Housing cycles and macroeconomic fluctuations: A global perspective

Cesa-Bianchi, Ambrogio

doi:10.1016/j.jimonfin.2013.06.004

Cited by 100 publications

(39 citation statements)

References 47 publications

(43 reference statements)

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“…The credit channel is an indirect channel for transmitting positive shocks from housing prices to economic activity (Cesa-Bianchi, 2013). Indeed, a rise in prices affects demand and supply of credit on the one hand and also encourages households to borrow more while it pushes financial institutions to grant more loans on the other hand.…”

Section: Broad Credit Channel (Financial Accelerator)mentioning

confidence: 99%

House Price Shock and Business Cycle: The French Case

Saad¹,

Alqatan²,

Arslan³

2021

SAEB

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This study examined the relationship between house prices and the business cycle. Specifically, we examined the effect of house price and stock price in the French business cycle. After presenting the transmission channels from house price to the business cycle, we analyzed the cyclical properties of house prices and compared them with the Gross Domestic Product (GDP) cycle. The question arises: are fluctuations in economic activity more sensitive to a real estate price shock (property wealth effect) or a stock price shock (stock market wealth effect)? We collected the data over the 1980Q1-2015Q4 period and investigated the effects of house price and stock price shocks on French GDP by employing the Structural VAR model. We found the evidence that house price strongly affects the GDP cycle. Indeed, their response is significantly more important than the stock price, suggesting that the housing market might contribute to the persistent propagation of the shocks hitting the economic system. The study has important implications for both academia and policymakers and offers new insights into the French experience.

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Section: Broad Credit Channel (Financial Accelerator)mentioning

confidence: 99%

House Price Shock and Business Cycle: The French Case

Saad¹,

Alqatan²,

Arslan³

2021

SAEB

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“…Although this approach is not sensitive to the ordering of the variables, it admits correlated errors, hence the economic interpretation of the resulting shocks might be difficult (see Pesaran et al, 2004). More recently, a number of studies have extended structural identification schemes to GVARs, to identify a single shock or a subset of shocks through either a Cholesky factorization (see Dees et al, 2007a;Cesa-Bianchi, 2013, among the others) or through sign restrictions (Chudik & Fidora, 2011). In this paper, we follow the suggestions of Eickmeier & Ng (2015) relying on sign restrictions on the impulse responses obtained from a GVAR model.…”

Section: Structural Identificationmentioning

confidence: 99%

“…The issue of across-countries residuals correlation is addressed by conditioning the domestic endogenous variables, y it , on the "foreign" variables, y * it . In order to check the cross-country correlation, we compute the average pairwise cross-country correlations among the endogenous variables and the individual V ARX * (1, 1) residuals (see Cesa-Bianchi, 2013;Eickmeier & Ng, 2015). Similar to the empirical findings of Cesa-Bianchi (2013) and of Eickmeier & Ng (2015), we obtain that the largest pairwise cross-country correlation between residuals (in absolute value) is 0.24, while the corresponding mean is 0.04 (see Table 4).…”

Section: Bootstrapping the Gvar Modelmentioning

confidence: 99%

“…, N , in order to get spatial regressors by averaging out the foreign variables, y * it = N j=1 w ij y jt . Generally, the weighting strategy is modelled by using shares of cross-country trade flows (Pesaran et al, 2004;Dees et al, 2007a;Cesa-Bianchi, 2013, among the others), cross-country bank lending exposures (Galesi & Sgherri, 2009, among the others), a combination of weights based on both trade and financial flows (Eickmeier & Ng, 2015) or spatial-based weights (Vansteenkiste, 2007;Vansteenkiste & Hiebert, 2011). Since our aim is to highlight a spatial dimension of the propagation mechanism of housing market shocks in the Italian provinces, we use time-fixed geographic weights.…”

Section: Weighting Strategymentioning

confidence: 99%

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Housing market shocks in italy: A GVAR approach

Cipollini

Parla

2020

Journal of Housing Economics

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In this paper, we use a Global Vector Autoregression (GVAR) model to assess the spatio-temporal mechanism of house price spillovers, also known as "ripple effect", among 93 Italian provincial housing markets, over the period 2004 − 2016. In order to better capture the local housing market dynamics, we use data not only on house prices but also on transaction volumes. In particular, we focus on estimating, to what extent, exogenous shocks, interpreted as negative housing demand shocks, arising from 10 Italian regional capitals, impact on their house prices and sales and how these shocks spill over to neighbours housing markets. The negative housing market demand shock hitting the GVAR model is identified by using theory-driven sign restrictions. The spatio-temporal analysis carried through impulse response functions shows that there is evidence of a "ripple effect" mainly occurring through transaction volumes.

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“…The shred of literature has proven the versatility of housing market and the perceived relationship with handful of macroeconomics (Aye, Balcilar, Bosch, & Gupta, ; Canarella, Miller, & Pollard, ; Cesa‐Bianchi, ; Cesa‐Bianchi, Cespedes, & Rebucci, ; Kishor & Marfatia, ; Nyakabawo, Miller, Balcilar, Das, & Gupta, ; Sirmans, Macpherson, & Zietz, ; Zhang, Li, Hui, & Li, ). Also, the housing market and the financial factors' relationship has been observed over time (Aoki, Proudman, & Vlieghe, ; Case, Quigley, & Shiller, ; Cesa‐Bianchi et al, ).…”

Section: Overview Of Previous Studiesmentioning

confidence: 99%

The housing market and agricultural land dynamics: Appraising with Economic Policy Uncertainty Index

Alola

Uzuner

2019

Int. J Fin Econ

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Agricultural land is a main component of the environment and ecological system. Therefore, this study employs the panel cointegration approach to investigate the dynamic relationship between the housing market vis‐à‐vis housing price (hp) and agricultural land (land) of a panel of 15 countries (countries with the gross domestic product–weighted average of the national Economic Policy Uncertainty index) over the period 1997–2015. Additionally, Granger causality approach of Dumitrescu–Hurlin is employed for the investigation. The model adjusts to the long‐run equilibrium with an annual speed of 11% from any situation of disequilibrium and with elasticities of −3.97 and −0.67, respectively, for a land and Global Economic Policy Uncertainty (gepu). Meanwhile, the impact during a short run of gepu is statistically significant and positive on hp, whereas it is not significant for land. Interestingly, the investigation reveals Granger causality from agricultural land to the housing price with feedback. The research presents an indication that policymaker(s) and urbanization stakeholders should be more concern about effective and sustainable long‐term policies. Future anomalies of food scarcity and skyrocketing house prices associated with agricultural land–house price trade‐off challenges could potentially be mitigated by such effective policy frameworks.

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Housing cycles and macroeconomic fluctuations: A global perspective

Cited by 100 publications

References 47 publications

House Price Shock and Business Cycle: The French Case

House Price Shock and Business Cycle: The French Case

Housing market shocks in italy: A GVAR approach

The housing market and agricultural land dynamics: Appraising with Economic Policy Uncertainty Index

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