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The Journal of Real Estate Finance and Economics
Published in: The Journal of Real Estate Finance and Economics 4/2023

28-09-2021

Sentimental Shocks and House Prices

Authors: Dimitris Anastasiou, Panayotis Kapopoulos, Kalliopi-Maria Zekente

Published in: The Journal of Real Estate Finance and Economics | Issue 4/2023

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Abstract

We investigate the impact of sentimental shocks on house price fluctuations in the Euro area. To this end, we isolate and measure non-fundamental-based sentimental shocks by employing survey-based indicators that proxy four key types of expectations of housing market participants. The novelty of our study is that specific sentimental shocks are identified through four uncertainty transmission channels in the real estate market (i.e., the precautionary savings channel, the credit supply channel, the credit demand, and the inflationary channel). We provide strong evidence that sentimental shocks drive fluctuations in house prices even in the absence of any changes in aggregate fundamentals. Finally, we find that these results are more pronounced in the peripheral Euro area countries. The finding that the real estate market is also governed by irrational behavior implies that both governments and policymakers should consider sentimental shocks when they form their real estate market policies or take actions to stabilize and improve the proper function of the European housing market.
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Appendix
Available only for authorised users
Footnotes
1
According to Clayton et al. (2009), the behavioral approach explicitly recognizes that some investors are not rational and that systematic biases in these investor’s beliefs induce them to trade on non-fundamental information (i.e., sentiment).
 
2
This is also in line with Gerlach and Peng (2005) who stated that credit conditions affect asset valuations, as decreases in credit availability may shrink the demand for a fixed supply of properties.
 
3
A deterioration of expectations could be for example the outcome of either a very high leverage or an adverse change in the risk-free rate as well as in the loan recovery rate which, as indicated by Geanakoplos (2010) and empirically confirmed by Wang and Zhang (2014) could trigger a financial crisis.
 
4
Banti and Phylaktis (2019) find that liquidity shocks have a significant impact on house prices in both emerging and advanced economies, signifying the high exposure of housing markets around the world to liquidity conditions.
 
5
In general, an asset class can be considered as safe-haven when its value increases while the reference portfolio of risky assets is losing value during risky times (Baur & Lucey, 2010; Eraslan, 2016). Traditionally, gold (Baur & Lucey, 2010; Baur & McDermott, 2016; Dicle & Levendis, 2017) and foreign exchange currencies (Grisse & Nitschka, 2015) are considered as safe-haven assets in the literature, especially during periods of financial turmoil.
 
6
The sample of countries and time span were selected based on data availability.
 
7
Following Ling et al. (2015) and Cox and Ludvigson (2019), we also posit that changes in bank credit standards for housing loans echo banks’ changing perspectives about housing market riskiness.
 
8
The diffusion index for credit standards is defined as the difference between the weighted sum of the percentages of banks responding, “tightened considerably” and “tightened somewhat”, and the weighted sum of the percentages of banks responding “eased considerably” and “eased somewhat”. Regarding demand for loans, the diffusion index is defined as the difference between the weighted sum of the percentages of banks responding, “increased considerably” and “increased somewhat”, and the weighted sum of the percentages of banks responding “decreased considerably” and “decreased somewhat”. (Bank lending survey for the Euro area—Glossary, p. 4). Thus, a relatively high diffusion index suggests that we have an economy with more tighten bank credit standards (i.e., less loan supply) and/or greater willingness for loan demand, respectively.
 
9
The reason why we model the relation between sentiment and house prices employing a lag structure model is twofold. First, because we want to mitigate any possible endogeneity issues, and second to recognize the nature of housing data. Since a sale transaction can take 30–90 days, or more, to be completed, actual price movements in a quarter may also be reflected in the next quarter’s statistics (Ling et al., 2015). Therefore, any price effects should be felt in the next quarter rather than the concurrent quarter.
 
10
The classic approach of Difference-GMM, firstly proposed by Arellano and Bond (1991) was not preferred because according to the literature it suffers from poor accuracy in simulation and from significant finite-sample bias.
 
11
According to Wintoki et al. (2012), in panel data, System GMM provides more consistent results in the presence of different sources of endogeneity.
 
12
According to Duca et al. (2010), a tightening of credit standards enables less population to buy homes, thus reducing the flow of demand for housing.
 
13
The imain imerit iof ithe iPCA iis ithat iit iaggregates ithe ialready iexisting iinformation of ithe ithree idifferent iindividual isurvey-based isentiment imeasures iinto ia isingle isentiment iindicator. iThis ielicited ifirst iprincipal icomponent igives ia isense iof ithe idimensionality iof ithe ithree iindividual isentiment iindices.
 
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Metadata
Title
Sentimental Shocks and House Prices
Authors
Dimitris Anastasiou
Panayotis Kapopoulos
Kalliopi-Maria Zekente
Publication date
28-09-2021
Publisher
Springer US
Published in
The Journal of Real Estate Finance and Economics / Issue 4/2023
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-021-09871-z

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