Managerial Decision Support and Performance Analytics

Introduction

Financial modeling creates structured representations of investment cash flows, financing, valuation, operating performance, and risk. Yet a model does not make a decision. Managers must interpret outputs, challenge assumptions, compare alternatives, communicate implications, and authorize action. This chapter examines that translation process in real estate investment and asset management.

The central problem is one of managerial relevance. A model may report an internal rate of return (IRR), net present value (NPV), debt-service coverage ratio (DSCR), or variance against budget, but each metric is incomplete unless its definition, time horizon, benchmark, data quality, and decision purpose are understood. Performance analytics therefore combines quantitative measures with judgment, governance, and communication.

The chapter focuses on five recurring decisions: whether to acquire an asset, whether to hold or sell it, whether to refinance, how to allocate and control the operating budget, and how to improve asset-level performance.

Learning Objectives

By the end of this chapter, the reader should be able to:

1. Translate model outputs into acquisition, hold-sell, refinancing, budgeting, and asset-management decisions.
2. Design a balanced real estate KPI system that connects strategy, operations, finance, and risk.
3. Calculate and interpret variance measures, returns, coverage ratios, and value effects.
4. Construct decision dashboards that distinguish leading indicators from lagging outcomes.
5. Prepare an investment memorandum and communicate uncertainty to executives and investment committees.
6. Identify limitations, governance risks, ethical issues, and appropriate uses of AI-enabled analytics.

1. Decision Support as a Controlled Process

Managerial decision support is the disciplined conversion of data and model outputs into an authorized course of action. It is not merely reporting. A useful decision-support process contains six linked stages:

1. Decision framing: define the decision, authority, timing, alternatives, and constraints.
2. Evidence assembly: collect operating, market, financing, legal, and ESG information.
3. Model analysis: calculate cash flows, valuation, financing effects, sensitivities, and scenarios.
4. Interpretation: identify the principal drivers, uncertainties, and trade-offs.
5. Recommendation: select an action and state conditions, mitigants, and approval requirements.
6. Monitoring: assign KPIs, thresholds, owners, and review dates.

A technically correct model can still support a poor decision if the question is framed incorrectly. For example, asking “What is the IRR?” is less useful than asking “Does acquiring this asset at the proposed price create acceptable risk-adjusted value relative to available alternatives?”

A decision should be documented as a chain:

Objective → assumptions → model outputs → interpretation → action → monitoring trigger

This chain creates accountability and allows subsequent users to distinguish an original assumption from a later operating fact.

2. Financial Outputs and Their Managerial Meaning

2.1 Net present value

NPV measures the value created relative to a required return. For periodic cash flows:

where I₀ is the initial investment, CFₜ is the net cash flow in period t, r is the required periodic discount rate, and n is the number of periods. Cash flows and the discount rate must use the same currency basis, timing convention, and period length. NPV is applicable when the discount rate reasonably represents the opportunity cost and risk of the cash flows.

A positive NPV indicates value creation relative to the specified discount rate, not certainty of profit. It may be positive because of optimistic rent growth, an aggressive exit value, or underestimated capital expenditure. Management should therefore inspect the drivers rather than accept the headline number.

2.2 Internal rate of return

IRR is the discount rate that makes NPV equal to zero:

IRR is useful for comparing projects with similar scale, timing, and risk, but it can mislead when cash flows change sign more than once, when projects differ substantially in size, or when reinvestment assumptions are unrealistic. Modified IRR or NPV should supplement it. Equity IRR and unlevered IRR must never be compared without recognizing their different cash-flow bases.

2.3 Operating and valuation indicators

Net operating income (NOI) is generally property revenue less property-level operating expenses, before interest, income taxes, depreciation, and owner-level capital structure effects. The capitalization rate is:

The numerator must be clearly defined as current or forward NOI, and the denominator must be measured on a compatible basis. A cap rate is not a complete return measure: it excludes future growth, capital expenditure, financing, taxes, and sale proceeds.

Cash-on-cash return is commonly defined as:

Definitions vary, especially regarding whether invested equity includes acquisition costs, reserves, and subsequent contributions. Every investment committee report should state the convention used.

2.4 Debt-service coverage ratio

DSCR assesses the ability of property cash flow to cover scheduled debt service:

The numerator must match the lender’s definition. If NOI is used, it is not automatically equivalent to cash available for debt service because reserves, recurring capital expenditure, and other deductions may apply. A DSCR of 1.25× means the defined cash flow is 1.25 times scheduled debt service; it does not mean that 25% of revenue is available as surplus.

