Property development feasibility studies may appear robust on paper, but a single assumption shift—construction costs rising 10%, sale prices softening 5%, or interest rates climbing 1%—could transform a profitable project into a marginal or loss-making venture. Sensitivity analysis is the systematic process of testing how changes to key inputs affect development outcomes, and it’s typically considered essential for any serious feasibility assessment.
Australian development lenders generally require sensitivity analysis as part of loan applications, valuers are increasingly expected to include it under professional standards, and sophisticated developers use it to understand project risk before committing capital. Yet despite its importance, sensitivity analysis remains poorly understood by many in the industry—often reduced to a simple “what if construction costs go up 10%” calculation rather than the comprehensive risk assessment tool it could be.
This guide covers everything property developers may need to know about sensitivity analysis: from fundamental concepts through to advanced techniques like tornado diagrams and Monte Carlo simulation. You’ll learn the standard ranges professionals typically test, how to build sensitivity matrices in Excel, what lenders expect to see in finance applications, and how to present sensitivity analysis effectively to investors and joint venture partners.
What is sensitivity analysis and why does it matter for developers?
Sensitivity analysis is a technique for understanding how uncertainty in model inputs affects model outputs. In property development terms, it answers the question: “If my assumptions are wrong, how wrong could my project go?”
Every feasibility study relies on assumptions—estimated construction costs, projected sale prices, anticipated finance rates, expected development timelines. These assumptions are informed guesses based on available evidence, but they’re never certain. Sensitivity analysis systematically tests what happens when those guesses prove incorrect.
The fundamental problem with static feasibility studies
A static feasibility study presents a single scenario: one set of inputs producing one set of outputs. It might show a development margin of 20% and conclude the project is viable. But this single-point estimate tells you nothing about:
- How confident you should be in that 20% margin
- What happens if construction costs exceed the estimate
- Whether the project remains viable if the market softens
- How much buffer exists before the project becomes unviable
- Which assumptions matter most to project success
Consider a townhouse development with a base case showing $500,000 profit and 18% development margin. Without sensitivity analysis, you might proceed confidently. With sensitivity analysis, you might discover that a 7% construction cost increase combined with a 5% sale price reduction—both entirely plausible scenarios—would reduce profit to $50,000 and margin to 2%. The same project, viewed through a sensitivity lens, might warrant considerably more caution.
Three core outputs developers typically sensitise
While sensitivity analysis could theoretically test any output, property developers typically focus on three key metrics:
Development profit and margin: The absolute dollar profit and its relationship to total development costs or gross development value (GDV). This is often the primary metric lenders and investors examine.
Return on equity or return on cost: The profit expressed as a percentage of equity invested or total project costs. This measures capital efficiency and is particularly relevant for comparing alternative investment opportunities.
Residual land value: The maximum price that could theoretically be paid for a development site while still achieving target returns. This is critical for site acquisition decisions and is typically highly sensitive to input changes.
The RICS guidance on development property valuation notes that residual valuations are “very sensitive to changes in key inputs”—small percentage changes in construction costs or end values can produce disproportionately large changes in residual land value.
Standard sensitivity ranges: what professionals typically test
Industry practice has established relatively consistent ranges for sensitivity testing. While these may vary by project type, location, and market conditions, they provide useful benchmarks.
Construction costs
Construction cost sensitivity typically ranges from ±5% to ±15%, with ±10% being the most common standard test. The appropriate range may depend on several factors:
| Project Stage | Typical Range | Rationale |
|---|---|---|
| Early feasibility (no QS) | ±15% to ±20% | High uncertainty in cost estimates |
| Post-QS estimate | ±10% to ±15% | Moderate confidence in quantities |
| Fixed-price contract | ±5% | Limited to variations and provisional sums |
| Design & construct | ±5% to ±10% | Depending on novation risks |
Projects with unusual construction requirements, complex sites, or volatile material costs may warrant wider ranges. The Master Builders Australia publishes construction cost indices that can inform assumptions about cost escalation risk.
