Economic Research and Perspectives

Economic Research and Perspectives

Analysis of Herd Behavior Caused by Dependency Culture in Iran’s Housing Market

Document Type : Original Article

Authors
Vahid Nikpey Pesyan 1
Yousef MohhamadZadeh 2
Ali Rezazadeh 2
Habib Ansari Samani 3
1 PhD student, Urmia University, Urmia, Iran
2 Associate Professor, Faculty of Economics and Management, Urmia University, Urmia, Iran
3 Associate Professor, Faculty of Economics and Management, Yazd University, Yazd, Iran
10.48311/ecor.2025.13639
Abstract
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.
Keywords
Behavioral finance
Dependency culture
Exchange rate fluctuations
Herding behavior
Housing market
Iranian provinces
Spatial econometrics

Subjects

Andersson, M., Hedesström, M., & Gärling, T. (2014). A social-psychological perspective on herding in stock markets. Journal of Behavioral Finance, 
15(3), 226-234.
Bahaddin, B., Weinberg, S., Luna‐Reyes, L. F., & Andersen, D. (2019). Building a bridge to behavioral economics: Countervailing cognitive biases in lifetime saving decisions. System Dynamics Review, 35(3), 187-207.
Bahmani-Oskooee, M., & Wu, T. P. (2018). Housing prices and real effective exchange rates in 18 OECD countries: A bootstrap multivariate panel Granger causality. Economic Analysis and Policy, 60, 119-126.
Bertrand, M., Mullainathan, S., & Shafir, E. (2006). Behavioral economics and marketing in aid of decision making among the poor. Journal of Public Policy & Marketing, 25(1), 8-23.
Bikhchandani, S., & Sharma, S. (2000). Herd behavior in financial markets. IMF Staff Papers, 47(3), 279-310.
Case, K. E., & Shiller, R. J. (1988). The behavior of home buyers in boom and post-boom markets.
Çelen, B., & Kariv, S. (2004). Distinguishing informational cascades from herd behavior in the laboratory. American Economic Review, 94(3), 484-498.
Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity markets: An international perspective. Journal of Banking & Finance, 24(10), 1651-1679.
Clark, S. P., & Coggin, T. D. (2009). Trends, cycles and convergence in US regional house prices. The Journal of Real Estate Finance and Economics, 
39, 264-283.
Dadgar, Y., Dargahi, H., and Qolizadeh, S. (2023). The role of investor sentiments and government behavior on Tehran Stock Exchange market fluctuations: A behavioral economics approach. Scientific Quarterly of Applied Economic Theories, 10(1), 191-214. [In Persian]
Dean, H., & Taylor-Gooby, P. (2014). Dependency Culture. Routledge.
DellaValle, N. (2019). People’s decisions matter: Understanding and addressing energy poverty with behavioral economics. Energy and Buildings, 204, 109515.
Dhami, S. (2016). The Foundations of Behavioral Economic Analysis. Oxford University Press.
Diala, A. O., Kalu, I. U., & Igwe-Kalu, A. (2017). Effects of exchange rate volatility on low income residential real estate investment returns in Nigeria. Research Journal of Finance and Accounting, 8(6), 8-15.
Dold, M. F., & Rizzo, M. J. (2021). The limits of opportunity-only: Context-dependence and agency in behavioral welfare economics. Journal of Economic Methodology, 28(4), 364-373.
Galbács, P., & Galbács, P. (2015). The rational expectations hypothesis as a key element of new classical macroeconomics. The Theory of New Classical Macroeconomics: A Positive Critique, 53-90.
Glindro, E. T., Subhanij, T., Szeto, J., & Zhu, H. (2018). Determinants of house prices in nine Asia-Pacific economies. 26th. issue (September 2011) of: The International Journal of Central Banking.
Habibi, S., Haji Mollah Darwish, N., and Raghofer, Hossein (2022). The effect of mental poverty on labor force performance. Scientific Quarterly of Applied Economic Theories, 8(1), 165-184. [In Persian]