2.5 Leverage and refinancing effects

Loan-to-value ratio is:

Leverage can increase equity returns when asset returns exceed the effective cost of debt, but it also increases default, refinancing, interest-rate, and covenant risk. Model outputs should show both levered and unlevered results, debt maturity, amortization, interest-rate exposure, and downside coverage.

3. KPI Architecture for Real Estate Management

A KPI system should translate strategic intent into measurable outcomes. A useful architecture combines five perspectives:

The system should include both lagging indicators and leading indicators. NOI is a lagging outcome. Leasing inquiries, lease expirations, tenant complaints, maintenance backlog, and delinquency trends may signal future NOI deterioration.

3.1 KPI design principles

A strong KPI has:

• a precise definition and formula;
• a stated frequency and data source;
• an owner who can influence the result;
• a benchmark, target, or tolerance range;
• a documented treatment of exceptional items;
• a clear escalation rule.

For example, “occupancy” may mean physical occupancy, economic occupancy, leased occupancy, or occupied area at a particular date. These measures are not interchangeable. Economic occupancy may account for concessions and bad debt, while physical occupancy simply measures occupied space.

Targets must also be economically coherent. Maximizing occupancy can reduce value if it requires excessive concessions or locks the asset into below-market rents. A balanced scorecard prevents a single KPI from dominating the decision.

3.2 KPI hierarchy

KPIs should operate at three levels:

1. Strategic: portfolio return, risk-adjusted value, capital allocation, and ESG objectives.
2. Tactical: asset NOI, leasing pipeline, refinancing readiness, and capital-project delivery.
3. Operational: rent collection, work-order completion, energy use, complaints, and vacancy days.

The hierarchy prevents executives from being overwhelmed by operational detail while preserving the ability to trace a strategic result to its operational causes.

4. Variance Analysis and Performance Attribution

Variance analysis compares actual results with an appropriate benchmark. The benchmark may be budget, prior year, underwriting case, market index, or a revised forecast. A variance without a benchmark and causal explanation is only a numerical difference.

For a quantity or financial amount:

The absolute value in the denominator avoids reversing the sign merely because a budget is negative. Percentage variance is inappropriate when the budget is zero or very small; in such cases, use an absolute amount or a rate-based measure.

For revenue, a useful decomposition is:

Thus, revenue variance can be investigated through rate, volume, occupancy, mix, concessions, and timing effects. For operating expenses, analysts should separate controllable from uncontrollable costs and distinguish price effects from usage effects.

Worked example: monthly asset variance

An office asset budgeted monthly rental revenue of $500,000 from 50,000 occupied square metres at an average effective rent of $10 per square metre. Actual occupied area was 47,500 square metres and effective rent was $10.20 per square metre.

Budget revenue:

50,000 × $10 = $500,000

Actual revenue:

47,500 × $10.20 = $484,500

Absolute revenue variance:

$484,500 − $500,000 = −$15,500

Percentage variance:

−$15,500 ÷ $500,000 × 100% = −3.1%

A price effect, holding budget volume constant, is:

(10.20 − 10.00) × 50,000 = $10,000

A volume effect, using the budget rent, is:

(47,500 − 50,000) × $10.00 = −$25,000

The combined effect is $10,000 − $25,000 = −$15,000. The remaining $500 difference arises from the interaction of changed price and changed volume. The managerial conclusion is not simply that revenue missed budget; it is that improved rent partly offset an occupancy shortfall. Leasing action should therefore focus on vacancy and retention rather than indiscriminate discounting.

Forecast variance and root-cause discipline

Monthly variance reports should distinguish:

• timing variance, where an event occurs later or earlier than expected;
• permanent variance, where the underlying economics have changed;
• assumption variance, caused by an incorrect model input;
• execution variance, caused by operational performance;
• scope variance, caused by an approved change in activity.

Root-cause analysis should result in an action, owner, and due date. A variance commentary such as “repairs above budget” is weak. “Emergency HVAC replacement increased repairs by $42,000; useful life and insurance recovery are being reviewed; facilities manager to report by 15 June” is decision-useful.