Sale prices and gross development value
Revenue sensitivity typically mirrors construction cost ranges at ±5% to ±15%. However, sale price assumptions often carry greater uncertainty than construction costs, particularly for:
- Off-the-plan apartment sales where prices won’t be tested for 18-24 months
- Emerging or gentrifying suburbs with limited comparable sales evidence
- Premium product positioning where price points are aspirational
- Markets showing signs of softening or volatility
Some practitioners apply asymmetric ranges—for example, testing -15% to +5%—reflecting the reality that downside risks often exceed upside potential in development.
Interest rates and finance costs
Interest rate sensitivity typically tests ±1% to ±3% movements. A 1% interest rate change might seem modest, but its impact compounds through the development period and can significantly affect total finance costs.
Example impact: On a $10 million facility drawn over 18 months with average utilisation of 60%, a 1% interest rate increase adds approximately $90,000 to finance costs. For a project with a $500,000 target profit, this represents an 18% profit reduction from a single percentage point rate movement.
The Reserve Bank of Australia publishes cash rate decisions and forward guidance that may inform interest rate assumptions.
Timeline and programme duration
Programme sensitivity typically tests delays of 3 to 12 months, depending on project complexity and approval risk. Timeline delays affect feasibility through:
- Extended holding costs on land acquisition debt
- Additional capitalised interest on development finance
- Delayed receipt of sale proceeds
- Potential market timing risks
- Increased professional fees and site costs
A common approach tests both 3-month and 6-month delays to understand the cost of programme slippage.
Summary of standard ranges
| Variable | Conservative | Standard | Stressed |
|---|---|---|---|
| Construction costs | ±5% | ±10% | ±15% |
| Sale prices/GDV | ±5% | ±10% | ±15% |
| Interest rates | ±0.5% | ±1% | ±2% |
| Programme duration | +3 months | +6 months | +12 months |
| Contingency | 5% | 7.5% | 10% |
The sensitivity matrix: testing two variables simultaneously
The most common sensitivity analysis format is the two-variable matrix, which shows how a key output (like development profit) changes when two inputs vary simultaneously. This approach reveals the combined impact of multiple assumptions shifting together—often a more realistic scenario than single-variable changes.
Anatomy of a sensitivity matrix
A typical sensitivity matrix places one variable along the horizontal axis (columns) and another along the vertical axis (rows). The intersection cells show the output value for each combination of inputs.
Example: Development margin sensitivity to GDV and construction costs
| GDV -10% | GDV -5% | GDV Base | GDV +5% | GDV +10% | |
|---|---|---|---|---|---|
| Costs +10% | 5.2% | 9.8% | 14.1% | 18.2% | 22.1% |
| Costs +5% | 8.4% | 12.8% | 17.0% | 21.0% | 24.8% |
| Costs Base | 11.4% | 15.7% | 19.8% | 23.6% | 27.3% |
| Costs -5% | 14.3% | 18.5% | 22.4% | 26.1% | 29.7% |
| Costs -10% | 17.1% | 21.1% | 24.9% | 28.5% | 31.9% |
This matrix immediately reveals several insights:
- The base case (centre cell) shows 19.8% margin
- A simultaneous 5% cost increase and 5% GDV decrease produces 12.8% margin
- The project remains above 15% margin threshold unless costs rise or GDV falls
- Downside scenarios (top-left quadrant) require careful consideration
Choosing variables for the matrix
The two variables selected for a sensitivity matrix should typically be:
- Materially significant: Large enough cost or revenue components to meaningfully affect outcomes
- Uncertain: Subject to genuine estimation risk rather than contractually fixed
- Independent or correlated in a known way: Understanding whether variables move together helps interpret results
Common variable pairings include:
| Pairing | When to use |
|---|---|
| GDV × Construction costs | Standard “revenue vs cost” test for most developments |
| Sale price × Sales rate | When sales timing significantly affects interest costs |
| Construction cost × Programme | When delays primarily affect construction and holding costs |
| Interest rate × Programme | For highly geared projects sensitive to finance costs |
| Land cost × Development margin | For site acquisition decisions and residual value analysis |
Building a sensitivity matrix in Excel
Excel’s Data Table function provides the most efficient method for creating sensitivity matrices. The process involves:
Step 1: Set up your feasibility model
Ensure your model has clearly identified input cells (assumptions) and output cells (results). The input cells you want to sensitise must be single cells that the model references.
