International Journal of Data Science and Analysis

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Investigating the Existence of a Bubble in the Kenyan Real Estate Market

Received: 11 October 2018    Accepted: 23 October 2018    Published: 21 November 2018
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Abstract

In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integration tests. Secondly, the study aimed at identifying the presence of a Kenyan real estate bubble using the forward-recursive Generalized Augmented Dickey-Fuller test (GSADF) and finally measured the size of the bubble at a given time relative to other key macroeconomic variables. The study utilized quarterly data on house prices and rental prices in Kenya and macroeconomic determinants from the year 2004 to 2017 September. Stationarity test revealed that the variables were stationary in their first difference I (1). Cointegration test revealed that there was no long term and short term house price dynamics between house prices and the macroeconomic variables at a lag of 4 determined through AIC, SIC and HQ criterion. Again a Granger causality test was performed and the results revealed that the macroeconomic variables did not Granger-cause house prices and vice versa. To investigate the presence of a Kenyan real estate bubble, cointegration test, and GSADF were performed and the results indicated the existence of a bubble in the Kenyan real estate. Two time period bubbles were identified from September 2009 to January 2010 and the other from April 2011 to September 2011. Finally, the bubble sizes were measured and were found to be 15% each in the two periods.

DOI 10.11648/j.ijdsa.20180405.13
Published in International Journal of Data Science and Analysis (Volume 4, Issue 5, October 2018)
Page(s) 89-97
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Bubble, Real Estate, Generalized Supremum ADF, Cointegration, Vector Error Correction Model (VECM)

References
[1] KNBS, D. G. (2014). Kenya Facts and Figure 2014 (Booklet) Nairobi Kenya: Kenya National Bureau of Statistics.
[2] CAHF (2012). Housing Finance in Africa. A review of Africa’s housing finance markets (2012 Year Book). Midrand: Centre for Affordable Housing Finance in Africa. A division of the Finmark Trust.
[3] Knight, F. H. (2012). Risk, uncertainty and profit. Courier Corporation.
[4] Consult, H. (2014). Residential areas of Kenya.
[5] Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings papers on economic activity, 2003 (2), 299-342.
[6] Belke, A., Weidman, M. (2013). Monetary Policy, Stock Prices and Central Banks–Cross-Country Comparisons of Cointegrated VAR Models. Ruhr Economic Papers #435.
[7] Smith, P. N., Sorensen, S., & Wickens, M. R. (2008). General equilibrium theories of the equity risk premium: estimates and tests. Quantitative and Qualitative Analysis Social Sciences, 2, 35-66.
[8] Baker, D. (2008). The housing bubble and the financial crisis. Real-world economics review, 46 (20), 73-81.
[9] Bulut, Z. B. (2009). Demand and supply of real estate market in Turkey: a cointegration analysis. Yayınlanmamış Yüksek Lisans Tezi. Ocak. Bilkent University.
[10] Kargi, B. (2013). Integration between the Economic Growth and the Construction Industry: A Time Series Analysis on Turkey (2000-2012).
[11] Panagiotis and Printzis (2015). Macroeconomic determinants of the housing market in Greece: a VECM approach. Hellenic Observatory, European Institute, London, UK.
[12] Phillips, P. C., & Yu, J. (2011). Dating the timeline of financial bubbles during the subprime crisis. Quantitative Economics, 2 (3), 455-491.
[13] Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Limit theory of real‐time detectors. International Economic Review, 56 (4), 1079-1134.
[14] Shen, Y. (2006). Housing price bubbles in Hong Kong, Beijing and Shanghai: a comparative study, J. Real Estate Financ, 33, 299-327.
[15] Dickey, D. and W. Fuller, 1979. Distribution of the estimators for autoregressive time series with a Unit Root. J. the American Statistical Association, 74 (366): 427-431.
[16] Johansen, S. and K. Juselius, 1990. Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics and Statistics, 52 (2): 169-210.
[17] Alexander, C., 2001. Market models: A guide to financial data analysis. John Wiley & Sons Ltd.
[18] Hui, E. C., & Yue, S. (2006). Housing price bubbles in Hong Kong, Beijing and Shanghai: a comparative study. The Journal of Real Estate Finance and Economics, 33 (4), 299-327.
[19] Arshanapalli, B., & Nelson, W. (2008). A cointegration test to verify the housing bubble. The International Journal of Business and Finance Research, 2 (2), 35-43.
[20] Jiang, Heng, Yu Song, and Chunlu Liu. "House price bubble estimations in Australia’s capital cities with market fundamentals." Pacific Rim Property Research Journal 17, no. 1 (2011): 132-156.
[21] Deng, Y., Girardin, E., Joyeux, R., & Shi, S. (2017). Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?. Pacific Economic Review, 22 (3), 276-292.
[22] Caspi, I. (2016). Testing for a housing bubble at the national and regional level: the case of Israel. Empirical Economics, 51 (2), 483-516.
Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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    Paul Kiarie Njoroge, Jane Akinyi Aduda, Carol Mugo. (2018). Investigating the Existence of a Bubble in the Kenyan Real Estate Market. International Journal of Data Science and Analysis, 4(5), 89-97. https://doi.org/10.11648/j.ijdsa.20180405.13