5. Dashboards and Management Information

A dashboard is an interface for prioritization, not a storage location for every available metric. It should answer three questions: What happened? Why did it happen? What requires a decision now?

A practical dashboard contains:

• headline financial performance;
• operating and leasing indicators;
• financing and covenant measures;
• forecast versus approved plan;
• risk and compliance alerts;
• a concise action register.

Traffic-light indicators are useful only when thresholds are explicit. For example, green may mean DSCR above a defined covenant buffer, amber may indicate a narrowing buffer, and red may indicate breach or probable breach. Thresholds should be based on contractual obligations, risk appetite, and decision time—not arbitrary colors.

Dashboard design should follow a “drill-down” structure. The executive page shows material exceptions; the asset page shows drivers; the transaction or work-order page provides evidence. Data lineage, timestamp, responsible owner, and definition should be available for every important KPI.

Common dashboard failures include mixing actual and forecast values without labels, changing definitions over time, using visually precise but statistically unreliable numbers, and showing too many indicators. A dashboard should not conceal uncertainty behind polished graphics.

6. Decision Applications

6.1 Acquisition decisions

Acquisition analysis should combine price, expected cash flows, financing, market evidence, operational requirements, and downside risk. The investment committee should review:

• entry yield and comparable transactions;
• stabilized and near-term NOI;
• leasing and rollover exposure;
• capital expenditure and deferred maintenance;
• financing terms and refinancing risk;
• base, downside, and upside cases;
• exit assumptions and holding-period sensitivity;
• legal, tax, environmental, and ESG constraints.

The key question is whether value is created after allowing for realistic execution costs and risk. A high modeled IRR driven by a short holding period and aggressive exit price deserves more scrutiny than a lower but resilient return supported by contractual income.

6.2 Hold-sell decisions

The relevant comparison is generally the value of continuing to hold against the net proceeds and redeployment value from selling. Historical purchase price is usually a sunk cost and should not determine the forward decision, although tax and accounting consequences may depend on it.

A hold decision should consider:

• forecast unlevered cash flows;
• current market value and selling costs;
• required capital expenditure;
• expected rent and occupancy changes;
• alternative investments;
• tax, transaction, and governance effects;
• strategic portfolio concentration.

A property can have a positive future return and still be a sell candidate if another opportunity offers superior risk-adjusted value. Conversely, a weak current yield may be acceptable where repositioning creates credible value.

6.3 Refinancing decisions

Refinancing analysis compares the present value of remaining and proposed debt cash flows, including fees, break costs, hedging, amortization, maturity, and covenant effects. Lower interest expense alone does not establish value creation if the new loan increases leverage or concentrates maturity risk.

Managers should test:

• interest-rate scenarios;
• debt-service coverage under stressed NOI;
• loan-to-value under stressed valuation;
• refinancing and exit dates;
• prepayment and extension provisions;
• fixed-rate, floating-rate, and hedging alternatives.

The decision should identify both economic savings and risk transfer. A refinancing that saves cash today but creates a large balloon payment during an uncertain market may be inferior to a more expensive but more resilient structure.

6.4 Budgeting and reforecasting

Budgeting is a resource-allocation process, not merely an extrapolation of last year. It should combine bottom-up operating plans with top-down strategic constraints. Major assumptions—rent growth, occupancy, utilities, wages, insurance, taxes, capital projects, and interest rates—should be documented and sensitivity-tested.

Rolling forecasts are useful when conditions change rapidly. However, frequent forecast changes can weaken accountability if managers revise the target after poor performance. Organizations should distinguish the approved budget, latest estimate, and forecast required to complete the year.

6.5 Asset management

Asset management converts the investment thesis into an operating plan. Typical interventions include lease renewals, tenant mix changes, energy retrofits, service-level adjustments, capital improvements, and refinancing preparation. Each intervention should have a baseline, expected benefit, cost, implementation date, and post-investment review.

The most valuable analytics often concerns relationships rather than isolated KPIs: whether maintenance response affects retention, whether concessions improve net effective rent, or whether energy investment improves operating cost and tenant demand. Correlation is not proof of causation; interventions should be evaluated with credible comparisons where feasible.

