آمار
| تعداد نشریات | 36 |
| تعداد شماره‌ها | 1,192 |
| تعداد مقالات | 8,579 |
| تعداد مشاهده مقاله | 6,988,705 |
| تعداد دریافت فایل اصل مقاله | 4,032,550 |
مدل‌سازی تفسیری - ساختاری تورش‌های رفتاری سرمایه‌گذاران بخش مسکن | ||
| پژوهش‌های راهبردی بودجه و مالیه | ||
| دوره 5، شماره 1 - شماره پیاپی 17، فروردین 1403، صفحه 79-101 اصل مقاله (1.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| محمد مهدوی پور* 1؛ حسین شیرمردی احمدآباد2؛ حمید مرتضی نیا3 | ||
| 1دانش آموخته کارشناسی ارشد مدیریت مالی، دانشکده مدیریت و برنامه‌ریزی راهبردی، دانشگاه جامع امام حسین (ع)، تهران، ایران | ||
| 2پژوهشگر، گروه مدیریت مالی اسلامی، دانشکده مدیریت و برنامه‌ریزی راهبردی ، دانشگاه جامع امام حسین(ع)،تهران، ایران | ||
| 3استادیار، گروه مدیریت مالی اسلامی، دانشکده مدیریت و برنامه‌ریزی راهبردی ، دانشگاه جامع امام حسین (ع)، تهران، ایران | ||
| تاریخ دریافت: 20 مهر 1402، تاریخ بازنگری: 18 آذر 1402، تاریخ پذیرش: 01 اسفند 1402 | ||
| چکیده | ||
| بنا بر مطالعات، بازار مسکن نیز همانند بازارهای مالی همیشه منطقی رفتار نمی‌کند و در زمانهای مختلف، خلاف قاعده‌های بازار و تورش‌های رفتاری متعددی در بازار مسکن مشاهده می‌شود. مطالعه سوگیری‌های رفتاریدر کنار سایر متغیرهای تصمیم‌گیری‌های، سیاستگذاری‌های اقتصادی، را بهبود خواهد بخشید. به این منظور ابتدا با مطالعات کتابخانه‌ای، تورش‌های تعریف شده جمع‌آوری و سپس تأثیرات تورش‌های شناسایی شده با کمک روش دلفی، کشف گردید. در مرحله دوم، توسط گروه کانونی متشکل از خبرگان مالی رفتاری و مسکن، با توجه به نتایج دلفی و همچنین جامع سازی تعاریف، ده تورش تأثیرگذار بر سرمایه‌گذاران بازار مسکن شناسایی شد. در نهایت، مبتنی بر ماتریس خودتعاملی تهیه شده از نظرات 13 نفر از خبرگان ، مدل نهایی پنج سطحی با روش تفسیری- ساختاری ترسیم گردید که در سطح پنجم این مدل باور گرایی وفرا اعتمادی و در سطح چهارم داشته بیش نگری و در سطح سوم افسوس‌گریزی و تعاملات اجتماعی و خوداسنادی و در سطح دوم دیرپذیری و در سطح اول شکل گرایی، بیش واکنشی و لنگر انداختن قرار گرفتند. | ||
| کلیدواژه‌ها | ||
| بازار مسکن؛ تورش رفتاری؛ مدل‌سازی تفسیری - ساختاری؛ تورم بازار مسکن؛ سوگیری‌های شناختی | ||
| عنوان مقاله [English] | ||
| Interpretive-Structural Modeling of Behavioral Biases of Housing Sector Investors | ||
| نویسندگان [English] | ||
| Mohammad Mahdavipour1؛ hossein shirmardi2؛ Hamid Morteza Nia3 | ||
| 1Master's degree in financial management, Faculty of Management and Strategic Planning, Imam Hossein University (AS), Tehran, Iran | ||
| 2Researcher, Department of Islamic Financial Management, Faculty of Management and Strategic Planning, Imam Hossein University, Tehran, Iran | ||
| 3Assistant Professor, Department of Islamic Financial Management, Faculty of Management and Strategic Planning, Imam Hossein University (AS), Tehran, Iran | ||
| چکیده [English] | ||
| Based on studies, the housing market, much like financial markets, does not always behave rationally. Various behavioral biases are observed in the housing market at different times, contrary to market norms. Studying these behavioral biases, alongside other decision variables and economic policies, will enhance understanding and improvement. To achieve this, initially, defined biases were collected through literature reviews, and the impacts of the identified biases were discovered using the Delphi method. In the second phase, a canonical group composed of experts in behavioral finance and housing identified ten influential biases on housing market investors based on Delphi results and a comprehensive definition synthesis. Finally, based on an interaction matrix derived from the opinions of 13 experts, a five-tier model was drawn using the interpretive-structural method. In this model, at the fifth level, we Confirmation Bias and Overconfidence; at the fourth level, Endowment Bias; at the third level, regret aversion, social interactions(Herd Behavior) and self-attribution; at the second level, Conservatism Bias; and at the first level, Framing Bias, overreaction, and anchoring and adjustment | ||
| کلیدواژه‌ها [English] | ||
| Housing market, Behavioral turmoil, Interpretive-Structural modeling, Housing market turmoil, Cognitive biases | ||
| مراجع | ||
|
Azar, A., & Bayat, K. (2008). Designing a model of business process orientation with an interpretive-structural modeling approach. Information Technology Management Journal, 1(1), 13-18. [in Persian]
Bahrami, J., & Morot, H. (2011). Modeling the boom and bust of the Tehran housing market with consideration of social dynamics. Research in Economic Policies and Economic Planning, (66), 168-143. [in Persian]
Cascão, A., Quelhas, A. P., & Cunha, A. M. (2023). Heuristics and cognitive biases in the housing investment market. International Journal of Housing Markets and Analysis, 16(5), 991-1006.
