Housing, consumption and monetary policy: How different are the US and the euro area?

Musso, Alberto; Neri, Stefano; Stracca, Livio

doi:10.1016/j.jbankfin.2011.04.004

Cited by 308 publications

(94 citation statements)

References 38 publications

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“…We order the variables as follows:Tbill and ghp following Musso et al (2011) and André et al (2011). Note that the latent variable s t which indicates bull or bear markets conventionally corresponds to "the high mean growth rate of house prices and stable s t a t e " a n d " l o w m e a n g r o w t h r a t e o f h o u s e p r i c e s a n d v o l a t i l e s t a t e " r e s p e c t i v e l y .…”

Section: Methodology: Markov-switching Vector Autoregressive (Ms-var)mentioning

confidence: 99%

Is the relationship between monetary policy and house prices asymmetric across bull and bear markets in South Africa? Evidence from a Markov-switching vector autoregressive model

et al. 2013

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Abstract. This paper examines asymmetries in the impact of monetary policy on the middle segment of the South African housing market from 1966:M2 to 2011:M12. We use Markov-switching vector autoregressive (MS-VAR) model in which parameters change according to the phase of the housing cycle. The results suggest that monetary policy is not neutral as house price growth decreases substantially with a contractionary monetary policy. We find that the impact of monetary policy is larger in bear regime than in bull regime; indicating the role of information asymmetry in reinforcing the financial constraint of economic agents. As expected, monetary policy reaction to a positive house price shock is found to be stronger in the bull regime. This suggests that central banks react more in bull regime in order to prevent potential crisis related to the subsequent bust in house prices bubbles which are more prominent in bull markets. These results substantiate important asymmetries in the dynamics of house prices in relation to monetary policy, vindicating the advantages of generating regime dependent impulse response functions.

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Section: Methodology: Markov-switching Vector Autoregressive (Ms-var)mentioning

confidence: 99%

Is the relationship between monetary policy and house prices asymmetric across bull and bear markets in South Africa? Evidence from a Markov-switching vector autoregressive model

et al. 2013

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“…In identifying a housing demand shock, we basically follow Jarocinski and Smets (2008) and Musso et al (2011). However, we try to decompose the housing demand shock in two types of separate shocks: a housing preference shock and a population shock.…”

Section: Housing Demand Shocksmentioning

confidence: 99%

“…2 In the empirical literature, interesting contributions on the role of credit supply shocks come from Busch et al (2010), Eickmeier and Ng (2011), Helbling et al (2011), Meeks (2012, Fornari and Stracca (2012) and Peersman and Wagner (2014). An key issue is the role and the relative importance of credit supply shocks and standard monetary policy shocks as identified by Musso et al (2011) for the US and the EU. Studies that take into account specific issues of housing are Jarocinski and Smets (2008), Goodhart and Hofmann (2008), Musso et al (2011), as well as Hirata et al (2012).…”

Section: Introductionmentioning

confidence: 99%

“…An key issue is the role and the relative importance of credit supply shocks and standard monetary policy shocks as identified by Musso et al (2011) for the US and the EU. Studies that take into account specific issues of housing are Jarocinski and Smets (2008), Goodhart and Hofmann (2008), Musso et al (2011), as well as Hirata et al (2012).…”

Section: Introductionmentioning

confidence: 99%

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Credit cycles and real activity: the Swiss case

Bäurle

Scheufele

2018

Empir Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper analyzes credit supply and demand shocks for the Swiss economy. Using a medium scale BVAR model we are able to take into account various interactions of housing prices, credit supply and demand, interest rates and real activity measures. To identify meaningful economic shocks, we used a combination of zero and sign restrictions. Generally, the empirical analysis implies that the effects of credit supply and demand shocks on real activity are limited, only playing a substantive role in specific episodes. Furthermore, the credit supply shocks (i.e. bank lending shocks and monetary policy shocks) explain a large fraction of housing price and credit fluctuations. Indeed, they tend to be more important for housing prices than housing demand shocks. There is further evidence that shocks related to credit supply, monetary policy shocks dominate bank lending shocks. For Switzerland as a small open economy it turns out to be very crucial to take into account foreign demand which is able to explain a substantial variation in real and financial variables. Terms of use: Documents in EconStor mayJEL classification: C11, C32, E30, E44, E51, E52

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“…These factors unbalance the pricing data and complicate econometric construction of a price index due to problems of heterogeneous, 1 The recent literature has witnessed an upsurge of interest in studying real estate markets from perspectives of banking, financial and macroprudential policy. See, for example, the study of the relationship between real estate prices and banking instability (Koetter and Poghosyan, 2010;Reinhart and Rogoff, 2013), the market linkage among different assets (Chan et al, 2011), the impact of macroprudential policy on housing prices (Shi et al, 2013;Mendicino and Punzi, 2014), the role of housing markets for macroeconomy (Iacoviello, 2005;Musso et al, 2011). missing, and unequally spaced observations.…”

Section: Introductionmentioning

confidence: 99%

New methodology for constructing real estate price indices applied to the Singapore residential market

Jiang

Phillips

2015

Journal of Banking & Finance

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This paper develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model design that uses a sale pairing process based on the individual building level, rather than the individual house level as is used in the repeat-sales method. The approach extends ideas from repeat-sales methodology in a way that accommodates much wider datasets. In an empirical analysis of the methodology, we fit the model to the private residential property market in Singapore between Q1 1995 and

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Housing, consumption and monetary policy: How different are the US and the euro area?

Abstract: 4Non-technical summary 5

Cited by 308 publications

References 38 publications

Is the relationship between monetary policy and house prices asymmetric across bull and bear markets in South Africa? Evidence from a Markov-switching vector autoregressive model

Is the relationship between monetary policy and house prices asymmetric across bull and bear markets in South Africa? Evidence from a Markov-switching vector autoregressive model

Credit cycles and real activity: the Swiss case

New methodology for constructing real estate price indices applied to the Singapore residential market

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