Investigating the Effect of Liquidity and Per capita Income on the Housing Market (Using a vector auto regression model)

Document Type : Original Article

Authors
shahrzm vahedi 1
Farhad Hanifi 2
seyyed jalal sadeghi sharif 3
1 Student of Financial Management, Department of Management, UAE Branch, Islamic Azad University, Dubai, United Arab Emirates
2 Assistant Professor, Department of Commerce, Tehran Branch, Islamic Azad University, Tehran, Iran .
3 Assistant Professor, Department of Financial Management, Shahid Beheshti University, Tehran, Iran.
Abstract
Abstract
The housing market has been one of the most volatile sectors of the economy in recent times, experiencing periods of stagnation and boom. It is important to note that the housing sector is most closely linked to other sectors of the economy. With the recession, the whole economy will be in crisis. Also, the housing sector, given these features, has a stronger impact on investment and housing prices than short-term economic fluctuations, as well as its widespread and past relevance to other sectors, has the potential to generate growth and development in other sectors of the economy and can serve as an endogenous growth incentive. To play a slower role, and to stimulate, to stimulate economic growth in the short term and to drive the recession out. Therefore, further reflection is necessary in this section. Therefore, in this study, using the vector auto regression Time Series (VAR) analysis model, we investigate the interaction between housing price markets of some macroeconomic variables such as liquidity volume, per capita income. The results showed that the volume of liquidity has a significant share in the volatility of the housing market. Therefore, policymakers in the economic field should pay more attention to this.
Keywords
housing market
vector auto regression
liquidity volume
per capita income
  • اصلانی، پروانه؛ خسروی، تقوا. (1391). تحلیل عوامل مؤثر بر حباب قیمت مسکن در تهران. فصلنامه پژوهش­ها و سیاست­های اقتصادی، 20(61)، 132- 105.
  • خلیلی عراقی، سید منصور؛ مهرآرا، محسن و عظیمی، سیدرضا. (1391). بررسی عوامل مؤثر بر قیمت مسکن در ایران با استفاده از داده های ترکیبی. فصلنامه پژوهش­ها و سیاست­های اقتصادی، 20(63)، 50- 33.
  • خنجری، سمیه؛ همایونی فر، مسعود (1390). بررسی اثرات شوک­های اسمی بر دستمزد واقعی در بخش صنعتی ایران .پژوهشنامه­ی اقتصادی، شماره­ی دوم، صص 97-110.
  • سوری، امیررضا؛ حیدری، حسن و افضلی، حسین. (1391). بررسی رابطه متغیرهای طرف تقاضا و عرضه مؤثر بر بخش مسکن بر قیمت مسکن در ایران. فصلنامه پژوهش­های اقتصادی، 12(1)، 140- 113.
  • عباسی­نژاد، حسین؛ یاری، حمید. (1387). تأثیر شوک­های نفتی بر قیمت مسکن در ایران. فصلنامه، پژوهش­های اقتصادی، 9(1)، 77- 59.
  • قلی‌زاده علی‌‌اکبر، براتی جواد. تأثیر سیاست‌های پولی و مالی بر سرمایه‌گذاری مسکونی در اقتصاد‌ باز . فصلنامه پژوهش ها وسیاست های اقتصادی. ۱۳۹۰; ۱۹ (۵۸) :۳۱-۵۰
  • مهرآرا، محسن؛ شهاب لواسانی، کیوان. (1391). آثار تکانه‌های نفتی و سیاست­های پولی بر رفتار چرخه­ای قیمت مسکن. فصلنامه تحقیقات مدل­سازی اقتصادی، 7، 26- 1.
  • یدری، حسن؛ سوری، امیررضا. (1389). بررسی رابطه نرخ سود سپرده­های بانکی و قیمت مسکن در ایران. مجله تحقیقات اقتصادی، 45(3)، 92- 65.
  • Asteriou, Dimitrios; Hall, Stephen G. (2011). Vector Autoregressive (VAR) Models and Causality Tests. Applied Econometrics (Second ed.). London: Palgrave MacMillan. pp. 319–333.
  • Balasubramanyan, L., & Coulson, E. (2013). Do House Prices Impact Business Starts?. Journal of Housing Economics, 22(1), 36-44.
  • Beltratti, A., & Morana, C. (2010). International House Prices and Macroeconomic Fluctuations. Journal of Banking & Finance, 34(3), 533-545.
