Precisely Wrong or Roughly Right?An Evaluation of Development Viability

Peter ByrnePat McAllisterPeter Wyatt

www.henley.reading.ac.uk/rep/fulltxt/0810.pdf

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Structure

a) Main Research questions

b) Background and context

c) Current practice

d) Uncertainty issues

e) Some hypothetically typical developments and their simulation outputs

f) Findings and conclusions

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Main research questions

Given intrinsic input uncertainty and volatility, what do:

• choice of model structure, and• level of input variable complexity

mean for the uncertainty of DVA output(s)?

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Background and context

• Financial modelling of development proposals has now become a matter of public policy

• DVAs are a central component of negotiations on planning contributions

• Developers and planning authorities must now demonstrate that proposals are financially viable

• Development appraisal methodology is increasingly being scrutinised and contested

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• DVAs are rule-based, deterministic models that attempt to simulate the expected input-output behaviour of a development project– Specifically regarding quantity and timing of future revenue and

expenditure• Like all models, they simplify – to varying degrees...

DVAs: current practice

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DVAs: current practice

• Well-known limitations of traditional residual approach linked to assumptions about– Timing of expenditure and receipts– Simplistic profit ‘mark-up’– Inclusion of finance costs– Lack of forecasts

• Cash flow models have ‘solved’ the timing and forecast issues only

• An alternative cash flow approach has been proposed – but requires a hurdle rate of return to be specified

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Uncertainty issues

• Model structure– Choice of model scale: extent and balance of disaggregation– Error propagation

• Inputs– Over-parameterisation vs. parsimony– Uncertain estimation and uncertain volatility– Most are stochastic rather than deterministic– Many are linked in a ratio-dependent manner– Some of these ratios are also stochastic

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Is there an optimally complex model?

Model Complexity

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Monte Carlo Simulation is usedto assess whether

• Choice of model structure produces significantly different output means and variance?

• Choice of model scale produces different output means and variances?

• Five models– Traditional residual model– Aggregated standard cash flow model– Aggregated alternative cash flow model– Disaggregated standard cash flow model– Disaggregated alternative cash flow model

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Key assumptions and inputs #1Inputs

Interest rate 8%Development period:

lead-in period 0.25building period 1.5

void period 0.25Rent 479,400 Rental grow th (% p.a.) 0.00%Forecast rent 479,400 Yield 6%Yield forecast 6.00%Developer's profit 20%Development costs 2,114,000 Cost inf lation (% p.a.) 0.00%Forecast costs 2,114,000

Calculations

PV 0.8573

DV (net of costs) 7,555,556 Interest over bldg period 129,344 Interest over void period 43,580

Output

Land value 3,437,390

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Results - #1Statistics Uncertainty Volatility Uncertainty Volatility Uncertainty Volatility

Mean 3,437,574 3,501,871 3,421,996 3,670,912 3,431,053 3,721,853Median 3,441,073 3,438,205 3,414,307 3,619,062 3,433,779 3,651,014Standard Deviation 577,425 810,685 585,694 904,511 590,509 1,136,220Skewness -0.0296 0.4051 0.0224 0.3536 0.0129 0.4236Kurtosis 3.02 3.39 2.96 3.15 2.9 3.16Coeff. of Variability 0.168 0.232 0.171 0.246 0.1721 0.3053Minimum 1,131,228 459,504 1,073,924 706,155 1,130,110 511,420Maximum 5,736,217 7,415,856 5,700,332 8,042,676 5,693,546 8,769,812

DistributionsInterest rate Normal Normal Lognormal Lognormal Lognormal LognormalDevelopment period:

lead-in period Normal Normal Lognormal Lognormal Lognormal Lognormalbuilding period Normal Normal BetaPERT BetaPERT BetaPERT BetaPERT Corr. With Dev. Costs 0.75

void period Normal Normal BetaPERT BetaPERT BetaPERT BetaPERT Corr. With Yield forecast 0.5Rent Normal Normal Normal Normal Normal Normal Corr. With Yield forecast -0.7Rental growth (% p.a.) Normal Triangular TriangularForecast rentYieldYield forecast Normal Lognormal LognormalDeveloper's profitDevelopment costs Normal Normal Normal Normal Normal NormalCost inflation (% p.a.) Normal Normal Normal

Model 1 Model 2 Model 3

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Key assumptions and inputs #2Variables

Areas: Values:Gross Internal Area (GIA) (f t2) 150,000 Estimated rent / ft2 £45.00Efficiency ratio (net/gross area) 85% All Risks Yield 4.49%Net Internal Area (NIA) (ft2) 127,500

Appraisal specific inputs:Construction costs: Developer's profit (% costs): 20.00%Site Preparation £0Building costs (£/ft2) £185 Finance:External w orks £0 Short term finance rate (annual) 8.00%Contingencies (% of all construction costs) 5.00% Short term finance rate (quarterly) 1.94%S106 £8,000,000

