tend to converge with income levels but are asymmetric within the period of convergence. Specifically, because the period of high housing loans lasts longer, and vice versa, housing demand increases as soon as house prices drop. Thus, periods of “inexpensive” house prices do not last long. This paper explains why living costs in different regions are related, and proposes that housing demands may be asymmetric when house prices are too high or too low.
I-Chun Tsai. Relationships among regional housing markets: Evidence on adjustments of housing burden. Economic Modelling 2018, 78, 309 -318.
AMA StyleI-Chun Tsai. Relationships among regional housing markets: Evidence on adjustments of housing burden. Economic Modelling. 2018; 78 ():309-318.
Chicago/Turabian StyleI-Chun Tsai. 2018. "Relationships among regional housing markets: Evidence on adjustments of housing burden." Economic Modelling 78, no. : 309-318.
According to search theory, transaction volume possesses the function of price discovery and reflects information more rapidly than price does. However, the findings of previous empirical studies differ considerably. In this study, a theoretical model is first established to analyze the potential information lag of transaction volume during pessimistic speculation. Data on the UK housing market are collected to conduct an empirical analysis of the responses of housing transaction volume to different market conditions. The results show that transaction volume responds to market information more quickly than does housing prices. However, under increasing market uncertainty, transaction volume lags four periods before reflecting the effect of the uncertainty. Moreover, this study performs a rolling window bootstrap Granger causality test, revealing that price leads volume during the period in which transaction volume fails to reflect an immediate rise in market uncertainty. An increase in market uncertainty reduces transaction volume. In addition, once transaction volume drops below a specific threshold, it loses its information content and price discovery function, extending the lead-lag gap with housing prices by two periods. The present study proposes a simple method for determining the informative-ness of housing transaction volume.
I-Chun Tsai. INFORMATION CONTENT OF TRANSACTION VOLUME: THE HOUSING MARKET IN THE UNITED KINGDOM. International Journal of Strategic Property Management 2018, 22, 348 -357.
AMA StyleI-Chun Tsai. INFORMATION CONTENT OF TRANSACTION VOLUME: THE HOUSING MARKET IN THE UNITED KINGDOM. International Journal of Strategic Property Management. 2018; 22 (5):348-357.
Chicago/Turabian StyleI-Chun Tsai. 2018. "INFORMATION CONTENT OF TRANSACTION VOLUME: THE HOUSING MARKET IN THE UNITED KINGDOM." International Journal of Strategic Property Management 22, no. 5: 348-357.
This paper constructs a generalized theoretical model to examine potentially high price variation and insufficient liquidity as the requirements for an enormous crash in a short period. In a market with insufficient liquidity, rapid changes in information induce selling pressure, therefore, this paper defines the prerequisite of a flash crash (large-scale decline in a short period of time) as when a market has insufficient liquidity and high price variability. Policy uncertainty provides an environment conducive to the occurrence of flash crashes. The paper examines the effect of economic policy uncertainty on the daily returns of the U.K. stock and foreign exchange markets. The results reveal that policy uncertainty is highly correlated with the risk of short-term, large-scale decline, which validates the theoretical inference of this paper. Although the timing at which extreme risks emerge is difficult to predict, this paper includes policy uncertainty as a factor to explain market conditions that can lead to flash crashes and remind traders to consider this factor when predicting the risk and value at risk of a market.
I-Chun Tsai. Flash crash and policy uncertainty. Journal of International Financial Markets, Institutions and Money 2018, 57, 248 -260.
AMA StyleI-Chun Tsai. Flash crash and policy uncertainty. Journal of International Financial Markets, Institutions and Money. 2018; 57 ():248-260.
Chicago/Turabian StyleI-Chun Tsai. 2018. "Flash crash and policy uncertainty." Journal of International Financial Markets, Institutions and Money 57, no. : 248-260.
This study examined the housing prices in major metropolitan areas in Taiwan and identified the reasons for the significant differences among them. A quantitative analysis of the spread effect in the housing prices revealed that the improvement in transportation infrastructure in the most recent decade intensified the spread effect. The findings obtained in this study also showed that relocation behavior and population density are the principal influencing factors on the spread effect in housing prices, validating that relocation behavior is incited by differences in housing prices and the convergence of housing prices in different cities. However, high housing price returns have also caused residents of northern Taiwan to relocate to central Taiwan. The empirical findings in this study indicate that in the past, Taiwanese housing prices were dispersed and differed significantly. These differences were caused by regional economic imbalance and inadequate transportation infrastructure. This situation has gradually improved.
I-Chun Tsai. Housing price convergence, transportation infrastructure and dynamic regional population relocation. Habitat International 2018, 79, 61 -73.
AMA StyleI-Chun Tsai. Housing price convergence, transportation infrastructure and dynamic regional population relocation. Habitat International. 2018; 79 ():61-73.
Chicago/Turabian StyleI-Chun Tsai. 2018. "Housing price convergence, transportation infrastructure and dynamic regional population relocation." Habitat International 79, no. : 61-73.
This study explored risk transfer among the housing markets of five major cities in China, comprising three first-tier cities (i.e., Beijing, Shanghai, and Shenzhen) and two second-tier cities (i.e., Tianjin and Chongqing). House price index data from January 2001 to June 2017 and a vector autoregressive–multivariate generalized autoregressive conditional heteroscedasticity model were employed to estimate correlations among these cities related to house price returns and volatility. In addition, volatility impulse-response functions were estimated to determine interactions among housing market risk in different cities. The results revealed that first-tier cities were more likely to transfer risk to second-tier cities, and that Beijing’s housing market exerted the greatest influence on risk in other cities’ housing markets. To consider the influence of the 2008 global financial crisis, data collected before and after the crisis were divided into two groups for subsequent investigation. The results revealed that these cities became more closely interrelated after the financial crisis, thereby escalating the risk of impulse influences. Finally, this study evaluated the influences of macroeconomic impulses on the housing markets of the three first-tier cities, indicating that real estate in these three cities can protect investors against inflation. The evidence presented in this paper can serve as a reference for the Chinese government regarding risk control.
I-Chun Tsai; Shu-Hen Chiang. Risk Transfer among Housing Markets in Major Cities in China. Sustainability 2018, 10, 2386 .
AMA StyleI-Chun Tsai, Shu-Hen Chiang. Risk Transfer among Housing Markets in Major Cities in China. Sustainability. 2018; 10 (7):2386.
Chicago/Turabian StyleI-Chun Tsai; Shu-Hen Chiang. 2018. "Risk Transfer among Housing Markets in Major Cities in China." Sustainability 10, no. 7: 2386.