Step 2: Create the matrix structure
- In an empty area, create your row headers (e.g., cost variations: -10%, -5%, Base, +5%, +10%)
- Create your column headers (e.g., GDV variations: -10%, -5%, Base, +5%, +10%)
- In the top-left corner cell (intersection of headers), enter a formula linking to your output cell (e.g., =Development_Margin)
Step 3: Set up the data table
- Select the entire matrix including the formula cell and all row/column headers
- Navigate to Data → What-If Analysis → Data Table
- For “Row input cell”, select the cell containing your GDV assumption
- For “Column input cell”, select the cell containing your construction cost assumption
- Click OK
Excel will automatically calculate the output for every combination of row and column values.
Step 4: Apply conditional formatting
Visual formatting dramatically improves matrix readability:
- Select the data cells (excluding headers)
- Apply a three-colour scale: green for high values, yellow for middle values, red for low values
- Alternatively, use icon sets or colour thresholds based on target returns
Interpreting matrix results
When reviewing a sensitivity matrix, consider:
The diagonal: Moving from top-left to bottom-right typically shows improving outcomes (lower costs, higher revenues). The steepness of improvement indicates how sensitive the project is overall.
Threshold boundaries: Identify where the matrix crosses key thresholds (e.g., minimum acceptable margin, lender requirements). How much can assumptions deteriorate before crossing these boundaries?
Probability weighting: Not all scenarios are equally likely. The centre of the matrix (base case ± small variations) typically represents more probable outcomes than extreme corners.
Asymmetry: Are downside risks (top-left) proportionally larger than upside opportunities (bottom-right)? This asymmetry often indicates higher risk.
What lenders expect to see
Australian development lenders typically require sensitivity analysis as part of feasibility documentation for finance applications. Understanding lender expectations helps ensure your analysis meets their requirements.
Major bank requirements
While specific requirements vary by institution and change over time, major Australian banks generally expect:
Minimum return thresholds: Most major banks look for development margins of 15-20% or higher in base case scenarios. Sensitivity analysis should demonstrate the project maintains acceptable returns under reasonably stressed conditions.
Loan-to-cost ratios: Banks typically lend 65-75% of total development costs. Sensitivity analysis helps demonstrate that this ratio remains appropriate if costs increase or values decrease.
Interest rate buffers: Following APRA’s prudential guidance, lenders typically apply serviceability buffers of at least 3% above the loan rate. Your sensitivity analysis should demonstrate resilience to rate increases.
Pre-sale coverage: Major banks often require pre-sales covering 100% of committed debt. Sensitivity analysis may need to show the project remains viable even with some settlement failures or price reductions.
Non-bank lender expectations
Non-bank and private lenders may have different requirements:
- Lower or no pre-sale requirements, but higher interest rates
- Greater focus on exit strategies and worst-case scenarios
- More emphasis on developer track record and equity contribution
- Potentially wider sensitivity ranges reflecting higher risk tolerance
Presenting sensitivity analysis to lenders
Effective presentation typically includes:
Clear base case summary: Before showing sensitivity, establish the base case assumptions and returns clearly.
Standard format matrices: Use the conventional format lenders expect—typically GDV versus construction costs as the primary matrix.
Threshold highlighting: Clearly identify where results cross minimum acceptable thresholds.
Scenario narratives: Supplement matrices with written descriptions of key scenarios (base, downside, worst case) and their implications.
Assumption justification: Explain why your base case assumptions are reasonable and what evidence supports them.
Advanced sensitivity techniques
While two-variable matrices are the industry standard, more sophisticated techniques can provide deeper insights for complex projects or detailed risk assessment.
Tornado diagrams: ranking variable importance
A tornado diagram ranks variables by their impact on a chosen output, showing which assumptions matter most. This helps prioritise where to focus due diligence efforts and risk mitigation.
