A reviewable early estimate keeps quantities, rates, assumptions and uncertainty connected.
An early estimate helps decide whether a project proceeds, even though little is settled. This note sets out a structured method and uses the Paparangi feasibility study to show how one scenario responds to changes in its inputs. It does not claim measured time savings or quantity-surveyor accuracy.
The first cost view sets the working budget.
Before much is drawn, a quantity surveyor turns a sketch and a floor area into a number. Its purpose is plain: to establish whether the project is affordable and to set a realistic cost limit that then disciplines the design.1 It is also where a project is quietly won or sunk, because a figure set too low commits everyone to something that cannot be built for the money, in a market where build costs have risen far faster than general inflation over the past decade.19
Whether the project proceeds at all, on the figure put in front of the client.
The budget the design then has to be kept inside as it develops.
What the client can realistically ask for on this budget, and what has to give.
What must be raised or borrowed, and the basis on which lenders and boards commit.
Concept-stage estimates need an explicit range.
Uncertainty is structural at this stage of design. With the design only a few percent defined, the international cost-classification standard puts the realistic accuracy of a concept estimate as wide as minus 50 to plus 100 percent.2 The historical record agrees: across a large sample of public projects, costs were underestimated in almost nine out of ten, by an average of about 28 percent, and a later database of more than 16,000 projects found over 90 percent ran over budget, over schedule, or both.34
The realistic accuracy band of a concept estimate, at 0 to 2 percent design definition, under the AACE classification.
AACE 18R‑97Public infrastructure projects whose costs were underestimated against the decision-to-build figure.
Flyvbjerg, JAPA · 2002Average cost overrun across 258 infrastructure projects, in constant prices, with a wide spread around it.
Flyvbjerg, JAPA · 2002The Paparangi study shows why the assumptions matter more than the headline number.
The Paparangi method study tested a consented 30-home scheme using stated areas, cost allowances, sale-price assumptions, margin and finance. The result is a feasibility scenario for review, not a valuation or a quantity-surveyor estimate.
| Input | Study value | Status | Review implication |
|---|---|---|---|
| Base cost | $480,570 per home | Before land, finance and margin | Verify rates |
| Commercial area | 2,814 m² | 4.9% below design model | Conservative |
| Two-bedroom price | $779,000 | Advertised pre-sale, not settled | Market check |
| Margin hurdle | 18% | Named scenario input | Developer review |
| Facility rate | 7.5% | Named scenario input | Finance review |
| Land residual | $1.17m | Advertised-price case | Illustrative |
The scenario is sensitive to one input. When the two-bedroom price falls from $779,000 to $740,000, the illustrative residual falls to $456,000. At $701,000 it becomes negative. The study therefore directs the next work toward settled sales evidence, a current cost plan and verified contribution allowances rather than presenting the $1.17 million result as settled.
AI can coordinate measurement, pricing and benchmarking under QS review.
The practical contribution is keeping quantities, rates, comparables and assumptions connected as the design changes. Whether this is faster than the current process still needs a timed comparison.1420
Quantity takeoff
Measuring areas and counts from the available design information, with each quantity open to review.
Live pricing
Pricing those quantities against a current cost library, and updating as rates and the design change.
Benchmarking
Comparing the figure against the outturn of similar past projects, which is the basis of a sound early estimate.
Present the range
Presenting the estimate as a band with its assumptions so its uncertainty remains clear.
Input quality sets the limit.
At concept stage the design is only partly defined and good structured cost history is scarce, so the number remains wide whoever produces it. The method can make the calculation and its sources traceable, while the underlying uncertainty remains.2
Structured cost tools provide the more reliable estimating method.
A controlled method keeps rates and arithmetic in structured tools and uses AI to coordinate, compare and explain the work. This preserves a reproducible calculation and a source trail behind each material input.
Every material rate carries a source, region, date and stated basis.
Cost evidenceQuantities and sums remain deterministic, reproducible and open to checking.
Structured calculationThe result shows its sensitivity and does not hide uncertainty behind precision.
Professional adviceA general model estimates
Ask a capable model to reason over the project and the open web and arrive at a cost figure.
- Drafting and explaining the estimate in plain words
- Surfacing comparable projects qualitatively
- Speed, and flexing to an unusual brief
- A readable first pass with nothing to build
- Exact arithmetic, which it gets wrong as numbers grow
- Current local rates, which it does not hold
- Provenance: which rate came from where, and when
- Returning the same figure twice
Structured cost data, AI on top
Keep rates and sums in cost tools and your own project history, and use AI to orchestrate, draft and explain.
- Current, authoritative rates with a source attached
- Deterministic, checkable arithmetic
- Benchmarking against real project outturns
- An auditable trail behind every figure
- Only as good as the cost data behind it
- Build and upkeep of the data and tools
- The genuinely novel project with no comparable
- Questions outside the rates it holds
The strongest method keeps rates and arithmetic in structured tools, uses AI for coordination and drafting, and leaves the final judgement with the surveyor.
The arithmetic
Rates and sums sit in structured tools, so every figure is current, sourced and reproducible.
