Hello from Manchester — developer focused on retail units

For occupied units, capture the income assumptions separately from the building details. Otherwise you may think the property sold at a premium for location when buyers were really responding to the occupational arrangement.
 
Property management should sit inside the downside case, not as a footnote. Ask who handles repairs, access, routine communication and shared areas, and what happens to your model if those tasks cost more than expected.
 
A useful exercise is to model the same unit three ways: occupied as presented, vacant at purchase, and vacant after a delayed reletting. If only the first case works, that tells you something important.
 
How will you handle publication delays in completed-price information? The listing market can move while you wait, so every comparison should show both the marketing date and the completion date.
 
Keep withdrawn and repeatedly relisted units. They are not completed evidence, but they reveal where seller expectations failed to meet demand. Just don’t assign them an imaginary sale price.
 
Renovation estimates should include uncertainty and time, not only materials and labour. A lower purchase price may be poor value if the unit remains unusable during a complicated project.
 
What unit of comparison are you planning to use? Total price is easy to read, but floor area, frontage, occupancy and condition all affect whether two retail properties belong in the same set.
 
I’d also track how long each assumption remains unverified. An estimate carried for months can start looking like a fact simply because it has been copied into several versions of the model.
 
Tax treatment and transaction charges can depend on the deal and current rules, so I would not hard-code them from a forum example. Keep editable fields and confirm the applicable position when a real purchase emerges.
 
Use a stable property identifier in your own table. The same unit can return with new photographs or wording, and duplicate entries will make the apparent supply larger than it is.
 
So my direct answer remains: start on the Manchester board, choose perhaps ten genuinely comparable units, and learn the transaction trail. Broader datasets make more sense once you know what your fields mean.
 
Would mixed-use buildings be included? A shop beneath other accommodation can carry costs and responsibilities that a standalone retail unit does not, so I’d separate those categories immediately.
 
If mixed-use is relevant, split the model into building-wide costs, retail-specific costs and anything attributable to another part. A single blended allowance will be difficult to test later.
 
The remaining lease term, repair wording and renewal assumptions can materially affect an occupied-unit comparison. Those are matters for the relevant advisers on a live deal, but your research table should at least flag when they are unknown.
 
I’d defend the cross-market angle more than some replies have. Different cities can expose assumptions Manchester discussions take for granted. Just compare decision processes and risk allocation rather than pretending prices are directly portable.
 
Yes, cross-market reading is valuable. The danger is converting every observation into a universal discount from asking price. There is no reliable single discount for retail units.
 
A price reduction may also reflect a changed package, corrected listing, deterioration, vacancy or urgency. Treat it as a prompt to investigate, not proof that the original seller was simply optimistic.
 
Floor-area figures deserve their own note about where they came from. If two listings describe space differently, a price-per-area comparison can look precise while being fundamentally inconsistent.
 
Could the table include a short negotiation narrative? Even ‘reason unknown’ is useful. It would stop people from treating every gap between advertised and completed price as evidence of the same bargaining pattern.
 
I’d use confidence labels: confirmed, stated in marketing, inferred, and unknown. That makes it much easier to revisit weak assumptions without pretending all entries have equal reliability.
 
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