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This pioneering study analyzes herding and reverse herding in US housing markets using Zillow ZIP-level house price indices. It discovers that reverse herding is more prevalent than herding, a contrast to equity markets and less detailed house price indices. The findings propose that the interplay of price appreciation and overconfidence may fuel reverse herding, with their occurrence heavily influenced by market conditions and local characteristics.
This study is the first to explore herding and reverse herding in the US housing markets at the metropolitan statistical area (MSA) level, allowing for the identification of local variations. Housing markets possess unique characteristics, such as local variations and high information acquisition costs, necessitating an examination of both herding and reverse herding at the local level. The study investigates the environments in which these behaviors occur, including market conditions, major crisis periods, and the interaction of overconfidence with price appreciation. It contributes to the literature by expanding research on herding in real estate, particularly housing, and by analyzing it at a new spatial level. Additionally, the study delves into the phenomenon of reverse herding, highlighting its significance and proposing a unique measure of individual overconfidence. Evidence suggests that reverse herding is more prevalent than herding, potentially due to factors like homeowner overconfidence, strong private information in local markets, and market maturity. The study finds herding more common in down markets and before the Global Financial Crisis (GFC), while reverse herding is more common in up markets and after the GFC.
Herding, the tendency for individuals to mimic the actions of others in financial decision-making, can occur rationally or irrationally, leading to sub-optimal investment outcomes and bubble formation. Rational herding involves investors following the crowd based on the belief that the group possesses superior knowledge. Conversely, irrational herding occurs when behavioral biases override rational decision-making processes, such as the need to keep up with societal norms or personal desires. This behavior can compress returns around the market average, potentially leading to bubbles and subsequent market collapse.
Studies show evidence of herding in various financial markets, including real estate investment trusts (REITs) and housing markets, with factors such as market sophistication and culture influencing its prevalence. While herding is more common in developing markets, reverse herding, where investors deviate from the consensus, is more prevalent in developed ones.
Reverse herding can stem from factors like overconfidence or reliance on private information, particularly in markets with high information acquisition costs like real estate. It’s suggested that reverse herding may be more observable at a granular level, such as across Metropolitan Statistical Areas (MSAs), due to local market dynamics and heterogeneity.
Market maturity and volatility also impact herding behaviors, with both traditional herding and reverse herding playing significant roles in speculative bubbles. Overconfidence, exacerbated by market conditions and personal biases, can drive both herding and reverse herding, contributing to market instability.
Understanding both herding and reverse herding behaviors is crucial for identifying and mitigating the risks of speculative bubbles, as they can amplify market fluctuations and lead to systemic issues. This perspective underscores the importance of considering psychological and behavioral factors in financial market analysis and regulation.
This paper investigates herding and reverse herding at the Metropolitan Statistical Area (MSA) level and identifies substantial evidence of potentially irrational responses to significant increases in absolute market returns. Analyzing these phenomena across various market conditions and spatial levels contributes significantly to herding research. Consistent with existing literature, herding tends to occur primarily during downturns, while reverse herding is more prevalent in bullish market conditions. The Global Financial Crisis (GFC) appears to have induced some permanent changes in behavior, with herding becoming less common while reverse herding occurrences doubled. The study suggests that inefficient markets and homeowner overconfidence may contribute to reverse herding, particularly due to the costly commitment of home purchasing. Future research should focus on establishing reliable measures of confidence at the MSA level. In local geographical contexts, individuals likely possess substantial knowledge of housing markets, potentially leading to overconfidence and motivating reverse herding. The high costs of information acquisition and the relatively low institutional involvement in housing markets may exacerbate irrational responses to market dynamics. Consumption remains the primary driver in housing markets, emphasizing the need to consider herding behavior in the context of consumption-driven decisions, particularly for owner-occupiers. These findings have policy implications as they can serve as leading indicators of market instability, particularly regarding the link between herding behavior and bubble formation, thereby informing lenders, investors, and policymakers.
Source:
Pollock, M., Mori, M., & Wu, Y. (2024). Herding and reverse herding in US housing markets: new evidence from a metropolitan-level analysis. Regional Studies, 1–16. https://doi.org/10.1080/00343404.2024.2325613