Tourism and House Prices: A Wavelet Analysis

Wu, Tsung-Pao; Wu, Hejun; Chu, Chien-Chi; Deng, Ruixin

doi:10.1080/19388160.2019.1683111

Cited by 10 publications

(5 citation statements)

References 48 publications

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“…In this work, the CWT was chosen as a useful tool to decompose the concerned series into wavelets. Interested readers can refer to several studies (e.g., Aguiar-Conraria and Soares 2014; Wu and Wu 2019a, 2019b; Wu et al 2019) in detail.…”

Section: Data Collection and Wavelet Theorymentioning

confidence: 99%

Global Economic Policy Uncertainty and Tourism of Fragile Five Countries: Evidence from Time and Frequency Approaches

2020

Journal of Travel Research

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The purpose of this study is to examine the relationship between global economic policy uncertainty (GEPU) and tourism activities in the Fragile Five (F5) countries, namely, Brazil, India, Indonesia, South Africa, and Turkey. By using wavelet transform context structures and the annual data during the period of 1997–2016. The finding shows that the relationship is generally positive but changes over time, displaying low- to high-frequency cycles. Moreover, the timing and frequency change when GEPU co-moves with tourism. It can be recommended that the government maintain the national security and peace protocols.

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Section: Data Collection and Wavelet Theorymentioning

confidence: 99%

Global Economic Policy Uncertainty and Tourism of Fragile Five Countries: Evidence from Time and Frequency Approaches

2020

Journal of Travel Research

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“…Research studies in finance and economics are witnessing the growth on the usage of the wavelet transformation analysis using the domain of frequency. It considered an improvisation of the traditional Fourier transform as the Fourier analysis is only able to provide a shorter frame in extracting co-movement at the frequency level (Rua, 2010; Wu et al , 2019; Sleziak et al , 2015). By implementing the wavelet analysis, the studies are able to fetch high-quality information confined into a signal in various scales and time.…”

Section: The Research Methodology and Datamentioning

confidence: 99%

“…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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“…The result showed that tourism leads house prices in some regions, whereas the house-leading tourism hypothesis is accepted in some regions. Wu et al (2019) applied the wavelet technique to explore the association between tourism and house prices in first-tier cities of China. The result showed that the association between the two variables is positive but varies over time.…”

Section: Literature Reviewmentioning

confidence: 99%

Panel smooth transition regression approach to analyzing tourism development – housing price nexus in G7 countries

Khaskheli

Raza

et al. 2022

IJHMA

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Purpose The purpose of this study is twofold: first is to examine the nonlinear relationship between tourism development and housing prices, and secondly, the role of economic growth in facilitating the materialization of tourism development and housing prices nexus in G7 countries. Design/methodology/approach The authors used the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data from 1995 to 2018. Findings Results confirmed that the nexus between the tourism development and housing prices is nonlinear and regime dependent. Moreover, the results showed that the threshold level of economic growth above which tourism development increases the housing prices is 2.63%. The relationship above the threshold value is positive and growth enhancing, while below the threshold, tourism development has a negative effect on housing prices. The economic growth and housing prices also showed the U-shape relationship implying that at a certain level increase in economic growth decreases the housing prices but after a certain level increase in economic growth increases the housing prices. Originality/value This paper makes a unique contribution to the literature with reference to developed economies, being a pioneering attempt to investigate the nonlinear relationship between tourism development and housing prices and applying more rigorous and advanced econometric techniques like PSTR.

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Tourism and House Prices: A Wavelet Analysis

Cited by 10 publications

References 48 publications

Global Economic Policy Uncertainty and Tourism of Fragile Five Countries: Evidence from Time and Frequency Approaches

Global Economic Policy Uncertainty and Tourism of Fragile Five Countries: Evidence from Time and Frequency Approaches

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

Panel smooth transition regression approach to analyzing tourism development – housing price nexus in G7 countries

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