How tornado diagrams work:
- Select a single output metric (e.g., development profit)
- Test each input variable individually across its sensitivity range
- Calculate the output range for each variable
- Sort variables by the width of their output range
- Display as horizontal bars, with widest (most impactful) at top
Example tornado diagram results for a townhouse development:
| Variable | Low Value | High Value | Output Range |
|---|---|---|---|
| Sale prices | -15% | +10% | $320,000 |
| Construction costs | +15% | -10% | $280,000 |
| Land cost | +10% | -10% | $180,000 |
| Interest rate | +2% | -1% | $95,000 |
| Programme | +6 months | -2 months | $75,000 |
| Professional fees | +20% | -10% | $45,000 |
This ranking reveals that sale prices and construction costs dominate project risk, while professional fees—despite often receiving significant negotiation attention—have relatively minor impact.
Building a tornado diagram in Excel:
Excel doesn’t have a built-in tornado chart, but you can create one manually:
- Calculate the output for low and high values of each variable
- Calculate the midpoint and deviation from midpoint for each
- Create a stacked bar chart with negative deviations extending left and positive right
- Format bars with consistent colours and sort by total range
- Add a vertical line at the base case value
Alternatively, commercial add-ins like Sensit or @RISK automate this process.
Scenario analysis: combining multiple variables
While sensitivity analysis typically varies one or two inputs at a time, scenario analysis examines coherent combinations of multiple input changes representing plausible future states.
Standard development scenarios:
Base case: Current best estimates for all inputs, representing the most likely outcome based on available evidence.
Downside case: Moderately adverse conditions—perhaps construction costs 7% higher, sale prices 5% lower, programme 3 months longer, and interest rates 0.5% higher. These changes should be plausible rather than extreme.
Worst case: Severely adverse but still credible conditions. This might combine 15% cost overruns with 10% price reductions and 6-month delays. The worst case tests project resilience under stress.
Upside case: Favourable conditions that could reasonably occur—costs under budget, prices exceeding expectations, faster sales. This shows potential upside but should be used cautiously to avoid optimism bias.
Using Excel’s Scenario Manager:
Excel’s built-in Scenario Manager handles multi-variable scenarios efficiently:
- Navigate to Data → What-If Analysis → Scenario Manager
- Click “Add” to create each scenario
- Define which cells change and their values for each scenario
- Use “Summary” to generate a comparison report showing all scenarios side-by-side
Scenario Manager supports up to 32 changing cells per scenario, far exceeding data table limitations.
Monte Carlo simulation: probability distributions
For sophisticated risk assessment, Monte Carlo simulation replaces single-point estimates with probability distributions, then runs thousands of iterations to generate a distribution of possible outcomes.
Conceptual approach:
Instead of assuming construction costs will be exactly $2,500 per square metre, Monte Carlo treats costs as a distribution—perhaps normally distributed with a mean of $2,500 and standard deviation of $200. Each simulation iteration randomly samples from this distribution, and from distributions for all other uncertain variables, calculating the resulting output.
After 1,000+ iterations, you have a distribution of possible development profits showing:
- The expected (average) outcome
- The probability of achieving target returns
- The range of possible outcomes
- The likelihood of loss or below-threshold returns
Implementing Monte Carlo in Excel without add-ins:
Basic Monte Carlo simulation is possible using native Excel functions:
-
Replace fixed assumptions with formulas generating random values:
=NORMINV(RAND(), mean, std_dev)for normally distributed variables=RAND()*(max-min)+minfor uniformly distributed variables
-
Set up a data table with a single column:
- Column header links to your output cell
- Row numbers 1 to 1000 (or more)
- Use Data Table with column input cell pointing to any empty cell
-
This generates 1000 random outcomes you can analyse:
=AVERAGE()for expected value=STDEV()for standard deviation=PERCENTILE()for confidence intervals=COUNTIF()for probability of exceeding thresholds
Interpreting Monte Carlo results:
Monte Carlo output typically includes:
- P50 (median): The outcome exceeded 50% of the time—a reasonable central estimate
- P90: The outcome exceeded 90% of the time—a conservative downside estimate
- P10: The outcome exceeded only 10% of the time—an optimistic upside estimate
- Probability of loss: Percentage of iterations producing negative profit
- Probability of achieving target: Percentage meeting minimum acceptable returns
For development finance, lenders may be interested in the probability of achieving their required coverage ratios or return thresholds.
Practical application: sensitivity analysis workflow
Integrating sensitivity analysis into your development assessment process typically involves several stages.
