How do housing prices affect consumption in China? New evidence from a continuous wavelet analysis

Abstract

In this paper, we revisit the relationship between housing prices and consumption in China by using a continuous wavelet analysis. This method provides an insight into the dynamic nexus in both time and frequency domains. In general, Empirical results show that there is a positive relationship between the two series in China, although it varies across time and frequencies. First, we find that disposable income is the core factor which affects both consumption and housing prices in China. Second, housing prices have a weak wealth effect on consumption in most time. High housing ownership and proportion of housing asset in the total household asset lead to a wealth effect of housing prices on consumption. Third, in the long term, there is a significant wealth effect of housing prices on consumption, but it weakens after 2008, which implies that excessively high housing prices in recent years have partially constrained residents’ consumption. These findings have important implications for seeking housing market regulation and expanding domestic consumption decisions.

Cointegrations in house price dynamics and ageing population risks

“…[ 15 ] study ageing and house prices in Korea again in a panel regression framework but results between rural and urban areas are mixed. [ 16 ] find that house prices in China have a weak wealth effect on consumption but results vary across horizons. None of the papers cited above focus on long run ageing risk, measured by cointegration relation between rural area ageing and house prices.…”

The health consequence of rising housing prices in China

“…Recent studies have found that such rapid housing appreciation has affected individuals and households enormously. The rise in housing prices has been found to tighten household consumption (Su, Yin and Tao, 2018;Waxman et al, 2019), reduce female labor force participation (Fu, Liao and Zhang, 2016), increase marriage age (Wrenn, Yi and Zhang, 2019), and raise the probability of inter-generational co-residence (Li and Wu, 2019), among other things. Yet little is known about the health consequences of the rapid growth in housing prices.…”

The Identification and Prediction in Abundance Variation of Atlantic Cod via Long Short-Term Memory With Periodicity, Time–Frequency Co-movement, and Lead-Lag Effect Across Sea Surface Temperature, Sea Surface Salinity, Catches, and Prey Biomass From 1919 to 2016

“…To address the potential periodicity, time-frequency comovement, and lead-lag effects of non-stationary long-term time series, wavelet analysis is a mathematically basic tool to discover how to approximate time series to a certain frequency band within a certain time period simultaneously, which could optimally describe the occurrence of transient events, and well adapt to conditions where the amplitude of the response varies significantly in non-stationary time series (Steel and Lange, 2007;Cloern and Jassby, 2010;Rhif et al, 2019). As the extended usage of wavelet transform, wavelet coherence (Maraun and Kurths, 2004;Su et al, 2018Su et al, , 2019 and phase difference (Aguiar-Conraria and Soares, 2011) can be utilized to recognize whether two time series are quantitatively linked by a certain correlation, even causality relationship. Kogovšek et al (2010) have applied continuous wavelet transform to investigate the long-term fluctuation of time series of jellyfish abundance in the past 200 years and further confirmed a major and minor period of its proliferation.…”