Hirshleifer, D., & Hong Teoh, S. (2003). Herd behaviour and cascading in capital markets: A review and synthesis. European Financial Management, 9(1), 25-66.
Holly, S., Pesaran, M. H., & Yamagata, T. (2011). The spatial and temporal diffusion of house prices in the UK. Journal of Urban Economics, 69(1), 2-23.
      https://ideas.repec.org/a/fip/fedbne/y1988inovp29-46.html
https://webofproceedings.org/proceedings_series/ECOM/MFSSR%202019/MFSSR19065.pdf
https://www.researchgate.net/publication/289785184_Factors_shaping_the_decision_to_become_a_landlord_and_retain_rental_investments
Jani, S., & Safizadeh, S. (2020). Analyzing tourism industry effect on employment among provinces by spatial econometric panel data approach. Quarterly Journal of Economic Research and Policies, 28(93), 233-266. [In Persian]
Klein, E., & Mills, C. (2017). Psy-expertise, therapeutic culture and the politics of the personal in development. Third World Quarterly, 38(9), 1990-2008.
Knoll, M. A. (2010). The role of behavioral economics and behavioral decision making in Americans' retirement savings decisions. Soc. Sec. Bull., 70, 1.
Lam, C. H. L., & Hui, E. C. M. (2018). How does investor sentiment predict the future real estate returns of residential property in Hong Kong?. Habitat International, 75, 1-11.
Levinson, J. D., & Peng, K. (2007). Valuing cultural differences in behavioral economics. ICFAI Journal of Behavioral Finance, 4, 32-47.
Lieb, L., & Schuffels, J. (2022). Inflation expectations and consumer spending: The role of household balance sheets. Empirical Economics, 63(5), 2479-2512.
Lo, D., Chau, K. W., Wong, S. K., McCord, M., & Haran, M. (2022). Factors affecting spatial autocorrelation in residential property prices. Land, 11(6), 931.
Mahalik, M. K., & Mallick, H. (2011). What causes asset price bubble in an emerging economy? Some empirical evidence in the housing sector of India. International Economic Journal, 25(2), 215-237.
Malliaris, Anastasios G., Mary Malliaris, and Mark S. Rzepczynski. (2024). One man’s bubble is another man’s rational behavior: Comparing alternative macroeconomic hypotheses for the US housing market. Journal of Risk and Financial Management, 17: 349.
Meen, G. (1999). Regional house prices and the ripple effect: A new interpretation. Housing Studies, 14(6), 733-753.
Miles, W. (2019). Regional convergence and structural change in US housing markets. Regional Studies, Regional Science, 6(1), 520-538.
Montero, M. (1998). Dialectic between active minorities and majorities: A study of social influence in the community. Journal of Community Psychology, 26(3), 281-289.
Ngene, G. M., & Gupta, R. (2023). Impact of housing price uncertainty on herding behavior: Evidence from UK’s regional housing markets. Journal of Housing and the Built Environment, 38(2), 931-949.
Nikpey, V., Mohammadzadeh, Y., Rezazadeh, A., & Ansari Samani, H. (2024). Spatial analysis of dependency culture resulting from exchange rate fluctuations on herding behavior in Iran’s housing market. International Journal of Housing Markets and Analysis. 18(6), 328-356.
Olweny, T., & Omondi, K. (2011). The effect of macro-economic factors on stock return volatility in the Nairobi stock exchange, Kenya. Economics and Finance Review, 1(10), 34-48.
Owusu, S. P., & Laryea, E. (2023). The impact of anchoring bias on investment decision-making: evidence from Ghana. Review of Behavioral Finance, 
15(5), 729-749.‏
Parveen, S., Satti, Z. W., Subhan, Q. A., Riaz, N., Baber, S. F., & Bashir, T. (2023). Examining investors' sentiments, behavioral biases and investment decisions during COVID-19 in the emerging stock market: a case of Pakistan stock market. Journal of Economic and Administrative Sciences, 39(3), 549-570.
