Definition

In an APC context, business forecasting is the process of using historical data, market intelligence, and analytical techniques to estimate a firm’s future performance — typically revenue, costs, staffing needs, and cash flow. The HM Treasury Green Book (2022) distinguishes between quantitative forecasts (based on numerical data) and qualitative forecasts (based on expert judgement), and most surveying practices use a combination of both.

Why this matters for Business Planning

  • Level 1 knowledge: you must be able to name at least three forecasting methods and explain when each is most appropriate.
  • Forecasts underpin the firm’s budget, staffing plan, and marketing strategy; inaccurate forecasting leads to over-hiring, cash shortfalls, or missed growth opportunities.
  • Assessors expect candidates to link forecasting to SMART objectives and KPIs, demonstrating that planning is evidence-based.
  • RICS regulated firms must be financially viable; good forecasting is part of the management discipline that maintains that viability.

Key principles

Quantitative forecasting

Time-series analysis uses historical data to project future trends. A valuation firm might plot monthly instruction volumes over three years and use a moving average or trend line to project the next 12 months. Regression analysis models the statistical relationship between a variable such as fee income and one or more drivers such as mortgage approvals or planning application volumes. These methods are most reliable when historical patterns are stable and less useful during structural market shifts.

Qualitative forecasting

When historical data is limited — for example, when entering a new service line — qualitative methods are more appropriate. The Delphi method collects structured opinions from a panel of experts, iterating to a consensus. Expert judgement from partners familiar with a sector is the most common qualitative input to a firm’s annual fee forecast, supplemented by client pipeline reviews and market surveys.

Demand, financial, and resource forecasting

Most practices run three interlocking forecasts. Demand forecasting estimates instruction volumes by service line, drawing on market data. Financial forecasting translates demand into a projected income statement and cash flow. Resource forecasting uses the demand forecast to determine the staffing levels and skills needed to deliver the anticipated workload without overloading existing teams.

Scenario planning

A single-point forecast is rarely sufficient. Scenario planning develops a base case, an upside, and a downside, testing the firm’s resilience to each. Sensitivity analysis identifies which assumptions have the greatest impact: a 10 per cent fall in average fees may matter more than a 10 per cent fall in volume. Presenting a range of outcomes rather than a single number is more credible and more useful to decision-makers.

Relevant RICS guidance and legislation

  • RICS Rules of Conduct (effective 2 February 2022) — firm obligations require regulated firms to be financially sound; robust forecasting supports that requirement.
  • HM Treasury Green Book (2022) — the authoritative UK framework for appraisal and investment decisions; its guidance on optimism bias is directly relevant to all business forecasts.
  • Companies Act 2006 — directors are required to prepare a directors’ report and, where applicable, a strategic report that includes forward-looking information.

Ethics and Rules of Conduct angle

Forecasts prepared for external parties — lenders, investors, or clients — must be honest and based on reasonable assumptions. Knowingly overstating projected revenue to secure a bank loan would breach Rule 1 of the RICS Rules of Conduct (honesty and integrity) and could constitute fraud under the Fraud Act 2006. Internally, optimism bias — the tendency to over-estimate future performance — is well documented; responsible managers subject forecasts to independent challenge before presenting them to decision-makers.

APC-style Q&As

Q (Level 1)Name three forecasting methods used in a surveying practice.

Time-series analysis (projecting trends from historical data), the Delphi method (structured expert consensus), and market surveys (gathering client spending intentions). Financial and resource forecasting are recognised categories that combine these techniques.

Q (Level 1)What is the difference between a qualitative and a quantitative forecast?

A quantitative forecast uses numerical data and statistical analysis, such as a trend line. A qualitative forecast relies on structured expert opinion, such as a Delphi exercise or client survey. Most firms use both, with qualitative methods providing context and sense-checks for quantitative models.

Q (Level 2)How does your firm produce its annual revenue forecast?

(example) Our practice produces a two-stage forecast: service-line leads submit a bottom-up estimate of anticipated instructions, drawing on their pipeline and client commitments. Finance overlays a top-down sense-check against historical conversion rates and market data. The two are reconciled and the resulting forecast is stress-tested against a downside scenario before the management board approves it as the basis for the budget.

Q (Level 2)Why is scenario planning preferable to a single-point forecast?

A single-point forecast presents one outcome as if it were certain, leaving the firm unprepared for deviation. Scenario planning develops a base case, upside, and downside, allowing management to identify trigger points at which they would change strategy. It also forces the team to test assumptions and identify the most sensitive variables before committing to a plan.

Q (Level 3)Your firm is considering launching a new party wall service with no historical data. How would you approach the demand forecast?

With no internal data, I would begin qualitatively: surveying existing clients about their party wall needs, consulting colleagues who have worked in this area, and researching publicly available data such as planning application volumes in our target geography. I would then build a bottom-up model estimating instructions the team could handle per month, applying a conservative conversion rate to enquiries. I would pressure-test the model with a Delphi exercise involving two or three external specialists before presenting a range of scenarios — launch, defer, or six-month pilot — to the management board with clear decision criteria.