Built environment cost intelligence for governments, public bodies and infrastructure teams.

What this supports

ICMSCosts is being extended to let users build their own reporting views from the underlying project, cost, attribute and benchmarking data. The current administrative reporting tool already supports configurable chart definitions, and this will be developed into a more usable end-user reporting interface.

• Create reusable reports for specific project types.
• Build pie charts, bar charts, scatter charts and bubble charts.
• Select project attributes for chart axes and grouping.
• Select ICMS cost categories for cost-based reporting.
• Use cost dividers to create cost-per-unit style metrics.
• Apply report filters and limiters to focus results.
• Support organisation-specific reporting views and dashboards.

Example report types

• Bubble chart comparing project size, cost and year.
• Scatter chart showing cost per functional unit against capacity.
• Bar chart comparing ICMS cost categories across project types.
• Pie chart showing distribution by project attribute or cost group.
• Filtered reports for a country, organisation, asset type or programme.

Why it matters

Different organisations need different views of the same underlying benchmark data. Custom reporting will let users turn ICMSCosts into a decision-support tool rather than just a searchable database.

What this supports

ICMSCosts includes advanced project filtering tools designed to help users identify relevant comparators, rather than relying on broad or weakly-related benchmark sets.

• Search by project type, sector and functional use.
• Filter by country, city and regional coverage.
• Restrict results by project year and project status.
• Filter using project attributes and custom metadata.
• Search across organisation, shared and public datasets.
• Support ICMS-based benchmarking and peer analysis workflows.

Example use cases

• Find comparable airport projects in North America delivered after 2020.
• Search for recreational facilities with similar functional unit sizes.
• Identify projects suitable for lifecycle cost comparison.
• Build benchmark groups for programme assurance or business case review.

Why it matters

Better comparator selection improves the credibility of benchmarking results and reduces the risk of misleading cost comparisons.

What this supports

ICMSCosts includes a peer matching system designed to identify the most relevant benchmark projects before cost comparison takes place. Rather than comparing against all projects equally, the platform progressively searches for increasingly broader comparator groups only when required.

• Prioritise projects of the same project type.
• Prefer projects within the same country or geographic region.
• Optionally match functional use and operational purpose.
• Apply functional unit size bands to improve comparability.
• Expand peer ranges gradually when insufficient evidence exists.
• Generate confidence indicators based on peer quality and quantity.

How the process works

The system begins with the tightest comparator set available - for example:

• Same project type
• Same country
• Similar functional unit size

If too few peers are available, the search progressively broadens to larger size bands or wider geographic scopes, ensuring the benchmark still has sufficient evidence behind it.

Why it matters

Strong benchmarking depends on comparator quality. Intelligent peer selection improves statistical relevance, transparency and trust in benchmark outcomes.

What this supports

The ICMSCosts data catalog helps users understand what supporting data is available before they begin benchmarking. It summarises inflation datasets, location factor datasets and cost overlays, including their coverage by country, city, year and source.

• Browse available inflation and price index datasets.
• Review location factor datasets for geographic cost adjustment.
• Explore cost overlays such as tariffs, subsidies, regulatory impacts and market-specific adjustments.
• Filter catalogue coverage by country.
• Identify available report links for authenticated users.
• Understand whether datasets are public, organisation-specific or personal.

Why it matters

Benchmarking is only credible when users can understand the underlying data. The catalogue gives visibility over the datasets used to normalise, adjust and enrich project comparisons.

What this supports

ICMSCosts includes statistical outlier analysis tools designed to highlight projects whose costs differ significantly from comparable peers. This helps users identify unusual records, validate datasets and investigate potential anomalies.

• Analyse initial construction cost per functional unit.
• Analyse lifecycle cost per functional unit.
• Compare projects against dynamically selected peer groups.
• Use inflation-adjusted and location-adjusted benchmark values.
• Provide confidence indicators for benchmark reliability.
• Support project assurance and data quality review workflows.

How the analysis works

Projects are compared against statistically relevant peer sets generated using project type, geography, function and size similarity. The platform then evaluates whether a project’s costs sit materially outside expected peer ranges.

Where sufficient evidence exists, projects may be flagged as unusually high or unusually low compared with similar projects.

Example use cases

• Identify potentially misclassified or incomplete records.
• Review projects with unexpectedly high construction cost.
• Support assurance reviews and audit processes.
• Improve confidence in benchmarking datasets.

Why it matters

Benchmarking quality depends on reliable underlying data. Outlier detection helps organisations understand whether benchmark results are representative or potentially distorted by anomalous records.

What this supports

ICMSCosts includes inflation datasets and geographic location factors to support more meaningful comparison between projects delivered in different years, cities and countries.

• Rebase historical project costs using inflation indices.
• Adjust benchmark comparisons between cities and regions.
• Support international benchmarking workflows.
• Use multiple index sources and dataset series.
• Apply organisation-specific or public adjustment datasets.
• Integrate inflation and location adjustments into benchmarking and outlier analysis.

How the process works

Project costs can be normalised to a common basis using selected inflation series and location factors. This reduces distortion caused by market conditions, currency timing and regional construction cost variation.

The platform supports both public datasets and organisation-managed adjustment series, allowing users to tailor benchmarking to their own markets and methodologies.

Example use cases

• Compare projects delivered in different years.
• Benchmark projects across multiple countries.
• Review regional construction market differences.
• Support programme-level cost rebasing exercises.
• Improve the reliability of international cost comparisons.

Why it matters

Raw project costs alone rarely provide a fair comparison. Inflation and location adjustment improves benchmark credibility and helps users compare projects on a more consistent basis.

What this supports

ICMSCosts allows organisations to control how their project data is managed, validated and shared. Data can remain entirely private, be used within a sandbox environment, or contribute to wider benchmark datasets.

• Create organisation-specific sandbox environments.
• Upload and validate private benchmark datasets.
• Restrict access to internal project records.
• Share selected projects into wider benchmark pools.
• Support anonymised project visibility where appropriate.
• Separate public, organisation and personal datasets.

Sandbox environments

Sandbox environments allow organisations to experiment with imports, overlays, benchmarking rules and reporting workflows before introducing data into production benchmarking exercises.

Anonymisation and sharing

Organisations can choose whether project names and programme information remain visible, are anonymised, or are shared only in aggregate benchmarking workflows.

Shared-data approaches can improve benchmark richness while still supporting governance and confidentiality requirements.

Why it matters

Many organisations want the benefits of benchmarking without immediately exposing sensitive project information. Flexible data ownership and sharing controls help organisations adopt benchmarking at their own pace.