Time:Fees: Lead-in period (yrs) 0.25Professional fees: (% construction costs) 10.00% Building period (yrs) 1.50Letting Agent's Fee (% ERV) 10.00% Letting void (yrs) 0.25Letting Legal Fee (% ERV) 5.00% Total Development Period (yrs) 2.00Marketing & Promotion £100,000Sale Agent's Fee (% NDV) 0.75% Other Inputs:Sale Legal Fee £300,000 Debt Proportion 100%Investment Purchaser's Costs (% NDV) 5.75% Building cost inf lation (%pa) 5.800%Planning £150,000 Rental Grow th (%pa) 4.730%Building Regs £50,000Land acquisition costs (% site purchase price) 5.75%

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Key assumptions and inputs #2

Typical S106 variablesProvision of open spaceLandscapingGeneral environmental improvementsEcology, countryside management, etc.Temporary highw ay w orksPermanent highw ay w orksTraffic management / calmingParking provisionGreen transport / travel plansProvision and improvement of public rights of w ayCommunity artTow n centre managementPublic toiletsWaste and recycling facilitiesRegeneration initiativesPublic transport contribution

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Results #21 2 3 4

Conventional Cashflow Conventional Cashflow Conventional Cashflow Conventional CashflowMean £45,920,504 £53,504,669 £45,796,574 £53,341,425 £45,711,242 £53,196,765 £45,694,647 £53,179,017Median £45,110,494 £52,437,353 £45,103,577 £52,425,697 £44,929,722 £52,218,936 £44,825,633 £52,064,681Standard Deviation £7,803,124 £10,281,852 £11,144,647 £14,673,442 £11,393,279 £15,035,915 £11,465,718 £15,134,209Skewness 0.6223 0.6223 0.3446 0.3451 0.453 0.4522 0.4704 0.4681Kurtosis 3.83 3.83 3.18 3.19 3.57 3.57 3.49 3.49Coeff. of Variability 0.1699 0.1922 0.2434 0.2751 0.2492 0.2826 0.2509 0.2846Minimum £23,150,855 £23,502,049 £13,034,505 £10,219,759 £7,234,292 £2,308,248 £11,244,373 £7,383,355Maximum £108,483,270 £135,941,028 £101,567,585 £126,791,962 £109,752,032 £137,966,987 £109,574,176 £137,395,776

5 6 7Conventional Cashflow Conventional Cashflow Conventional Cashflow

Mean £45,682,013 £53,164,132 £45,555,312 £52,995,655 £45,780,073 £61,809,111 1 All Risks Yield

Median £44,948,872 £52,234,050 £44,838,125 £52,092,048 £45,015,017 £58,189,833 2 Estimated rent / ft2

Standard Deviation £11,338,803 £14,976,929 £11,365,594 £15,012,988 £11,521,513 £35,750,694 3 Building costs (£/ft2)

Skewness 0.372 0.3696 0.3815 0.3775 0.4388 0.6226 4 Short term finance rate (quarterly)Kurtosis 3.31 3.3 3.25 3.25 3.44 3.63 5 S106

Coeff. of Variability 0.2482 0.2817 0.2495 0.2833 0.2517 0.5784 6 Building cost inflation (%pa)Minimum £9,867,471 £5,527,143 £7,891,381 £3,291,030 £10,792,709 -£35,108,985 7 Rental Grow th (%pa)

Maximum £98,233,248 £122,655,464 £93,838,390 £116,543,747 £109,409,509 £244,648,367

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Results #2

Conventional Cashflow Conventional CashflowMean £45,092,151 £60,431,615 £45,261,918 £60,279,603Median £44,581,432 £57,067,313 £44,858,689 £56,899,922Standard Deviation £8,300,643 £34,463,725 £8,786,600 £34,187,908Skewness 0.3377 0.5282 0.3152 0.5454Kurtosis 3.31 3.37 3.31 3.51Coeff. of Variability 0.1841 0.5703 0.1941 0.5672Minimum £16,228,988 -£32,753,263 £14,070,151 -£37,972,561Maximum £83,009,129 £225,980,263 £91,736,854 £246,678,883

Disaggregated 20 variables Disaggregated 20 variablesTwo variables correlated

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Conclusions and Further Work• DVAs are now an important nexus in the UK planning system.

• Many of the dilemmas in development viability modelling are shared in other disciplines.

• Largely due to high levels of input uncertainty, it is a common finding that simple, aggregated models can display equifinality with complex, disaggregated models.

• We also find evidence of equifinality in the outputs of a simple, aggregated model relative to more complex, disaggregated models.

• However, we need further testing to model phased developments and development period uncertainty.

• We need to be more certain that our conclusions are robust and are not a function of subjective estimates of estimated variances, distributions and correlations.