Los Angeles studios at $152,000–$228,000: is 38 days meaningful?

That clustering point is important. If new listings arrive mainly at one end of the band, the average period can move even when individual properties behave exactly as before.
 
I would want several repeated observations. An early change should show persistence through new listings, price cuts, withdrawals and completed sales—not just one current-stock calculation.
 
Financing and condition should be considered together. A lower asking price does not necessarily widen demand if the property’s circumstances make the purchase more difficult for some buyers.
 
Clara’s question about completed sales also needs timing. A recently completed transaction may reflect an agreement reached well before July, so compare dates carefully rather than treating every completion as current sentiment.
 
And the vacant/non-vacant comparison needs enough observations on both sides. If one category contains only a handful, report the individual outcomes instead of leaning on an average.
 
What decision are you using this for—setting an offer, choosing when to list, or simply monitoring? The evidence threshold should be higher if money is about to depend on the conclusion.
 
There is a risk of overengineering this. For a practical buying decision, three genuinely comparable completed sales may be more useful than a large table of loosely similar studios.
 
I partly disagree. Completed sales are essential, but current cuts and withdrawals can reveal a change before completions catch up. Use both rather than choosing one dataset.
 
Could you report the number of properties behind the 38-day figure? Without the denominator, nobody can judge whether one long-running listing is distorting it.
 
I’d list withdrawals separately rather than treating them as failed sales. Some may return or leave for reasons unrelated to demand, so the status is informative but not conclusive.
 
The asking band also needs discipline over time. If a property enters after a reduction or exits after rising outside it, note that movement instead of silently changing the sample.
 
A further distinction is vacant at first advertisement versus becoming vacant later. If that information is unavailable, it is safer to say vacancy association rather than vacancy effect.
 
For condition, broad categories are probably enough. The aim is not to score finishes precisely, just to avoid comparing an apparently renovated studio with one requiring obvious work.
 
Listing language about occupancy can be incomplete or ambiguous. I would not build the main conclusion around vacancy unless the classification is consistently supported across the group.
 
Instead of only calculating an average, show the individual marketing periods in order. That makes an outlier visible without introducing another summary measure that hides the sample.
 
Agreed. Individual rows would also show whether price cuts occurred early or only after a long wait, which is more informative than the final 38-day figure.
 
Seller motivation is the hardest variable because it often cannot be observed directly. Actual pricing actions are better evidence than assumptions based solely on whether a unit is empty.
 
Try the analysis twice: once with every qualifying studio and once excluding the longest-marketed property. If the interpretation flips, the group is too fragile for a broad conclusion.
 
You could also compare vacant and occupied properties only after matching neighbourhood, price position and condition. Otherwise the exercise risks attributing every difference to vacancy.
 
My concern remains the neighbourhood boundary. A consistent city label is not enough if this month’s studios sit in materially different local settings from the next month’s group.
 
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