Tokyo listings averaging 85 days — market shift or sample bias?

[sailsAndQuill]

I’m reviewing Tokyo property listings priced from ¥21,420,000 to ¥32,130,000 this month. The retail units still online appear to take roughly 85 days to find a buyer, with most of the outliers seemingly linked to lease length.

Would recent completed sales support that picture, or is the 85-day figure being distorted because my sample excludes properties that sold quickly and includes stale or withdrawn stock?

 

[askTheSparrow]

The live listings probably overstate the typical selling period. Anything that completed quickly has already disappeared, while difficult stock remains visible and keeps ageing. I’d separate completed, active and withdrawn listings, then compare their original listing dates. Otherwise 85 days is mainly a measure of what has survived online.

 

[EarlyGlass]

I agree that the live sample is biased, but I’m not convinced that ageing stock alone explains the result. The ¥21,420,000–¥32,130,000 range may be combining neighbourhoods with different buyers, so an 85-day average could conceal several smaller markets.

I would first group the properties by a tighter location and comparable condition, then separate vacant units from those sold with leases. After that, look at withdrawals and price reductions within each group. If the older listings cluster around one occupancy type or area, that is stronger evidence than assuming lease length accounts for every outlier.

 

[BenFlint]

I wouldn’t dismiss the 85 days entirely. A live-listing sample is biased, yes, but it can still show what buyers are currently resisting. Check whether the older units had price cuts, and when those cuts happened. Eighty-five days at the original asking price is a different story from 85 days with a recent reduction.

 

[blueprint.solid]

New-listing volume matters too. If many properties entered the range this month, the younger listings should pull the average down. If few arrived, older stock will dominate it. Compare like-for-like properties by neighbourhood, condition and occupancy before deciding that lease length is the main cause.

 

[hikesAndBirch]

Seller motivation may explain some apparently irrational hold times. A seller testing a price can leave a unit online longer than someone who needs a prompt transaction. Completed sales are useful, but without the asking-price history they may hide the negotiation that finally produced the deal.

 

[blueprint.solid]

Agreed on asking-price history, although I’d avoid making the analysis too elaborate before confirming the basic dates. First establish whether “days” ends at an accepted offer, removal from the portal or recorded completion. Those events can be separated by a meaningful period, so completed-sale timing and listing exposure are not automatically comparable.

 

[EarlyGlass]

That definition issue is important. I’d make a simple table: first listed, latest price change, removed, and completed where known. Mark withdrawals separately rather than assuming they sold. Then group by small neighbourhood and vacant versus leased. Even with gaps, that should reveal whether 85 days comes from a broad slowdown or a handful of awkward properties.

 

[EarlyGlass]

One more split: condition. A cheaper unit needing work may attract a different buyer from a ready-to-use property at the same price. Financing can also affect which buyers can proceed, but unless you have reliable deal-level information, I wouldn’t assign failed or delayed sales to financing. Keep that as a possible explanation, not a measured one.

 

[blueprint.solid]

I partly disagree with narrowing the sample too far. Once every unit is divided by neighbourhood, condition, occupancy and price history, the groups may become too thin to say much. Start with the broad range, identify which properties are driving the 85-day result, and only then add the categories that actually explain those cases.

 

[OrlaIves]

A practical way to present it would be a range rather than one headline average: active exposure separately from known completed-sale exposure, with withdrawn listings shown but not counted as sales. Add the number of new listings during the same period and note any boundary changes. That would answer whether this month changed without pretending the live sample represents every Tokyo transaction.