Reza Zadeh, A., Jahangiri, S., Fahid Azer, Y., & Nikpey Pesyan, V. (2022). Investigating the impact of covid-19 on the relationship between cryptocurrencies and oil price shocks using the non-linear autoregressive distributed lag (NARDL) approach. Journal of asset management and financing, 10(2), 121-148. [In Persian]
Rook, L. (2006). An economic psychological approach to herd behavior. Journal of Economic Issues, 40(1), 75-95.
Ruoxi, Q. (2019). Behavior Analysis of Real Estate Investors. Mfssr, 308-312.
Samadi, A., Sohrabi, R., & Khazaei, M. M. (2013). Identification of behavioral biases affecting the decision making of individual shareholders in buying and selling shares in the Hamedan Regional Stock Exchange Hall. Journal of Industrial Management Studies, 9(28), 85–100. [In Persian] 
Samadipour, Sh., Qolizadeh, A., A., and Sepherdoost, H. (2023). Investigating the effect of behavioral components on housing prices in Iran. Quarterly Journal of Applied Economic Studies, 12(46), 241-274. [In Persian]
Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. The American Economic Review, Vol. 80, No. 3, 465-479.
Selsbil, M., Rafiei Atani., A., and Naini Naini, A. (2022). Explaining the model of consumer decision-making based on cognitive and behavioral sciences using the metacombination method. New Researches in Decision Making, 7(2), 88-109. [In Persian]
Sepidbar, Z., & Nikpey Pesyan, V., and Mohammadzadeh, Y. (2024). Analysis of the effect of entrepreneurship index on employment in Iranian provinces: Spatial econometric approach. The Economic Research, 24(1), 285-311.[In Persian]
Tan, C. (2022). A study of boundedly rational behaviour in housing choice: Evidence from Malaysia. International Journal of Housing Markets and Analysis, 15(5), 1259-1274.
Tan, L., Chiang, T. C., Mason, J. R., & Nelling, E. (2008). Herding behavior in Chinese stock markets: An examination of A and B shares. Pacific-Basin Finance Journal, 16(1-2), 61-77.
Thaler, R. H. (1990). Anomalies: Saving, fungibility, and mental accounts. Journal of Economic Perspectives, 4(1), 193-205.
Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12(3), 183-206.
Thaler, R. H. (2017). Behavioral economics. Journal of Political Economy, 125(6), 1799-1805.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty. Science, 185(4157), 1124-1131.
Umoi, I., Amiri Eghdai, S. F., and Ansari, A. (2022). Designing a model of misbehavior caused by the mental biases of customers in the financial industry; Case study: Bank and stock market customers. New Marketing Research Journal, 12(2), 67-90. [In Persian]
Van Nieuwerburgh, S., & Weill, P. O. (2010). Why has house price dispersion gone up?. The Review of Economic Studies, 77(4), 1567-1606.
Wilkinson, N., & Klaes, M. (2017). An Introduction to Behavioral Economics. Bloomsbury Publishing.
Wood, G., & Ong, R. (2010). Factors shaping the decision to become a landlord and retain rental investments.
Yang, C. F. (2021). Common factors and spatial dependence: An application to US house prices. Econometric Reviews, 40(1), 14-50.
Zheng, X., Chen, X., & Yuan, Z. (2021). Exploring the spatial spillover effect of home purchase restrictions on residential land prices based on the difference-in-differences approach: Evidence from 195 Chinese cities. Land Use Policy, 102, 105236.
Zhou, J., & Anderson, R. I. (2013). An empirical investigation of herding behavior in the US REIT market. The Journal of Real Estate Finance and Economics, 47, 83-108.
Zhou, R. T., & Lai, R. N. (2009). Herding and information based trading. Journal of Empirical Finance, 16(3), 388-393.