Seoul homeowner learning how to compare retail asking and completed prices

I’d include it under context, not revenue. The model should work from current evidence, with future development treated as a scenario rather than guaranteed demand.
 
That same distinction helps cross-market comparisons: observed facts in one section, assumptions in another. Otherwise optimistic local narratives quietly become numbers.
 
For a first pass, I would compare only completed deals of similar unit type and size, then inspect the outliers. The strange cases often expose a lease, condition or location difference you initially missed.
 
Price per area is useful for sorting, not deciding. Retail value can change sharply with frontage, floor, access and layout even when the measured area is identical.
 
And confirm that every source uses the same area definition before calculating anything. A neat spreadsheet can still compare unlike measurements.
 
Property management belongs in the model too. Ask who handles common areas, repairs, tenant communication and vacant-unit security, then identify which costs sit with the owner.
 
That raises a good legal-checklist question: which responsibilities are written into the lease or building arrangements, and which are merely assumed? The answer will vary by property and jurisdiction.
 
I would not wait for a perfect checklist before researching. Begin with ten genuinely comparable units; the missing fields will quickly show which questions matter.
 
Ten is manageable, but keep rejected units in a separate tab with the reason for rejection. Otherwise you may unknowingly select only the examples supporting your view.
 
Good suggestion. Rejection reasons such as wrong floor, unknown occupancy, stale advertisement or unsuitable size also reveal where the available data is weakest.
 
For completed-price gaps, use both the amount and percentage difference. The percentage helps comparison, while the cash amount matters for financing and renovation capacity.
 
Be careful calling it a discount unless you know the final asking price immediately before completion. Comparing a sale with the first advertisement may exaggerate the negotiated reduction.
 
Duplicate advertisements are another trap. Match by address and unit characteristics where possible, rather than assuming every listing entry represents a different shop.
 
Could the local board maintain a blank comparison template rather than recommending one dataset? Members could then add sources they trust without forcing unlike information into one ranking.
 
Suggested core fields: district, unit type, floor, area definition, condition, occupancy, first and latest asking prices, completed price, dates and evidence notes.
 
Optional fields could cover frontage, access, building charges, lease details, renovation estimate and financing. Keeping them optional avoids pretending every researcher can obtain everything.
 
I’d add a plain-language reason for considering each property. Numbers alone can’t show whether it fits an owner-occupier, passive landlord or redevelopment idea.
 
That purpose should appear at the top. A vacant unit may be a problem for an income buyer but useful to someone wanting possession for their own business.
 
It also prevents mortgage comparisons from drifting. Financing suitable for a home purchase cannot simply be carried across to a retail acquisition without checking the actual terms available.
 
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