Housing prices and investor sentiment dynamics: Evidence from China using a wavelet approach

Hong, Yun; Li, Yang

doi:10.1016/j.frl.2019.09.015

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…A multi-period DID strategy was implemented to capture the impact of the COVID-19 pandemic on the housing price difference between gated and open communities. The aim is to provide complementary evidence for changes in people's perception of GCs, which could reflect on the dynamics of the housing market [21,22].…”

Section: Methods For Data Analysismentioning

confidence: 99%

“…Changes in residents' perception of GCs' security zone function and the preference for GCs due to the COVID-19 outbreak are identified based on self-reported data from the online survey and an analysis of housing transaction data using a multi-period difference-in-differences (DID) model. It is established in the literature that people's perception can have a real impact on housing price [21,22]. In this study, the changing housing price dynamic related to key attributes of GCs is measured by the transaction price, the number of interested buyers and the discount from the listing price.…”

Section: Introductionmentioning

confidence: 99%

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The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China

Ling

Wan

2021

Land

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The ongoing COVID-19 pandemic has left a strong imprint on many aspects of urban life. Gated communities (GCs) in China are less commonly perceived as a negative and segregated urban form of community compared to other contexts, owing to their wide variety and relative openness. Yet, the enhanced security zone function and the popularity of GCs, along with the heightened segregation and exclusion effects, mean they are most likely to emerge in post-pandemic urban China because of the perceived effectiveness of GCs in preventing health risks by excluding outsiders during the pandemic. Drawing on empirical data from Beijing, this research presents strong evidence for a strengthened perceived ‘security zone’ effect of GCs during the pandemic. Given that rigid pandemic control measures were organized at the community level, a large-scale household survey in Beijing suggests that residents commonly recognise the effectiveness of GCs in security control and show a strong preference for GCs over open communities after the pandemic, even though there is a lack of direct evidence of reduced COVID-19 risk in GCs. The heightened perceived ‘security zone’ function of GCs has shown a significant impact on the housing market, evidenced by an increase of 2% in the housing prices for GCs, compared with those of open communities. The rising popularity of GCs is also evidenced by a significant increase in property viewings by potential homebuyers and smaller price discounts in actual transactions in gated communities vis-à-vis open communities. We argue that the rising risk-averse sentiment in the post-pandemic era has given rise to the popularity of GCs. This study provides timely and fresh insights into the changing meaning of GCs in post-pandemic China.

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Section: Methods For Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China

Ling

Wan

2021

Land

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“…These shrinkage-based approaches allow us to efficiently conduct the forecasting experiment, without suffering from the "curse of dimensionality", especially in the context of a time-varying framework with multiple predictors and relative short-span (13 years) of data (in our case 156 monthly observations). Our paper can be considered to be an extension of Hong and Li (2019b), whereby they use wavelet analysis to provide in-sample evidence of the predictability of housing returns in China due to investor sentiment. However, since in-sample predictability does not guarantee forecasting gains, and as pointed out by Campbell (2008), and Bork and Møller (2015) specifically for housing returns, that the ultimate test of any predictive model (in terms of the econometric methodologies and the predictors used) is in its out-of-sample performance, evidence of forecastability, if it exists, would provide more robust evidence (relative to an in-sample analysis) of the role of investor sentiment for future housing returns.…”

Section: Introductionmentioning

confidence: 99%

“…One must recall that investor sentiment is a latent variable, and needs to be derived from appropriate proxies. Given this Hong and Li (2019b), followed Baker and Wurgler (2006) to form the investor sentiment index using the principal component analysis (PCA) to aggregate the information from six individual proxies (the closed-end fund discount; average first-day returns on initial public offerings (IPOs); the ratio of the number of advancing stocks to the number of declining stocks; new A-share market accounts; market turnover rate; and consumer confidence index), which we use as well, both as an index and individually. But as an alternative to PCA, we also use partial least squares (PLS) to construct the sentiment index.…”

Section: Introductionmentioning

confidence: 99%

The role of investor sentiment in forecasting housing returns in China: A machine learning approach

Çepni

Gupta

Onay

2022

Journal of Forecasting

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This paper analyzes the predictive ability of aggregate and dis-aggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. Using a monthly out-of-sample period of 2011:01 to 2018:12, given an in-sample of 2006:01-2010:12, we find that indeed the new aligned investor sentiment index proposed in this paper has greater predictive power for housing returns than the a principal component analysis (PCA)-based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the dis-aggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the dis-aggregate proxies of investor sentiment. Furthermore, when we let the machine learning models to choose from all key control variables and the aligned sentiment index, the forecasting accuracy is improved at all forecasting horizons, rather than just the short-run as witnessed under standard predictive regressions. This result suggests that machine learning methods are flexible enough to capture both structural change and time-varying information in a set of predictors simultaneously to forecast housing returns of China in a precise manner. Given the role of the real estate market in China's economic growth, our result of accurate forecasting of housing returns, based on investor sentiment and macroeconomic variables using state-of-the-art machine learning methods, has important implications for both investors and policymakers.

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“…When comes the research literature reviews concerning co-movement and dynamics relating to housing market, the wavelet approach is becoming a prevalent methodology in explaining as cogently as possible about the interdependence factors of housing market (e.g. Hong and Li, 2020; Wu et al , 2019; Wang et al , 2019; Liow et al , 2019a; Su et al , 2018; Li et al , 2015; Chou and Chen, 2011; Seo and Kim, 2020; Hu et al , 2020). Housing prices are oblique as nonstationary time series due to their intricate set of patterns over time and countercyclical cycles (Mu et al , 2009; Wheaton, 1999) as to that wavelet test is well suited in subduing this constrain due its ability to capture concurrently the frequency and time variation of a series.…”

Section: Introductionmentioning

confidence: 99%

Residential property market in Malaysia: an analysis of price drivers and co-movements

Thaker

Ariff

Subramaniam

2020

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PurposeThe purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.Design/methodology/approachThis study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.FindingsThe empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.Practical implicationsThis study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.Originality/valueThe findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

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Housing prices and investor sentiment dynamics: Evidence from China using a wavelet approach

Cited by 22 publications

References 26 publications

The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China

The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China

The role of investor sentiment in forecasting housing returns in China: A machine learning approach

Residential property market in Malaysia: an analysis of price drivers and co-movements

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