The judgement
AI drafts, benchmarks and surfaces the range and the assumptions for the surveyor to weigh.
The signature
The quantity surveyor tests the inputs, sets the uplift and owns the number.
False precision is the central risk in an AI-assisted estimate.
An AI estimate returned to the dollar can imply certainty at a stage when the realistic range is wide. Established estimating discipline addresses this risk: show the range, carry every rate's source and date, name the assumptions, and add an explicit allowance for optimism, which the evidence says is endemic.3
A figure like $4.24 million reads as settled, invites the client and the board to plan against it, and hides the wide band it actually carries. When the real cost arrives, the gap becomes the project's problem rather than the estimate's, and that is how budgets are quietly broken.
The estimate is a band, every rate shows where it came from and when, the assumptions are named, and an optimism-bias uplift is applied from comparable projects. New Zealand's own guidance for public investment expects exactly this, and the United Kingdom Treasury's evidence-based uplift for a non-standard building at this stage reaches about half the capital cost again.111213
The next study should compare the structured method with a normal early estimate.
A named, qualified surveyor should review both outputs against the same brief. The comparison should record preparation time, corrections, missing assumptions, sensitivity and whether each figure can be traced to its source. The RICS standard on AI requires that a named, qualified surveyor owns and documents any AI output that affects the advice.1822
Connected inputs
Quantities, rates, assumptions and outputs remain linked.
An honest range
The client sees a scenario and its sensitivity, not a settled figure.
A review trail
Each material input can carry its source and status.
Clear next evidence
The result identifies the rates and market inputs that need verification.
Preparation time
Record the full time spent assembling and reviewing each estimate.
Correction effort
Count material changes made by the reviewing surveyor.
Source coverage
Check whether every material rate and assumption can be traced.
Decision usefulness
Ask whether the result supports the next investment decision.
The quantity surveyor remains accountable for the early estimate.
AI can support measurement, pricing and benchmarking, then present the range and assumptions for review. The quantity surveyor tests the comparables, sets the uplift and takes responsibility for the advice. The Paparangi scenario demonstrates a reviewable structure. A measured practice trial is still required to establish speed and accuracy.
- RICS. NRM 1: Order of cost estimating and cost planning for capital building works. Effective 1 Dec 2021. rics.org
- AACE International. Recommended Practice 18R‑97 / 56R‑08: Cost Estimate Classification System. 2016–2020. aacei.org
- Flyvbjerg, Holm & Buhl. Underestimating Costs in Public Works Projects: Error or Lie? Journal of the American Planning Association 68(3). 2002. arxiv.org
- Flyvbjerg & Gardner. How Big Things Get Done (database of 16,000+ projects). 2023. prh.com
- Office of the Auditor-General. Making infrastructure investment decisions quickly (NZ Upgrade and Shovel-Ready). 2023. oag.parliament.nz
- RNZ. Auckland's City Rail Link cost climbs to $5.49b. 2023. rnz.co.nz
- RNZ. Transmission Gully: original estimate and the Richards review. 2021–2022. rnz.co.nz
- RNZ. New Dunedin Hospital: approved budget higher than government claimed (Treasury QIR). 2026. rnz.co.nz
- RNZ. School building projects face delays and cost blowouts (MoE OIA data). 2023. rnz.co.nz
- NZ Herald. America's Cup infrastructure cost and economic cost-benefit. 2021. nzherald.co.nz
- HM Treasury. Supplementary Green Book Guidance: Optimism Bias (uplift table). gov.uk
- NZ Treasury. Better Business Cases and Guide to Social Cost Benefit Analysis (optimism bias; quantitative risk assessment). 2015–2023. treasury.govt.nz
- Waka Kotahi NZ Transport Agency. Cost Estimation Manual SM014 (consider optimism bias; expected accuracy by method). Eff. May 2025. nzta.govt.nz
- Togal.AI. Automated quantity takeoff: accuracy and time-saving claims (vendor). 2026. togal.ai
- Emsley et al. Data modelling and the application of a neural network approach to the prediction of total construction costs. Construction Management and Economics 20(6). 2002. tandfonline.com
- Mirzadeh et al. (Apple). GSM‑Symbolic: understanding the limitations of mathematical reasoning in LLMs. 2024. arxiv.org
- Cobbe et al. (OpenAI). Training Verifiers to Solve Math Word Problems (GSM8K; calculator injection). 2021. arxiv.org
- RICS. Responsible use of AI in surveying practice (a named surveyor must own AI outputs). Effective 9 Mar 2026. rics.org
- QV (Quotable Value). Construction costs up 61% since 2015 versus CPI 33%. 2025. qv.co.nz
- QV CostBuilder. New Zealand construction cost database (elemental and per-m² rates for preliminary estimating). 2026. costbuilder.qv.co.nz
- Rider Levett Bucknall. Riders Digest New Zealand 2025 (indicative cost rates, excl GST). 2025. rlb.com
- NZIQS. Code of Conduct (members accountable for negligent or incompetent quantity surveying services). 2025. nziqs.co.nz