7. Investment Memoranda and Executive Communication

An investment memorandum should make the decision auditable and concise. A recommended structure is:

1. Decision requested: approval, rejection, authority limit, or next-stage diligence.
2. Executive recommendation: action, rationale, and principal conditions.
3. Asset and market: location, use, tenancy, competition, and strategic fit.
4. Economics: price, sources and uses, cash flows, valuation, returns, and financing.
5. Risks: downside drivers, mitigants, residual risks, and trigger points.
6. Alternatives: hold, sell, refinance, defer, or renegotiate.
7. Sensitivity and scenarios: variables, ranges, and decision robustness.
8. Implementation: responsibilities, timetable, approvals, and monitoring KPIs.

Executives usually need the implications before the mechanics. Communication should therefore distinguish fact, assumption, estimate, and judgment. It should avoid false precision, unexplained acronyms, and a “single-number” conclusion.

A strong verbal briefing can follow the sequence: “Here is the decision; here is the value created; these are the two drivers; this is the downside; these are the protections; and this approval is requested.”

8. Governance, Ethics, and Data Reliability

Decision analytics is exposed to model risk, data risk, and behavioral bias. Model risk arises when a model is incorrectly specified, implemented, or applied outside its assumptions. Data risk includes incomplete leases, inconsistent area measures, stale valuations, and duplicated records.

Governance should include:

• version control and an assumption register;
• independent review of material models;
• documented data ownership and lineage;
• access controls and change logs;
• reconciliation between model, ledger, and source documents;
• scenario and sensitivity testing;
• post-investment review of forecast versus actual results.

Ethical issues include selective presentation of favorable scenarios, manipulation of KPI definitions, omission of known environmental or safety risks, and inappropriate use of tenant or employee data. A fiduciary or investment committee communication should present material downside information even when it threatens approval.

Regulatory requirements vary by jurisdiction, but managers should consider financial reporting rules, lending covenants, privacy and data protection, anti-discrimination obligations, environmental regulation, building safety, and disclosure requirements. Compliance is not a substitute for good judgment; it is a minimum control boundary.

9. Technology and AI-Enabled Analytics

Business-intelligence platforms can automate data extraction, dashboards, alerts, and scenario comparisons. Machine-learning tools may support rent forecasting, anomaly detection, maintenance prediction, tenant segmentation, and energy optimization. Their value depends on data quality, explainability, monitoring, and human accountability.

AI outputs should be treated as analytical evidence rather than automatic decisions. A model trained on historically discriminatory leasing outcomes may reproduce bias. A forecast may fail after a structural market change. Managers should require documented training data, validation results, confidence limits where available, human review, and a process for correcting errors.

Automation is most appropriate for repetitive, well-defined tasks with review controls. Decisions involving tenant rights, major capital allocation, covenant risk, or material reputational consequences require accountable human authorization.

10. Illustrative Case: Riverside Exchange

Background

Riverside Exchange is an illustrative, fictional 18,000-square-metre mixed-use asset. An investor acquired it several years ago. The asset has 88% physical occupancy, annual NOI of $2.40 million, and outstanding debt of $22 million. The current valuation is estimated at $40 million. Debt matures in 18 months.

Problem

Management must choose among three actions: hold and execute a leasing plan, sell, or refinance and invest in repositioning. The base plan requires $3 million of capital expenditure over two years. Leasing data show that 35% of area expires within 24 months, while two competing assets have recently improved amenities.

Analysis

Current LTV is:

$22m ÷ $40m = 55%

The current NOI yield on estimated value is:

$2.40m ÷ $40m = 6.0%

The dashboard shows a mixed picture: rent on renewed leases is 4% above the existing average, but vacancy days are increasing, tenant complaints concern outdated common areas, and the debt maturity creates refinancing urgency. A hold-only scenario produces acceptable near-term cash flow but risks further vacancy. The repositioning scenario requires capital and temporarily reduces cash flow, yet it improves leasing competitiveness. The sale scenario crystallizes value and eliminates refinancing risk but sacrifices potential upside.

Management prepares three cases. The downside case assumes occupancy falls to 80%, capital expenditure rises by 20%, and exit yield widens by 75 basis points. The base case assumes occupancy reaches 93% after improvements. The upside case assumes stronger demand and lower downtime. The committee is shown levered and unlevered results, not only the promoted equity return.

Approach and recommendation

The recommended action is conditional refinancing combined with a staged capital program. Approval conditions include a fixed-price first phase, minimum pre-leasing targets, an interest-rate hedge, and a covenant buffer tested under the downside case. The asset manager receives quarterly targets for signed leases, effective rent, vacancy days, capital expenditure, tenant complaints, and NOI.