During site acquisition
At the acquisition stage, sensitivity analysis helps determine appropriate offer prices and assess whether a site offers adequate risk-adjusted returns.
Key applications:
- Residual land value sensitivity: Test how maximum supportable land price changes with GDV and cost assumptions
- Threshold analysis: Determine what cost or price movements would make the site unviable at the asking price
- Comparison with competition: Understand how your assumptions compare with what competitors might assume
Example: You’re assessing a site with an asking price of $2.5 million. Your base case residual land value calculation shows $2.7 million—apparently $200,000 of upside. But sensitivity analysis reveals that a 5% GDV reduction alone drops residual value to $2.3 million, below the asking price. This suggests the apparent margin of safety is thin.
During design development
As designs progress and cost estimates firm up, sensitivity analysis helps evaluate design decisions and their risk implications.
Key applications:
- Value engineering assessment: Test whether cost savings justify potential value impacts
- Yield optimisation: Understand the return impact of adding or removing units
- Specification decisions: Evaluate premium versus standard finishes considering price and cost impacts
During finance application
Lenders expect comprehensive sensitivity analysis demonstrating project resilience.
Typical requirements:
- Two-variable matrices showing margin or profit against cost and revenue changes
- Scenario analysis presenting base, downside, and worst cases
- Commentary explaining key risks and mitigations
- Evidence supporting base case assumptions
During sales and construction
Even after project commitment, sensitivity analysis remains valuable for monitoring and decision-making.
Key applications:
- Sales pace monitoring: Test implications of faster or slower than expected sales
- Variation assessment: Evaluate cost impact of construction variations
- Market movement response: Assess whether price adjustments are warranted given market changes
Common mistakes in sensitivity analysis
Several pitfalls commonly undermine the value of sensitivity analysis.
Optimistic base cases
If your base case already represents optimistic assumptions, sensitivity analysis around that base provides false comfort. The downside scenarios remain too optimistic, and the analysis fails to reveal true project risk.
Solution: Ensure base case assumptions represent realistic, supportable estimates—not targets or aspirations. Document the evidence supporting each assumption.
Testing irrelevant variables
Sensitising variables that are contractually fixed or represent minimal cost components wastes effort and can distract from material risks.
Solution: Focus sensitivity analysis on genuinely uncertain variables that materially affect outcomes. A tornado diagram can help identify which variables warrant detailed analysis.
Ignoring correlations
In reality, variables often move together. Construction costs and sale prices may both increase in an inflationary environment. Treating variables as independent can understate or overstate risk.
Solution: Consider whether your variables are correlated and, if so, test scenarios reflecting realistic combinations rather than purely independent variations.
Static analysis over time
A sensitivity analysis conducted at project inception may become stale as market conditions change and uncertainties resolve.
Solution: Update sensitivity analysis at key project milestones—after design finalisation, contract execution, pre-sales achievement, and during construction.
Presentation without interpretation
A sensitivity matrix without context is just numbers. Decision-makers need to understand what the analysis means for the project.
Solution: Always accompany sensitivity analysis with written interpretation: What are the key insights? What does this mean for risk? What actions might be warranted?
Australian regulatory and professional context
APRA and banking regulation
The Australian Prudential Regulation Authority (APRA) sets standards for authorised deposit-taking institutions (ADIs) that indirectly affect development finance.
APRA’s Prudential Standard APS 220 requires ADIs to have appropriate credit risk management frameworks, including stress testing capabilities. While not prescribing specific sensitivity analysis requirements for development loans, the standard establishes expectations for risk assessment that flow through to lending practices.
The prudential practice guide APG 223 on residential mortgage lending includes guidance on valuation practices and risk assessment that informs how banks approach development lending.
International Valuation Standards
The International Valuation Standards Council (IVSC) publishes standards adopted or referenced by valuers globally, including through the Australian Property Institute.
IVS 410 (Development Property) notes that valuers should “highlight the potentially disproportionate effect of possible changes in either the construction costs or end value on the profitability of the project.” This explicitly recognises the importance of sensitivity analysis in development valuations.
RICS professional standards
The Royal Institution of Chartered Surveyors (RICS) publishes detailed guidance on development valuations and viability assessments.
