One of the more recent research endeavors to investigate the use of pro forma DCF analysis and
simulation was produced by Foster and Lee (2009). The authors reviewed multiple real estate
development projects in the New England area from 2001-2007 and compared ex post returns to
proposed ex ante returns. They found that realized ex post returns were approximately 12% lower than
predicted ex ante returns. To test their hypothesis of whether the spread between ex post and ex ante
returns could be reduced using Monte Carlo simulation, the authors recreated ex ante pro forma returns
using MCS. They found that MCS was beneficial in uncovering sensitive input parameter assumptions,
such as the timing of cash flows and hard or soft construction estimates. However, this required the
developer to define input probability distributions for each non-deterministic parameter across a myriad
of market and submarket situations, which ultimately limited practical usage of the technique. Indeed,
the authors‘ survey of development participants found that 70% of the respondents had very limited
knowledge of simulation, and of that percentage, none had actually used it as an add-on to more
traditional DCF valuation analysis, such as sensitivity and scenario analysis.
One of the more recent research endeavors to investigate the use of pro forma DCF analysis andsimulation was produced by Foster and Lee (2009). The authors reviewed multiple real estatedevelopment projects in the New England area from 2001-2007 and compared ex post returns toproposed ex ante returns. They found that realized ex post returns were approximately 12% lower thanpredicted ex ante returns. To test their hypothesis of whether the spread between ex post and ex antereturns could be reduced using Monte Carlo simulation, the authors recreated ex ante pro forma returnsusing MCS. They found that MCS was beneficial in uncovering sensitive input parameter assumptions,such as the timing of cash flows and hard or soft construction estimates. However, this required thedeveloper to define input probability distributions for each non-deterministic parameter across a myriadof market and submarket situations, which ultimately limited practical usage of the technique. Indeed,the authors‘ survey of development participants found that 70% of the respondents had very limitedknowledge of simulation, and of that percentage, none had actually used it as an add-on to moretraditional DCF valuation analysis, such as sensitivity and scenario analysis.
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