Berlin homeowner building a student-housing comparison sheet

A small spreadsheet tip: distinguish blank, estimated and confirmed entries. A blank can accidentally behave like zero and make an incomplete option look best. The visual distinction is especially helpful for transaction and renovation items you are still researching.
 
I wouldn’t spend too long designing the perfect model before entering a few real examples. Three or four varied listings can reveal missing fields quickly. They are test cases, not evidence of completed values, so keep them in an advertised-price section.
 
That is a fair counterpoint to Naomi’s earlier suggestion. Define enough structure to avoid random collecting, then let actual listings challenge it. If every example needs extensive exceptions, the categories probably need revising.
 
When judging any dataset, look for clear definitions: geography, property type, date, condition and whether the price is advertised or completed. If those are unclear, a large table may be less useful than a smaller, transparent set of examples.
 
Another reason to keep the two formats separate: management complexity may not scale neatly with purchase price. Put operational questions beside the financial model rather than burying them in one expense estimate.
 
Once the sheet is populated, test a conservative case by changing several assumptions together rather than only one at a time. Renovation delay, higher management effort and weaker income may be connected. The purpose is not prediction; it is seeing which option becomes fragile first.
 
The useful starting package now seems clear: local-board terminology, two distinct property models, labelled asking and completed prices, itemised transaction costs, renovation and management scenarios, plus a Berlin-specific legal checklist. Share the column headings when drafted; people can then spot omissions without debating unsupported figures.
 
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