The RICS Professional Standard on Financial Viability in Planning (primarily UK-focused but influential in Australia) states that “all FVAs and subsequent reviews must provide a sensitivity analysis of the results and an accompanying explanation and interpretation of respective calculations on viability.”
The RICS guidance note on Valuation of Development Property recommends that “the simplest form of risk analysis is sensitivity analysis, which should be used to evaluate how changes to individual inputs might affect the valuation.”
Australian Property Institute
The Australian Property Institute (API) represents valuers and property professionals in Australia. API members are expected to follow International Valuation Standards, which include guidance on sensitivity analysis for development valuations.
Worked example: townhouse development sensitivity analysis
To illustrate practical application, consider a hypothetical eight-townhouse development in metropolitan Melbourne.
Base case assumptions
| Input | Base Case Value |
|---|---|
| Land cost | $2,000,000 |
| Construction cost | $3,200,000 ($400,000/townhouse) |
| Professional fees | $320,000 (10% of construction) |
| Statutory costs | $180,000 |
| Contingency | $240,000 (7.5% of construction) |
| Finance costs | $380,000 |
| Selling costs | $240,000 (3% of GDV) |
| Total development cost | $6,560,000 |
| Gross development value | $8,000,000 ($1,000,000/townhouse) |
| Development profit | $1,440,000 |
| Development margin | 21.9% |
Two-variable sensitivity matrix
Testing development margin against GDV (±10%) and construction costs (±10%):
| Dev. Margin | GDV $7.2m (-10%) | GDV $7.6m (-5%) | GDV $8.0m (Base) | GDV $8.4m (+5%) | GDV $8.8m (+10%) |
|---|---|---|---|---|---|
| Costs +10% | 6.5% | 11.4% | 16.1% | 20.5% | 24.7% |
| Costs +5% | 9.3% | 14.1% | 18.6% | 22.9% | 27.0% |
| Costs Base | 12.0% | 16.7% | 21.2% | 25.4% | 29.4% |
| Costs -5% | 14.7% | 19.3% | 23.7% | 27.8% | 31.7% |
| Costs -10% | 17.4% | 21.9% | 26.1% | 30.1% | 34.0% |
Interpretation
The base case margin of 21.2% (slight rounding difference from earlier calculation) appears healthy. However, the matrix reveals:
- Lender threshold (15%): Most cells exceed 15%, but the combination of 10% cost overrun with 5% GDV reduction (11.4%) falls below typical bank requirements
- Downside quadrant: The top-left quadrant shows margins ranging from 6.5% to 16.1%—a wide range of outcomes all representing significant return reduction
- Asymmetry: The worst case (6.5%) is further from base case than the best case (34.0%), reflecting higher downside risk
- Break-even proximity: Even the worst tested scenario remains profitable, suggesting reasonable buffer against loss
Scenario analysis
| Scenario | Description | Dev. Margin | Dev. Profit |
|---|---|---|---|
| Base case | Best estimates | 21.2% | $1,440,000 |
| Downside | Costs +7%, GDV -5%, Finance +$50k | 10.8% | $720,000 |
| Worst case | Costs +15%, GDV -10%, +6 month delay | 1.2% | $85,000 |
| Upside | Costs -5%, GDV +5% | 27.8% | $1,950,000 |
The downside scenario—plausible without being extreme—halves the profit. The worst case, while unlikely, would effectively eliminate returns. This suggests the project has meaningful but not excessive risk.
Tornado diagram insights
Ranking variables by impact on development profit:
- GDV (±10%): $800,000 range
- Construction costs (±10%): $352,000 range
- Land cost (±10%): $200,000 range
- Finance costs (±25%): $95,000 range
- Professional fees (±20%): $64,000 range
- Selling costs (±1% rate): $80,000 range
This confirms that GDV dominates project risk, followed by construction costs and land cost. Finance costs and professional fees, while worthy of management attention, have proportionally smaller impact.
Key takeaways for property developers
Sensitivity analysis is not merely a compliance exercise for finance applications—it’s a fundamental tool for understanding and managing development risk.
Start with realistic base cases: Sensitivity analysis around optimistic assumptions provides false comfort. Ensure your base case represents supportable estimates.