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    Paul Kiarie Njoroge; Jane Akinyi Aduda; Carol Mugo. Investigating the Existence of a Bubble in the Kenyan Real Estate Market. Int. J. Data Sci. Anal. 2018, 4(5), 89-97. doi: 10.11648/j.ijdsa.20180405.13

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    AMA Style

    Paul Kiarie Njoroge, Jane Akinyi Aduda, Carol Mugo. Investigating the Existence of a Bubble in the Kenyan Real Estate Market. Int J Data Sci Anal. 2018;4(5):89-97. doi: 10.11648/j.ijdsa.20180405.13

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  • @article{10.11648/j.ijdsa.20180405.13,
      author = {Paul Kiarie Njoroge and Jane Akinyi Aduda and Carol Mugo},
      title = {Investigating the Existence of a Bubble in the Kenyan Real Estate Market},
      journal = {International Journal of Data Science and Analysis},
      volume = {4},
      number = {5},
      pages = {89-97},
      doi = {10.11648/j.ijdsa.20180405.13},
      url = {https://doi.org/10.11648/j.ijdsa.20180405.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijdsa.20180405.13},
      abstract = {In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integration tests. Secondly, the study aimed at identifying the presence of a Kenyan real estate bubble using the forward-recursive Generalized Augmented Dickey-Fuller test (GSADF) and finally measured the size of the bubble at a given time relative to other key macroeconomic variables. The study utilized quarterly data on house prices and rental prices in Kenya and macroeconomic determinants from the year 2004 to 2017 September. Stationarity test revealed that the variables were stationary in their first difference I (1). Cointegration test revealed that there was no long term and short term house price dynamics between house prices and the macroeconomic variables at a lag of 4 determined through AIC, SIC and HQ criterion. Again a Granger causality test was performed and the results revealed that the macroeconomic variables did not Granger-cause house prices and vice versa. To investigate the presence of a Kenyan real estate bubble, cointegration test, and GSADF were performed and the results indicated the existence of a bubble in the Kenyan real estate. Two time period bubbles were identified from September 2009 to January 2010 and the other from April 2011 to September 2011. Finally, the bubble sizes were measured and were found to be 15% each in the two periods.},
     year = {2018}
    }
    

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    AU  - Paul Kiarie Njoroge
    AU  - Jane Akinyi Aduda
    AU  - Carol Mugo
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    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijdsa.20180405.13
    AB  - In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integration tests. Secondly, the study aimed at identifying the presence of a Kenyan real estate bubble using the forward-recursive Generalized Augmented Dickey-Fuller test (GSADF) and finally measured the size of the bubble at a given time relative to other key macroeconomic variables. The study utilized quarterly data on house prices and rental prices in Kenya and macroeconomic determinants from the year 2004 to 2017 September. Stationarity test revealed that the variables were stationary in their first difference I (1). Cointegration test revealed that there was no long term and short term house price dynamics between house prices and the macroeconomic variables at a lag of 4 determined through AIC, SIC and HQ criterion. Again a Granger causality test was performed and the results revealed that the macroeconomic variables did not Granger-cause house prices and vice versa. To investigate the presence of a Kenyan real estate bubble, cointegration test, and GSADF were performed and the results indicated the existence of a bubble in the Kenyan real estate. Two time period bubbles were identified from September 2009 to January 2010 and the other from April 2011 to September 2011. Finally, the bubble sizes were measured and were found to be 15% each in the two periods.
    VL  - 4
    IS  - 5
    ER  - 

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