A wavelet analysis of the ripple effect in UK regional housing markets

Cascio, Iolanda Lo

doi:10.1016/j.iref.2021.08.001

Cited by 11 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For D4, D3, D2 and D1, it can be seen that linear causality is much weaker as compared with relatively longer timescales, including S6, D6 and D5. Similar empirical evidence is also found in the international housing market (Cascio, 2021; Flor and Klarl, 2017). Linear causality test results for these relatively shorter timescales do, however, reveal interesting and possibly many sensible responses (effects) of the house price of one city to (on) that of another.…”

Section: Resultssupporting

confidence: 79%

House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains

Zhang

2022

IJHMA

View full text Add to dashboard Cite

Purpose With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019. Design/methodology/approach The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework. Findings The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect. Originality/value Results here should be of use to policymakers in certain policy analysis.

show abstract

Section: Resultssupporting

confidence: 79%

House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains

Zhang

2022

IJHMA

View full text Add to dashboard Cite

show abstract

“…Zhang et al (2021) used a network approach based on partial correlations to study regional housing prices in the UK. Lo Cascio (2021) applied wavelet analysis to study UK regional housing markets and identified London as the dominant market (i.e. London’s market leads price changes elsewhere).…”

Section: Literature Reviewmentioning

confidence: 99%

“…heterogenous vs homogenous policy implementation). This issue has gained much attention in real estate research because of its importance across countries (Gupta and Miller, 2012; Yunus and Swanson, 2013; Stevenson, 2004; Barros et al , 2014; Apergis and Payne, 2012; Miles, 2020; Zhang et al , 2021; Lo Cascio, 2021). Research has shown that post-GFC cointegration among regional housing prices is increasing, leading to diminishing diversification benefits (Yunus and Swanson, 2013; Kallberg et al , 2014; Miles, 2015; Yunus, 2018).…”

Section: Introductionmentioning

confidence: 99%

Long-run drivers and integration in interprovincial Canadian housing price relations

Vishwakarma

2021

IJHMA

View full text Add to dashboard Cite

Purpose This paper aims to examine the integration of housing markets in Canada by examining housing price data (1999–2016) of six metropolitan areas in different provinces, namely, Calgary, Vancouver, Winnipeg, Toronto, Montreal and Halifax. The authors test for cointegration, driver cities of long-run relationships, long-run Granger causality and instantaneous causality in light of the global financial crisis (GFC) (2007–2008). Design/methodology/approach The authors use Johansen’s system cointegration approach with structural breaks. Moving average representation is used for common stochastic trend(s) analysis. Finally, the authors apply vector error correction model-based Granger causality and instantaneous causality. Findings Cities’ housing prices are in long-run equilibrium. Post-crisis Canadian housing markets became more integrated. The Calgary, Vancouver, Toronto and Montreal markets drive the Canadian housing market, leading all cities toward long-run equilibrium. Strong long-run Granger causality exists, but the authors observe no instantaneous causality. Price information takes time to disseminate, and long-run price adjustments play a significant role in causation. Practical implications The findings of cointegration increasing after the GFC and strong lead–lag can be used by investors to arbitrage and optimize portfolios. This can also help national and local policymakers in mitigating risk. Incorporating these findings can lead to better price forecasting. Originality/value This study presents many novelties for the Canadian housing market: it is the first to use repeat-sales regional pricing indices to test long-run behaviors, conduct common stochastic trend analyzes and present causality relations.

show abstract

Are metaverse coins more prone to geopolitical risk than traditional crypto assets?

Jana

2024

International Review of Economics & Finance

View full text Add to dashboard Cite

A wavelet analysis of the ripple effect in UK regional housing markets

Cited by 11 publications

References 42 publications

House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains

House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains

Long-run drivers and integration in interprovincial Canadian housing price relations

Are metaverse coins more prone to geopolitical risk than traditional crypto assets?

Contact Info

Product

Resources

About