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The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks

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  • Anthony C Constantinou
  • Norman Fenton

Abstract

In 2015 the British government announced a number of major tax reforms for individual landlords. To give landlords time to adjust, some of these tax measures are being introduced gradually from April 2017, with full effect in tax year 2020/21. The changes in taxation have received much media attention since there has been widespread belief that the new measures were sufficiently skewed against landlords that they could signal the end of the Buy-To-Let (BTL) investment era in the UK. This paper assesses the prospective performance of BTL investments in London from the investor’s perspective, and examines the impact of incoming tax reforms using a novel Temporal Bayesian Network model. The model captures uncertainties of interest by simulating the impact of changing circumstances and the interventions available to an investor at various time-steps of a BTL investment portfolio. The simulation results suggest that the new tax reforms are likely to have a detrimental effect on net profits from rental income, and this hits risk-seeking investors who favour leverage much harder than risk-averse investors who do not seek to expand their property portfolio. The impact on net profits also poses substantial risks for lossmaking returns excluding capital gains, especially in the case of rising interest rates. While this makes it less desirable or even non-viable for some to continue being a landlord, based on the current status of all factors taken into consideration for simulation, investment prospects are still likely to remain good within a reasonable range of interest rate and capital growth rate variations. The results also suggest that the recent trend of property prices in London increasing faster than rents will not continue for much longer; either capital growth rates will have to decrease, rental growth rates will have to increase, or we shall observe a combination of the two events.

Suggested Citation

  • Anthony C Constantinou & Norman Fenton, 2017. "The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-30, June.
  • Handle: RePEc:plo:pone00:0179297
    DOI: 10.1371/journal.pone.0179297
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    References listed on IDEAS

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    1. Kuo, Chiong-Long, 1997. "A Bayesian Approach to the Construction and Comparison of Alternative House Price Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 113-132, Jan.-Marc.
    2. Sandra Gomes, 2011. "Housing Market Dynamics: Any News?," Working Papers w201121, Banco de Portugal, Economics and Research Department.
    3. Eric Mayer & Johannes Gareis, 2013. "What Drives Ireland’s Housing Market? A Bayesian DSGE Approach," Open Economies Review, Springer, vol. 24(5), pages 919-961, November.
    4. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    5. Ghosh, Gaurav S. & Carriazo, Fernando, 2007. "Bayesian and Frequentist Approaches to Hedonic Modeling in a Geo-Statistical Framework," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon 9916, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Sam K. Hui & Alvin Cheung & Jimmy Pang, 2010. "A Hierarchical Bayesian Approach for Residential Property Valuation:Application to Hong Kong Housing Market," International Real Estate Review, Global Social Science Institute, vol. 13(1), pages 1-29.
    7. Shang, Yilun, 2016. "On the likelihood of forests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 157-166.
    8. Norman Fenton, 2011. "Improve statistics in court," Nature, Nature, vol. 479(7371), pages 36-37, November.
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