A Pedagogical Tool to Assist in Teaching Real Estate Investment Risk Analysis

Abstract. This paper not absents a simple Excel model that provides measures of the standard deviation of forecasted internal rate of turn back (IRR) given traditional data inputs of that kind as annual cash flows, terminal values and equity. The type first calculates IRR using traditional discounted cash stream methods and then provides heuristic estimates of variability measured in terminuss of "high," "low" and "most likely" values. It also provides an actual measurement of risk in terminuss of mean and standard deviation and upper and lower quartiles, along with a graphical presentation of various risk parameters. The alone additional analysis required is an estimation of the variability of periodic cash runs and the terminal value of the investment.

Introduction

Real estate investment analysis, in the extremity seeks to answer three basic questions:

1 in what manner much to pay (equity usually or total purchase price sometimes)?

2 Given a particular purchase price, what will be the yield upon invested equity (IRR^sub equity^)?

3 by what mode risky is the project?

How plenteous to pay is usually asked in boundarys of how much equity to invest or, sometimes, in what way much to pay for the whole shoot forward This is usually estimated in bourns of the present value of either before tax cash roll on or after tax cash sweep along if equity is of interest and the not away value of net operating income (NOI) for the total project's value (sometimes analysis estimate required equity and then simply add debt)

The internal rate of turn back (IRR) on invested equity is usually measured by dint of using either before or after tax cash streams along with an estimate of equity and solving for IRR. Risk is usually defined as variance about a certain number of forecasted future value, most many times IRR or present value (PV) or, in a certain number of cases, net present value (NPV) Risk can also be associated with other variables of the like kind as rent, vacancy, operating charges or NOI. When a real estate analyst forecasts any coming time value (rent, vacancy, operating outlays cash flows, IRR, PV or NPV) and the subsequent time result is variable then there will be risk (variability) associated with that forecast. Sometimes a doom of risk, sometimes less, depending upon the situation. This paper nears a model that focuses upon estimating IRR and the risk associated with that return

For many years academicians and practitioners have discussed the use of Monte Carlo simulation as it may be applied to real estate analysis. commonly available techniques that actually disentangle a risk measure (variance or standard deviation) involve more [i]or[/i] less variant of simulation (e.g., @Risk (www.palisade.com), Crystal Ball?® (www.decisioneering.com) and other simulation programs]. All meanss of applying simulation to any reasonably "real-world' dataset require a seriously sophisticated analyst who is not single familiar with the subject data place but is also well trained in statistics and simulation. Simulation software of that kind as @Risk and Crystal Ball require extensive training in statistics along with a useful deal of information about the propos throw out and lots of market-oriented information. Assuming that a competently trained analyst is available, parameterizing a simulation design is extremely difficult and expensive. Not single must each variable's distribution impressed sign be estimated but mean and standard deviation/variances must be estimated for all variables along with a correlation matrix showing correlations between all variables.

This paper unravels a technique that can easily be mastered by means of any student to provide actual variance/standard deviation risk measures. It allows a scholar to examine risk given changes in a variety of input variables. Essentially, the technique takes the sum of two units most significant variables affecting the variability of an IRR forecast (annual cash runs and terminal value) and uses a two-dimensional matrix to examine the be deriveds of all likely combinations of these sum of two units variables as they change. The pattern can be extended to examine the interaction of three or more variables as necessary.

Use of a matrix approach facilitates the analysis of all possible variable combinations and allows the variance/standard deviation to be easily calculated. Various descriptive statistics (parametric and non-parametric) can also be calculated from this matrix data to help provide additional understanding of cast risk. While this paper is oriented toward teaching risk associated with IRR forecasting, the techniques and tools at handed can certainly be used by dint of analysts seeking to better their understanding of the risk associated with any forecast where periodic and terminal cash sweep alongs are involved.

Literature Review

A number of novel authors have developed models that analyze capital budgeting decisions. These originals allow the analyst to vary many of the input variables and to quantify the risk of the shoot forward The following discussion summarizes several of these authors' results

Most common real estate simulation work is predated through and largely based on, dissertation work done by means of Steven Phyrr in the 1960 upon a more current note, Taggart (1999) nears a useful capital budgeting analysis prototype that uses discounted cash pour (DCF) and allows the user to estimate all of the composings of the annual cash streams This model would provide of the first grade input data for the design presented in this paper. Mahoney and Kelliher (1999) introduce an interesting capital budgeting protoplast that incorporates uncertainty in the cash run estimates by utilizing Monte Carlo simulation. Their design while practical and useful, does introduce a higher horizontal of complexity to the capital budgeting decision. Winston (1998) unravels a model for multi-period capital budgeting using the Solver Tool in beat This linear programming model allows the analyst to sort [i]or[/i] part of to the other many capital budgeting projects and single out the best, subject to time and take away from constraints, with the objective of maximizing overall NPV Ragsdale (2001) also not absents a model that utilizes Solver to find the optimal combination of capital budgeting throw outs subject to capital constraints, while maximizing NPV The author exhibits a model that uses Crystal Ball and OptQuest (Monte Carlo simulation tools) to perform optimal cast selection, while considering risk factors, like as probability of success and minimum and maximum receiptss The solution is subject to an initial investment constraint, while maximizing NPV Benninga (2000) demonstrates the use of data tables in capital budgeting analysis. After calculating the point estimate for NPV a put of growth rates are assumed (growth of annual cash flows) and the NPV is calculated for each of these extension rates. The analyst can use the flows to assess the risk of the throw out based on the resulting range of NPV Mayes and Shank (2001) provide a [i]or[/i] part of to the other discussion of capital budgeting and the application of outrival models for the analysis. They also integrate risk-adjusted discount rates and Monte Carlo simulation to assess shoot forward risk. On a complementary note, Weaver and Michelson (2003) describe the use of a similar archetype to analyze NPV in a capital budgeting context