A sell trigger is established if pre-leasing remains below target after two quarters or if stressed DSCR falls below the approved threshold. A refinancing trigger requires at least two lender proposals and a documented comparison of total debt cost, covenants, amortization, and maturity concentration.

Outcome and learning points

The case demonstrates that the decision is not determined by one ratio. The 55% LTV appears moderate, but value decline and NOI pressure can rapidly weaken covenant capacity. The 6.0% yield is informative but does not reveal capital needs or future leasing risk. A staged plan links model assumptions to operational evidence and creates an exit from the strategy if the thesis fails.

The principal learning points are:

• decision alternatives must be compared on a consistent cash-flow basis;
• operating KPIs provide early warning before financial statements deteriorate;
• financing decisions must include maturity and downside analysis;
• conditional approvals can manage uncertainty better than false certainty;
• post-decision monitoring is part of the investment decision itself.

Challenges and Limitations

First, real estate data are often sparse, delayed, and asset-specific. Second, valuation is sensitive to assumptions about market rent, vacancy, cap rates, and capital expenditure. Third, KPIs can create dysfunctional behavior when managers optimize the measure rather than the underlying objective. Fourth, dashboards may encourage short-term reactions to noisy monthly data. Fifth, scenario analysis does not assign probabilities automatically and should not be presented as a forecast distribution unless a defensible methodology exists.

Managers should also recognize that model sophistication does not eliminate uncertainty. Judgment remains necessary when markets are changing, evidence is incomplete, or strategic considerations cannot be represented reliably in a spreadsheet.

Best-Practice Checklist

Before presenting a recommendation, confirm that:

• the decision and alternatives are explicitly defined;
• cash-flow definitions and units are consistent;
• actual, budget, forecast, and underwriting values are labeled;
• major assumptions have owners and evidence;
• downside cases include operational and financing stress;
• KPIs have definitions, thresholds, and escalation owners;
• valuation and return metrics are reconciled;
• material risks and contrary evidence are disclosed;
• the recommendation states conditions and monitoring actions;
• the model, data, and memorandum are version-controlled.

Chapter Summary

Managerial decision support translates financial models into controlled action. NPV, IRR, cap rate, cash-on-cash return, DSCR, and LTV are useful only when their definitions, assumptions, and decision contexts are clear. A balanced KPI architecture connects strategic value, operations, financing, customers, risk, and responsibility. Variance analysis should identify whether performance differences arise from price, volume, timing, assumptions, execution, or scope, and should lead to assigned corrective action.

Dashboards should emphasize material exceptions, provide drill-down evidence, and disclose definitions and uncertainty. Acquisition, hold-sell, refinancing, budgeting, and asset-management decisions require integrated analysis of cash flow, risk, market evidence, financing, and implementation capacity. Investment memoranda should present the decision, evidence, risks, alternatives, conditions, and monitoring plan. Governance controls, ethical disclosure, data reliability, and human oversight are essential, including when AI-enabled tools are used. The strongest decisions are not those with the most elaborate models, but those with transparent assumptions, resilient economics, accountable execution, and effective feedback.

Key Terms

Decision support: A controlled process for converting evidence and analysis into an authorized managerial action.

Net present value (NPV): The present value of expected cash inflows and outflows discounted at a required rate.

Internal rate of return (IRR): The discount rate that makes the NPV of a project equal to zero.

Net operating income (NOI): Property-level operating income before financing and certain owner-level items.

Capitalization rate: Forward NOI divided by property value.

Cash-on-cash return: Annual cash distribution divided by invested equity, subject to stated definitions.

Debt-service coverage ratio (DSCR): Defined cash flow divided by scheduled debt service.

Loan-to-value ratio (LTV): Loan balance divided by property value.

KPI: A key performance indicator used to monitor an objective or critical activity.

Leading indicator: A measure that may signal future performance.

Lagging indicator: A measure that records an outcome after it has occurred.

Variance analysis: Comparison of actual performance with a benchmark and investigation of causes.

Sensitivity analysis: Examination of output changes when one or more assumptions change.

Scenario analysis: Evaluation of a coherent set of assumptions describing a possible future.

Investment memorandum: A structured document supporting an investment or asset-management decision.

Model risk: Risk arising from errors or inappropriate use of a financial model.

