نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسندگان English
Aim and Introduction
By integrating insights from psychology—especially cognitive psychology—into economic theory, behavioral economics provides a more realistic understanding of human behavior and economic decision-making (Thaler, 2017). A key subset of this field is behavioral finance, which posits that investment decisions are not always based on rational optimization. Instead, behavioral factors often lead to perceptual distortions, biased judgments, and irrational interpretations. These tendencies stem from various behavioral biases—collectively referred to as irrational behaviors—which commonly arise due to investors’ limited capacity to process information and the impact of emotional factors on their decision-making (Tan, 2022).
One notable cognitive bias is herding behavior, which refers to individuals mimicking the actions of the majority. This phenomenon is particularly notorious in markets such as housing, coins, and currency, where it is widely regarded by experts as a primary driver of severe and irrational price fluctuations (Rook, 2006).
Methodology
This research employs spatial econometric techniques to analyze the effects of dependency culture on herding behavior in the housing market across 31 Iranian provinces from (2011–2021) on a seasonal basis. Spatial econometrics extends traditional panel data models by incorporating geographical dimensions, which enables the analysis of spatial interdependence and regional heterogeneity. In the presence of spatial components, two primary issues must be addressed: spatial dependence, which refers to correlation among geographically proximate units, and spatial heterogeneity, which refers to structural differences across regions.
Before estimating the spatial panel models, tests for spatial autocorrelation were conducted to determine the necessity of incorporating spatial effects into the analysis. Specifically, Moran’s I, Geary’s C, and Getis-Ord J statistics were used to assess the presence of spatial autocorrelation among the error terms. A significant spatial dependence justifies the application of spatial econometric models. To define spatial relationships, two forms of spatial weighting structures were considered: coordinate-based distances derived from latitude and longitude, and neighborhood-based contiguity matrices that capture the relative location of each province in relation to others. Based on the detection of significant spatial autocorrelation, the Spatial Autoregressive (SAR) model was selected to capture the dynamic spatial interactions within the housing market across Iranian provinces.
Findings
The results of the spatial econometric analysis confirm that exchange rate fluctuations have a positive and statistically significant impact on the housing market across both the target provinces and their neighboring regions. This finding supports the hypothesis that dependency culture, shaped by sensitivity to macroeconomic signals such as exchange rate movements, plays a key role in fostering herd behavior within Iran’s housing sector during the study period. The presence of spatial spillovers indicates that changes in one province can influence housing activity in surrounding areas, reinforcing regional contagion effects.
In addition to the exchange rate, the variables of inflation rate, population density index, and the logarithm of stock exchange transaction volume were also found to have positive and significant effects on housing market dynamics. These factors appear to stimulate speculative behavior and intensify market activity. Conversely, the logarithm of the distance from Tehran province exhibited a negative and significant effect on housing market outcomes.
Discussion and Conclusion
In Iran, there are no legal limitations on the frequency of property transactions, which allows a residential unit or parcel of land to be repeatedly traded within a year. This lack of regulation encourages speculative and herding behavior. To mitigate this, the study recommends implementing transaction limits and a more effective taxation system, similar to those used in developed countries. For example, imposing higher taxes on multiple home ownership and on vacant housing units can discourage speculation.
Despite the high number of vacant units, a significant proportion of Iranian households remain without access to adequate housing and face declining
welfare due to soaring rents. Targeted housing assistance—including free land allocation—could help meet the actual demand and reduce speculative demand, thereby limiting herd behavior.
Furthermore, price booms typically originate in metropolitan and affluent regions, suggesting that a more balanced spatial development strategy could help diffuse housing market pressures. Introducing region-specific construction and transaction regulations, especially in high-risk speculative areas, could further manage housing price volatility.
Finally, encouraging investment in parallel financial markets and increasing stability and public trust in those markets could redirect speculative behavior away from real estate. Creating viable alternative investment opportunities would absorb excess liquidity and help stabilize the housing sector.
کلیدواژهها English