Focus on material uncertainties: Use tornado diagrams or simple ranking to identify which variables warrant detailed sensitivity testing. Don’t waste effort on contractually fixed or immaterial inputs.
Test correlated scenarios: Real-world risks often involve multiple variables moving together. Scenario analysis captures these combinations better than independent variable testing.
Update throughout the project: Sensitivity analysis isn’t a one-time exercise. Update your analysis as uncertainties resolve and new information emerges.
Present insights, not just numbers: Decision-makers need to understand what sensitivity analysis means for the project. Always provide interpretation alongside the technical analysis.
Match technique to purpose: Simple two-variable matrices suffice for most purposes. Reserve advanced techniques like Monte Carlo for complex projects or when sophisticated stakeholders require probabilistic outputs.
Document your assumptions: The value of sensitivity analysis depends on the quality of underlying assumptions. Document the basis for each assumption and the rationale for sensitivity ranges tested.
Understanding sensitivity analysis enables property developers to make more informed decisions, present more compelling finance applications, and ultimately manage development risk more effectively. In an industry where margins can evaporate quickly under adverse conditions, this understanding may prove invaluable.
Frequently asked questions
What is sensitivity analysis in property development?
Sensitivity analysis in property development is a technique for testing how changes to key assumptions—such as construction costs, sale prices, interest rates, and project timelines—affect development outcomes like profit, margin, and residual land value. It helps developers understand project risk by showing what happens when their assumptions prove incorrect.
What are the standard sensitivity ranges for development feasibility?
Industry practice typically tests construction costs and sale prices at ±5% to ±15%, with ±10% being most common. Interest rates are usually tested at ±1% to ±2%, while programme delays are commonly tested at 3 to 6 months. The appropriate range depends on project stage, complexity, and market conditions.
Do Australian banks require sensitivity analysis for development loans?
Yes, major Australian banks typically require sensitivity analysis as part of development finance applications. They generally expect to see that projects maintain acceptable returns (often 15-20% minimum margin) under reasonably stressed conditions, and that loan-to-cost ratios remain appropriate if costs increase or values decrease.
What’s the difference between sensitivity analysis and scenario analysis?
Sensitivity analysis typically varies one or two inputs at a time to understand their individual or combined impact on outputs. Scenario analysis examines coherent combinations of multiple input changes representing plausible future states—such as a “downside case” combining moderately adverse cost, revenue, and timing assumptions.
How do I create a sensitivity matrix in Excel?
Use Excel’s Data Table function. Set up a matrix with one variable’s values across columns and another’s down rows. In the corner cell, enter a formula linking to your output. Select the entire matrix, go to Data → What-If Analysis → Data Table, and specify which cells contain your input assumptions. Excel will calculate outputs for every combination.
What is a tornado diagram and when should I use it?
A tornado diagram ranks input variables by their impact on a chosen output, displayed as horizontal bars sorted from widest (most impactful) to narrowest. Use tornado diagrams when you need to identify which assumptions matter most to project outcomes and where to focus due diligence or risk mitigation efforts.
What is Monte Carlo simulation in property development?
Monte Carlo simulation is an advanced technique that replaces single-point estimates with probability distributions and runs thousands of iterations to generate a distribution of possible outcomes. It shows the probability of achieving target returns, the range of possible outcomes, and the likelihood of loss—providing richer risk information than standard sensitivity analysis.
Which variables should I include in sensitivity analysis?
Focus on variables that are both materially significant (large enough to meaningfully affect outcomes) and genuinely uncertain (subject to estimation risk). Typically, this includes construction costs, sale prices or GDV, interest rates, and project timelines. Variables that are contractually fixed or represent minor cost components generally warrant less attention.
How often should sensitivity analysis be updated?
Sensitivity analysis should be updated at key project milestones: after design finalisation, when construction contracts are executed, upon achieving pre-sales targets, and during construction if significant changes occur. Market conditions and resolved uncertainties may also warrant updates.
What are common mistakes in development sensitivity analysis?
Common mistakes include using optimistic rather than realistic base cases, testing irrelevant or immaterial variables, ignoring correlations between variables, failing to update analysis as projects progress, and presenting numbers without interpretation. These mistakes can undermine the value of sensitivity analysis for decision-making.