  • ‎Bojan Grum, Darja Kobe Govekar(2016),‎Influence of Macroeconomic Factors on Prices of Real Estate in Various Cultural Environments: Case of Slovenia, Greece, France, Poland and Norway,Procedia Economics and Finance, Volume 39, Pages 597-604
  • Bouchouicha, R., Ftiti, Z., 2012. Real estate markets and the macroeconomy: a dynamic coherence framework. Economic Modelling 29, 1820–1829.
  • Chun Tsai(2016),Relationships among regional housing markets: Evidence on adjustments of housing burden, Economic Modelling, In press, corrected proof, Available online 3 October 2018
  • Del Negro, M., & Otrok, C. (2007). 99 Luftballons: Monetary Policy and the House Price Boom Across US States. Journal of Monetary Economics, 54(7), 1962-1985.
  • Enders, Walter (2010). Applied Econometric Time Series (Third ed.). New York: John Wiley & Sons. pp. 272–355. ISBN978-0-470-50539-7.
  • Federico Camerin, Francesco Gastaldi(2018),Italian military real estate assets re-use issues and opportunities in three capital cities, Land Use Policy, Volume 78, Pages 672-681
  • Frank J. Fabozzi, Keli Xiao(2018),Explosive rents: The real estate market dynamics in exuberance,The Quarterly Review of Economics and Finance, Volume 66, Pages 100-107
  • Golob, K., M. Bastiˇc, and I. Pšunder. (2012). Analysis of Impact Factors on the Real Estate Market: Case Slovenia. Engineering Economics 23 (4): 357–67.
  • Hatemi-J, A.; Hacker, R. S. (2009). Can the LR test be helpful in choosing the optimal lag order in the VAR model when information criteria suggest different lag orders? . Applied Economics. 41 (9): 1489–1500.
  • Herman Donner, Han-Suck Song, Mats Wilhelmsson. (2016). Forced sales and their impact on real estate prices,Journal of Housing Economics, Volume 34, December 2016, Pages 60-68
  • ‎Jiaqi Ge(2017),‎Endogenous rise and collapse of housing price: An agent-based model of the housing market,Computers, Environment and Urban Systems, Volume 62, March 2017, Pages 182-198
  • Johnson, W. R. (2014). House Prices and Female Labor Force Participation. Journal of Urban Economics, 82, 1- 11.
  • Liyin Shen, Zhenyu Zhang, Xiaoling Zhang. (2017).Key factors affecting green procurement in real estate development: a China study,Journal of Cleaner Production, Volume 153, Pages 372-383
  • ‎Mandy H. M. Lau, Xueji Wei. (2018). ‎Housing size and housing market dynamics: The case of micro-flats in Hong Kong, Land Use Policy, Volume 78, Pages 278-286
  • Ning, C., & Hoon, O. D. (2012). Case studies of the effects of speculation on real estate price bubble forming: Beijing and Shanghai (2001-2010). Australia: PRESS Adelaide
  • Nneji, O , Chris Brooks, Charles W.R. Ward .(2013). House price dynamics and their reaction to macroeconomic changes, Economic Modelling 32 (2013) 172–178
  • Nneji, O., Brooks, C., & Ward, C. W. (2013). House Price Dynamics and Their Reaction to Macroeconomic Changes. Economic Modelling, 32, 172-178.
  • Qin, Duo (2011). Rise of VAR Modelling Approach. Journal of Economic Surveys. 25 (1): 156–174.
  • Shi, S., Jou, J. B., & Tripe, D. (2014). Can Interest Rates Really Control House Prices? Effectiveness and Implications for Macroprudential Policy. Journal of Banking & Finance, 47, 15- 28.
  • Tse, C. B., Rodgers, T., & Niklewski, J. (2014). The 2007 Financial Crisis and the UK Residential Housing Market: Did the Relationship Between Interest Rates and House Prices Change?. Economic Modelling, 37, 518-53
  • Valadez, R. M. (2010). The Housing Bubble and The GDP: a correlation perspective. Journal of Case Research in Business and Economics, 1-18.
  • Wei Tang, Yuan Wang. (2017). Incomplete information and real estate development strategy: Evidence from Hangzhou, China,Habitat International, Volume 63, Pages 1-10
  • Woodford, M. (2010). Financial intermediation and macroeconomic analysis. The Journal of Economic Perspectives, 24(4):21-44.
  • Ying Fan, Zan Yang, Abdullah Yavas. (2019). Understanding real estate price dynamics: The case of housing prices in five major cities of China,Journal of Housing Economics, Volume 43, Pages 37-55.