Improving the versatility of D.C.F. models by simple computer applications Dr. LI Ling Hin Associate Professor Dept. of Real Estate and Construction The University of Hong Kong

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Improving the versatility of D.C.F. models by
simple computer applicationsDr. LI Ling
HinAssociate ProfessorDept. of Real Estate and
ConstructionThe University of Hong Kong
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Useful references

• 1) Glenn Kautt and Fred Wieland Modeling the
  future The full monte, the Latin hypercube and
  other curiosities, Journal of Financial
  Planning Denver Dec 2001 Vol. 14, Issue 12,
  pp78-88
• 2) Craft, R. Kim Using spreadsheets to conduct
  Monte Carlo experiments for teaching introductory
  econometrics, Southern Economic Journal, Jan.
  2003Vol.69, Iss. 3  pg. 726, 10 pgs ,
• 3) Rouse, Paul, Constructing Monte Carlo
  Simulations on Lotus 1-2-3, Journal of
  Accounting Education, Spring 1993, Vol. 11, Issue
  1, p.113

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Introduction

• D.C.F. model of valuation provides mode sensible
  interpretation of real estate value, provided
  sensible assumptions are made.
• Since major variables applied in the D.C.F. model
  do vary to a certain extent according to
  different market circumstances, this may affect
  the validity of the model.

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• By incorporating the estimation of the
  likelihood of achieving a certain value for all
  the relevant variables by way of Monte Carlo
  simulation, the use of D.C.F. in property
  appraisal and analysis becomes more versatile.

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• This paper shows that average appraiser with a
  basic computer knowledge can provide a further
  option of appraising real estate investment by
  way of D.C.F. simulation model, with relatively
  little difficulty.
• The only limitation, however, is the quality
  control in the estimation of the input variables.

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Monte Carlo Simulation

• Simply put, where the probability distributions
  (and hence the cumulative probability) of most or
  even all of the variables are known, simulation
  techniques can be applied to analyze the expected
  outcome based on randomly drawn probability of
  these variables.

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• Hence a randomly drawn figure for each of the
  variables will be generated in each single
  simulation process.
• These random figures become the input variables
  to be used in the D.C.F. model for appraisal or
  analysis purposes.
• The end result of this appraisal or analysis
  becomes the first set of expected value.

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• When this simulation process is repeated to a
  certain times, such as one thousand or more
  times, the randomly drawn figures would be vary
  close to represent the probability of these
  figures actually appearing in the real world.
• With these one thousand or more simulated values,
  an average mean value can be obtained so that the
  final expected outcome can be estimated.

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Example - Investment Project

• Total retail floor space allowed 85,000 sq.ft.
• ground floor 30,000 sq.ft
• second floor 30,000 sq.ft
• third floor 25,000 sq.ft
• Total office space allowed 725,000 sq.ft.
• lower level (15 floors) 225,000 sq.ft
• middle level (20 floors) 300,000
  sq.ft
• high level (20 floors) 200,000 sq.ft
• Total car-parking spaces allowed 430 units
• Loan-to-value ratio of mortgage on land
  60
• Maximum mortgage loan term 10 years
• Land Price
  HK1,200 million

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Summary of Simulation Analysis

• Expected Net Profit HKm (before tax)
  2,179.4695
• Minimum value 1,178.7371
• Maximum value 3,975.3138
• Standard deviation 397.62
• Expected Overall net rate of return 87.631
• (ie. discounted net profit / discounted total
  cash outflows )
• Expected IRR (p.a.) 24.28
• Expected IRR (per quarter.) 5.90
• Minimum value (per quarter) 4.20
• Maximum value (per quarter) 7.81
• standard deviation 0.58