Time-Varying and Spatial Herding Behavior in the US Housing Market: Evidence from Direct Housing Prices

Ngene, Geoffrey; Sohn, Daniel P.; Hassan, M. Kabir

doi:10.1007/s11146-016-9552-5

Cited by 25 publications

(11 citation statements)

References 60 publications

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“…Complete details of these results are available upon request from the authors. It must be noted that, similar to our finding,Ngene et al (2017) found support for the hypothesis that herding effects at the state-level US housing returns are more prominent during the recession periods.…”

supporting

confidence: 91%

Estimating U.S. Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models

et al. 2020

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This paper investigates the dynamic connectedness of random shocks to housing prices between the 50 U.S. states and the District of Columbia. The paper implements a standard vector autoregressive (VAR) model as well as three VAR models with shrinkage effects-Elastic Net, Lasso, and Ridge VAR models. The transmission of random shocks on a regional basis flows from Southern states to Western states to Midwestern states to Northeastern states. Since VAR models generally confront parameter values between zero and one, the Elastic Net and Lasso VAR models perform the best since the penalty involves the absolute value rather than he squared value as in the Ridge VAR model. Our results have important implications for investors and policymakers.

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supporting

confidence: 91%

Estimating U.S. Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models

et al. 2020

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“…To be more precise, herding phenomena have been studied concerning stock markets ( Chang et al., 2000 ; Chiang and Zheng, 2010 ; Balcilar et al., 2014 ; Litimi et al., 2016 ; Bohl et al, 2017 ), commodities markets ( Babalos and Stavroyiannis, 2015 ; BenMabrouk and Litimi, 2018 ), bond markets ( Galariotis et al., 2016 ) and mutual funds ( Deng et al., 2018 ). Furthermore, academic work about herding has focused on the house market ( Ngene et al., 2017 ) and REITs ( Philippas et al., 2013 ). Analysis has also been conducted in both microeconomic and macroeconomic levels ( Venezia et al., 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Kyriazis

2020

Heliyon

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This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.

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“…The first is that herding effect should be further explored in other aspects of human society. Previous studies have found herding effect in consumer behaviors (Chen et al, 2010; Moraes, 2016; Gao et al, 2017), stock markets (Chiang and Zheng, 2010; Blake et al, 2017; Kabir and Shakur, 2018), housing markets (Ngene et al, 2017), and group incidents (Xiong et al, 2016). The present study preliminarily indicated the existence of herding effect in business decisions.…”

Section: Discussionmentioning

confidence: 95%

“…Currently, this concept is also used to describe particular social phenomena, in which people are strongly influenced by others’ decisions and follow behaviors of others in the same group (Asch, 1956; Banerjee, 1992). In fact, herding effect widely exists in human society and has been found in consumer behavior (Chen et al, 2010; Moraes, 2016; Gao et al, 2017), stock markets (Chiang and Zheng, 2010; Blake et al, 2017; Kabir and Shakur, 2018), housing markets (Ngene et al, 2017), and group incidents (Xiong et al, 2016). In past decades, a large number of studies have explored the determinants of herding effect and its impact on individual decisions and behaviors using surveys and behavioral tests (Banerjee, 1992; Cipriani and Guarino, 2005; Chen, 2008; Pierdzioch and Stadtmann, 2010; Huang et al, 2015; Chong et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Neural Basis of Herding Decisions in Enterprise Clustering: An Event-Related Potential Study

et al. 2019

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Herding behavior refers to the social phenomenon in which people are intensely influenced by the decisions and behaviors of others in the same group. Although several recent studies have explored the neural basis of herding decisions in people’s daily lives (e.g., consumption decisions), the neural processing of herding decisions underlying enterprise behavior is still unclear. To address this issue, this study extracted event-related potentials (ERPs) from electroencephalographic data when participants (i.e., top executives in real enterprises) performed a choice task in which they judged whether to let their enterprises settle in an industrial zone when the occupancy rate of the industrial zone was either low or high. The behavioral results showed that participants had a higher acceptance rate in the high occupancy rate condition than in the low one, suggesting the existence of herding tendency in top executives’ business decisions. The ERP results indicated that anticonformity choices induced a larger N2 amplitude than herding choices, demonstrating that participants might experience larger perceived risk and more decision conflict when they processed anticonformity choices. In contrast, we observed that herding choices induced a larger LPP amplitude than anticonformity choices, hinting that participants might experience better evaluation categorization and higher decision confidence when they processed herding choices. Based on these results, this study provides new insights into the neural basis of herding decisions made by top executives in business.

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Time-Varying and Spatial Herding Behavior in the US Housing Market: Evidence from Direct Housing Prices

Cited by 25 publications

References 60 publications

Estimating U.S. Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models

Estimating U.S. Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

The Neural Basis of Herding Decisions in Enterprise Clustering: An Event-Related Potential Study

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