Chang, C.-C., Chao, C.-H., & Yeh, J.-H. (2016). The role of buy-side anchoring bias: Evidence from the real estate market. Pacific-Basin Finance Journal, 38, 34-58. https://doi.org/https://doi.org/10.1016/j.pacfin.2016.02.008
Cunha, A. M., & Lobão, J. (2022). The effects of tourism on housing prices: applying a difference-in-differences methodology to the Portuguese market. International Journal of Housing Markets and Analysis, 15(4), 762-779.
Das, P., Füss, R., Hanle, B., & Russ, I. N. (2020). The cross-over effect of irrational sentiments in housing, commercial property, and stock markets. Journal of Banking & Finance, 114, 105799. https://doi.org/https://doi.org/10.1016/j.jbankfin.2020.105799
Diaz III, J., & Hansz, J. A. (1997). How valuers use the value opinions of others. Journal of Property Valuation and Investment, 15(3), 256-260.
Duca, J. V., Hoesli, M., & Montezuma, J. (2021). The resilience and realignment of house prices in the era of Covid-19. Journal of European Real Estate Research, 14(3), 421-431.
Fallahpour, M., & Abdullahi, (2011). Identifying and weighting behavioral biases of investors in the Tehran Stock Exchange: A fuzzy AHP approach. Financial Research, 31(13), 120-99. [in Persian]
Fisher, G., Steiner, E., Titman, S., & Viswanathan, A. (2022). Location density, systematic risk, and cap rates: Evidence from REITs. Real Estate Economics, 50(2), 366-400.
Huang, J., Tzeng, G., & Ong, C. (2005). Multidimensional Data in Multidimensional Scaling Using the Analytic Network Process. Pattern Recognition Letters, 26.
Kahn, J. A. (2008). What Drives Housing Prices? Federal Reserve Bank of New York Staff Report, 345.
Kannana, G., Shaligram, P., & Kumarc, P. S. (2009). A Hybrid Approach Using ISM and Fuzzy TOPSIS for the Selection of Reverse Logistics Provider. Resources, Conservation and Recycling, 54, 28-36.
Khan, S., Khan, M., & Honnutagi, A. (2013). Conceptualized Model of Green IT Purchasing Enablers - An Application of Delphi Technique and Interpretive Structural Modeling. Business Sciences International Journal of Research, 1, 24-37.
Levy, D. S., Frethey-Bentham, C., & Cheung, W. K. S. (2020). Asymmetric framing effects and market familiarity: experimental evidence from the real estate market. Journal of Property Research, 37(1), 85-104. https://doi.org/10.1080/09599916.2020.1713858
Maleki, B. (2016). Analysis of the Iranian housing market. Industrial Management Organization Publications. [in Persian]
Nakajima, M. (2011). Understanding house-price dynamics. Business Review, 2(Q2), 20-28.
Njo, A., I. Made, N., & Irwanto, A. (2019). Dual process of dual motives in real estate market Indonesia. International Journal of Housing Markets and Analysis, 12(1), 25-42.
Pandey, R., & Jessica, V. M. (2018). Measuring behavioural biases affecting real estate investment decisions in India: using IRT. International Journal of Housing Markets and Analysis, 11(4), 648-668.
Piazzesi, M., & Schneider, M. (2009). Momentum Traders in the Housing Market: Survey Evidence and a Search Model. American economic review, 99(2), 406-411.
Qiu, L., Tu, Y., & Zhao, D. (2020). Information asymmetry and anchoring in the housing market: a stochastic frontier approach. Journal of Housing and the Built Environment, 35, 573-591.
Rahnama Rudposhti, F., & Zandiye, V. (2012). Behavioral finance and neurofinance (a new paradigm in finance) from theory to practice. Islamic Azad University. [in Persian]
Salzman, D., & Zwinkels, R. C. (2017). Behavioral real estate. Journal of Real Estate Literature, 25(1), 77-106.
Shahabadi, A., & Soosafi, R. (2007). An introduction to behavioral finance. Bourse Monthly. [in Persian]
Shen, L., Song, X., Wu, Y., Liao, S., & Zhang, X. (2016). Interpretive Structural Modeling based factor analysis on the implementation of Emission Trading System in the Chinese building sector. Journal of Cleaner Production, 127, 214-227.
Shiller, R. J. (2007). Understanding recent trends in house prices and home ownership. In: National Bureau of Economic Research Cambridge, Mass., USA.
Susanto, S. A., & Njo, A. (2019). First-home buyers and herding behavior in Surabaya, Indonesia. International Journal of Housing Markets and Analysis, 13(3), 393-411. https://doi.org/10.1108/IJHMA-04-2019-0041
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.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of risk and uncertainty, 5, 297-323.
Wang, Y. (2013). Study of herding behavior on China’s real estate market price fluctuations. Information Technology Journal, 12(23), 7926-7929. https://doi.org/10.3923/itj.2013.7926.7929
Yang, J., Cashel-Cordo, P., & Kang, J. G. (2020). Empirical research on herding effects: case of real estate markets. Journal of Accounting and Finance, 20(1), 122-130. | ||
|
آمار تعداد مشاهده مقاله: 173 تعداد دریافت فایل اصل مقاله: 256 |
||