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We usually look at models as shapes. Scripts, dashboards and AI assistants see them differently: as a long table where each element is a row with a type, a location and properties. Every question ("how many doors on Level 3 have no fire rating?") is a filter and a count on that table. If the table is tidy, the answer is instant. If it isn't, every tool gives the wrong answer.
The table behind the model
| Element | Category | Type | Level | Zone | FireRating |
|---|---|---|---|---|---|
| 3cUkl… | Door | D1 900×2100 | L03 | ZA | EI 30 |
| 1Gq8x… | Door | D1 900×2100 | Level 3 | ZA | (blank) |
| 0wa6Q… | Door | D2 1200×2100 | L03 | Zone B | EI 60 |
Three doors, three problems: a level written two ways, a zone written two ways, a missing value. A person reading the model barely notices. A filter, a 4D rule or a quantity export silently gets them wrong.
A property (or parameter) is a named value on an element: FireRating = EI 30. Software groups properties into property sets (in IFC, for example Pset_DoorCommon). Some properties are typed in by people; others are calculated (area, volume).
Consistent beats complete
A model where every door has a level written the same way is more useful than one where doors have twenty properties spelled three ways. Agree allowed values for the properties people filter by:
| Property | Allowed values (example) |
|---|---|
| Level | B1, L00, L01, L02… |
| Zone | ZA, ZB, ZC |
| System | Use the project's system list |
| Status | Existing, New, Demolish |
See it as a table
Before any script or dashboard, export elements and their properties to a spreadsheet. Sort by each column. Gaps, duplicates and inconsistent spellings jump out in minutes.
An export of doors gives:
| Door | Level | FireRating |
|---|---|---|
| D-001 | L01 | EI 30 |
| D-002 | Level 1 | (blank) |
| D-003 | L01 | EI30 |
| D-004 | L02 | EI 60 |
| D-005 | 02 | (blank) |
| D-006 | L02 | n/a |
| D-007 | L02 | EI 30 |
| D-008 | Level 2 | EI 60 |
- Clean the values: what are the allowed values you'd use for Level and FireRating?
- After cleaning, how many doors per level have no valid fire rating?
- Which of these problems would you fix in the model, and which with a check?
Key takeaways
- Every element is a row: a type, a location and properties; every question about the model is a query on that table.
- Consistent values matter more than more values: 'L03', 'Level 3' and '03' break every filter.
- Agree allowed values (lists) for the properties people filter by: level, zone, system, status, classification.
- Export to a spreadsheet early to see the data the way scripts and dashboards will.
Your notes
Related tool
Export element properties from an IFC model to a formatted Excel schedule.
Open IFC to Excel Schedules →