Data lineage: A record of data origin, transformation, ownership, and use.

Refinancing risk: Risk that debt cannot be renewed or replaced on acceptable terms.

Economic occupancy: Occupancy measure that may reflect concessions, bad debt, or effective rent.

Risk-adjusted value: Value interpreted in relation to uncertainty, downside exposure, and required return.

Review Questions

Short Answer Questions

1. Why should NPV and IRR be interpreted together rather than used mechanically?
2. Distinguish a leading KPI from a lagging KPI and give one real estate example of each.
3. What is the difference between an absolute variance and a percentage variance?
4. Why must the numerator in DSCR be defined carefully?
5. Identify three controls that reduce financial model risk.

Long Answer Questions

1. Explain how a balanced KPI architecture can connect portfolio strategy with property-level operations.
2. Discuss the analytical and governance requirements of a hold-sell decision.
3. Explain how variance analysis can distinguish price, volume, timing, assumption, and execution effects.
4. Evaluate the benefits and limitations of dashboards in executive decision-making.
5. Describe the essential sections of a high-quality real estate investment memorandum.

Analytical / Application Questions

1. An asset has budgeted revenue of $800,000, but actual revenue is $760,000. Occupied area declined by 8%, while effective rent increased by 3%. Decompose the approximate revenue movement and recommend two management actions.
2. A property has NOI of $3.2 million, debt service of $2.4 million, debt of $30 million, and value of $50 million. Calculate DSCR and LTV, then explain two risks that would remain even if both ratios appear acceptable.
3. Using the Riverside Exchange case, design a quarterly dashboard containing six KPIs, define an escalation threshold for each, and explain how the dashboard would test whether the repositioning strategy is working.

Multiple Choice Questions with Answers

Question 1

Which statement best describes NPV?

A. The discount rate that makes project value equal to zero

B. The present value of cash flows less the initial investment at a specified discount rate

C. Annual NOI divided by property value

D. Debt balance divided by annual debt service

Correct Answer: B. The present value of cash flows less the initial investment at a specified discount rate

Question 2

A property has NOI of $2.5 million and scheduled debt service of $2.0 million. Its DSCR is:

A. 0.80×

B. 1.00×

C. 1.25×

D. 2.00×

Correct Answer: C. 1.25×

Question 3

Which is most clearly a leading indicator of future NOI performance?

A. Last year's NOI

B. Historical property value

C. Current leasing pipeline and lease expirations

D. Prior-year tax expense

Correct Answer: C. Current leasing pipeline and lease expirations

Question 4

Actual revenue is $484,500 and budget revenue is $500,000. The percentage variance is:

A. −3.1%

B. 3.1%

C. −15.5%

D. 15.5%

Correct Answer: A. −3.1%

Question 5

Which issue makes IRR particularly difficult to interpret?

A. It uses cash flows over time

B. It can be affected by multiple changes in cash-flow sign

C. It requires an initial investment

D. It is expressed as a percentage

Correct Answer: B. It can be affected by multiple changes in cash-flow sign

Question 6

If a property has debt of $22 million and value of $40 million, its LTV is:

A. 45%

B. 55%

C. 62.5%

D. 82%

Correct Answer: B. 55%

Question 7

The most appropriate purpose of a dashboard traffic-light threshold is to:

A. Replace all managerial judgment

B. Make every metric appear equally important

C. Trigger defined attention or escalation based on agreed limits

D. Guarantee that forecasts are accurate

Correct Answer: C. Trigger defined attention or escalation based on agreed limits

Question 8

Which item should normally be treated as a sunk cost in a forward hold-sell analysis?

A. Expected selling costs

B. Future capital expenditure

C. Original purchase price already paid

D. Expected future rent

Correct Answer: C. Original purchase price already paid

Question 9

A refinancing decision should assess more than interest savings because refinancing may also change:

A. The historical purchase price

B. Maturity, covenants, leverage, and refinancing risk

C. The definition of physical occupancy

D. The existence of past operating data

Correct Answer: B. Maturity, covenants, leverage, and refinancing risk

Question 10

Which practice best supports ethical investment-committee communication?

A. Presenting only the base case

B. Omitting risks that could delay approval

C. Disclosing material downside information and distinguishing facts from assumptions

D. Using maximum numerical precision to imply certainty

Correct Answer: C. Disclosing material downside information and distinguishing facts from assumptions