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Building the Cash Flow Model: The Information Needs

Building the Cash Flow Model: The Information Needs. Patrick McAllister. Remember the key questions. How much do we expect to receive? When? How much do we expect to pay out? When? What is the target rate of return? What is the expected holding period?. Garbage In-Garbage Out!.

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Building the Cash Flow Model: The Information Needs

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  1. Building the Cash Flow Model:The Information Needs Patrick McAllister

  2. Remember the key questions • How much do we expect to receive? When? • How much do we expect to pay out? When? • What is the target rate of return? • What is the expected holding period?

  3. Garbage In-Garbage Out!

  4. Information flows from? Do you trust them?

  5. Buyer Client Researcher Fund Manager Agent Asset manager • Target rate of return • Lot size • Covenant strength • Lease structure • Geographical/sector • preferences • Target rate of return • Exit yield - market • Rental growth - market • Market rent • Depreciation - market • Voids • Target rate of return • Exit yield - building • Rental growth - building • Market rent • Depreciation - building • Capex • Refurbishment • Redevelopment • Voids, bad debts and • management • Target rate of return • Exit yield - building • Rental growth – building • and market • Market rent • Depreciation – building • and market • Voids, bad debts and • Management • Capex • Refurbishment • Redevelopment • Voids, bad debts and • management • Capex • Refurbishment • Redevelopment

  6. Forecasting – who, what, where and when? • Rental growth, yield and total return for a.. • Five year horizon • Mainly quarterly • Building? City? District? Prime? Location? • PMA, Experian, IPD, major consultancies and investors

  7. Forecasting – why? • Improved performance – picking winners • Stock selection - acquisition/disposal decisions • Tactical asset allocation – sector/regional ‘plays’ • Timing the market • Analysing the market

  8. Forecasting – how? • For rents • Mainly theory-driven econometric models • Sometimes with time series extrapolation – atheoretical • Often with a judgement call • For capitalisation rates/yields • Models often similar to above • Also use fundamental DCF pricing model. • Lack confidence in ability to forecast capitalization rates relative to rents

  9. Forecasting – how? • Econometric models - multiple linear regression • Measure the relationship between dependent and independent variables • Use historic data to identify the variables that significantly ‘explain’ rental growth. • Measure how sensitive rent is to a change in a variable – interest rates, GDP growth, inflation, supply, consumer expenditure, employment etc

  10. Forecasting – how? • Econometric models - multiple linear regression • Crucial issue is model specification. • R-squared = • What is important here?

  11. Forecasting – how? • Econometric models - multiple linear regression • Crucial inputs are coefficients. • R-squared = 0.83 • What can go wrong?

  12. Uncertainty and Disagreement • Uncertainty is intrinsic to forecasting inputs and outputs. “all econometric models are mis-specified, and all economies have been subject to unanticipated shifts”. (Hendry and Clement, 2003, 303) • Disagreement is inevitable given heterogeneity in prior information “forecasters have both different types and different amounts of information to form their beliefs”. (Linden, 2003, 5)

  13. Consensus performance

  14. Consensus performance

  15. What follows? • Forecasts are affected by numerous sources of inherent and preventable uncertainty. • This feeds through directly to uncertainty about the cash flows in cash flow appraisals • Forecast uncertainty = model uncertainty • Marginal ‘decisions’ need to be rigorously reviewed. • Forecasting output should be treated as a starting point and not an endpoint?

  16. Key questions? • How reliable are IV estimates? • Does forecast uncertainty create too much uncertainty in the IV? • Are IV estimates adding any value to buying or selling decisions?

  17. Arguments Against IV • Do the values estimated by calculations of IV have too much intrinsic error to provide a reliable basis for making pricing decisions? • Because of this uncertainty, simple approaches may be just as reliable as complex approaches • This will not cancel out - investors will underbid for some assets and not acquire them. They will overpay for other assets and perform poorly.

  18. Arguments For IV • With apologies to Churchill “IV is the worst possible approach to real estate pricing decisions except for all the others”. • What’s the alternative? What other approach do you suggest to estimate a bid price? Intuition? • IV estimate provides a framework for a critical evaluation of the future performance of the asset and the factors that will drive this performance. • It makes the process of decision-making, the assumptions used, the rationale for forecasts etc more transparent – and rigorous?

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