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Monte Carlo Simulations for Real Estate Valuation

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Author Info

  • Martin Hoesli

    ()

  • Elion Jani

    ()

  • André Bender

    ()

Abstract

We use the Adjusted Present Value (APV) method with Monte Carlo simulations for real estate valuation purposes. Monte Carlo simulations make it possible to incorporate the uncertainty of valuation parameters, in particular of future cash flows, of discount rates and of terminal values. We use empirical data to extract information about the probability distributions of the various parameters and suggest a simple model to compute the discount rate. We forecast the term structure of interest rates using a Cox et al. (1985) model, and then add a premium that is related to both the real estate market and selected property-specific characteristics. Our empirical results suggest that the central values of our simulations are in most cases slightly less than the hedonic values. The confidence intervals are found to be most sensitive to the long-term equilibrium interest rate being used and to the expected growth rate of the terminal value.

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Bibliographic Info

Paper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp148.

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Date of creation: Jun 2005
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Handle: RePEc:fam:rpseri:rp148

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Keywords: Real estate valuation; Monte Carlo simulations; Adjusted Present Value (APV);

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References

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  1. Åke GUNNELIN & Patric H. HENDERSHOTT & Martin HOESLI & Bo SÖDERBERG, 2003. "Determinants of Cross-Sectional Variation in Discount Rates, Growth Rates, and Exit Cap Rates," FAME Research Paper Series rp90, International Center for Financial Asset Management and Engineering.
  2. Jim Clayton, 1996. "Market Fundamentals, Risk and the Canadian Property Cycle: Implications for Property Valuation and Investment Decisions," Journal of Real Estate Research, American Real Estate Society, vol. 12(3), pages 347-368.
  3. Ferson, Wayne E & Harvey, Campbell R, 1991. "The Variation of Economic Risk Premiums," Journal of Political Economy, University of Chicago Press, vol. 99(2), pages 385-415, April.
  4. Neil Crosby & Anthony Lavers & John Murdoch, 1998. "Property valuation variation and the 'margin of error' in the UK," Journal of Property Research, Taylor & Francis Journals, vol. 15(4), pages 305-330, January.
  5. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
  6. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "A Theory of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 53(2), pages 385-407, March.
  7. Larry E. Wofford, 1978. "A Simulation Approach to the Appraisal of Income Producing Real Estate," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 6(4), pages 370-394.
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Cited by:
  1. Goran Karanovic & Bisera Gjosevska, 2012. "Analysis of Risk and Uncertainty Using Monte Carlo Simulation and its Influence on Project Realization," Annals - Economic and Administrative Series -, Faculty of Business and Administration, University of Bucharest, vol. 6(1), pages 145-162, December.
  2. Baroni, Michel & Barthélémy, Fabrice & Mokrane, Mahdi, 2007. "Optimal Holding Period for a Real Estate Portfolio," ESSEC Working Papers DR 07008, ESSEC Research Center, ESSEC Business School.
  3. Charles-Olivier Amédée-Manesme & Michel Baroni & Fabrice Barthélémy & Etienne Dupuy, 2011. "Combining Monte Carlo Simulations and Options to Manage the Risk of Real Estate Portfolios," Post-Print hal-00671067, HAL.
  4. Michele Leonardo Bianchi & Agostino Chiabrera, 2012. "Italian real estate investment funds: market structure and risk measurement," Questioni di Economia e Finanza (Occasional Papers) 120, Bank of Italy, Economic Research and International Relations Area.

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