Phoenix warehouse listings: why are similar prices producing very different outcomes?

A

anika_vale

Real estate agent
Established
The Phoenix listings I saved are not moving together at all. They range from $744,000 to $1,116,000, are mostly warehouses, and the typical listing in my sample has been visible for 57 days.

My suspicion is that local supply is making apparently comparable properties behave very differently. Before drawing that conclusion, what should I separate out—recent completed sales, neighbourhood boundaries, condition, financing, or seller motivation? I’m especially interested in what the street-level picture says that the headline listing count misses.
 
Start by splitting the warehouses into genuinely comparable pockets rather than treating Phoenix as one market. Two buildings at similar prices may serve different buyers because of location, access, configuration and condition. I’d also compare the active listings with completed sales; asking prices alone can make a slow property look comparable to one that was correctly positioned.
 
How are you counting those 57 days? If it is only the current listing period, withdrawn and relisted stock may appear newer than it really is. I’d note original appearance, any disappearance, return date and price changes separately. That could explain part of the gap without relying on local supply alone.
 
Emma’s point matters, although I wouldn’t automatically treat every relisting as continuous exposure. A property can return with changed terms or after material work. Keep both numbers: days in the current campaign and total time observed. Also mark the date of the first price cut; a stale listing that just adjusted may be entering a different phase.
 
I’m not convinced supply is the main explanation yet. Within that price bracket, condition and usability could dominate. A warehouse needing substantial work is not economically equivalent to one ready for a buyer’s intended use, even if the asking prices are close. Are your notes capturing condition consistently, or mainly price, location and days visible?
 
Agreed that condition needs its own column, but seller motivation is harder to read from a listing. I’d avoid assigning motives based on wording. Observable behaviour is safer: repeated cuts, a withdrawal, unchanged pricing despite long exposure, or a quick response to competing stock. Those actions do not prove the reason, but they help distinguish flexible sellers from aspirational pricing.
 
There’s another selection problem: saved listings show what remained available long enough to be noticed. Quick sales can be underrepresented, while stale properties accumulate in the sample. A rolling record of new listings and removals would be more informative than looking only at the current saved set.
 
Yes, and removals should not all be recorded as sales. Some may be withdrawn or expire without a completed transaction. Unless a completed sale can be confirmed, I’d label the outcome unknown. Otherwise the apparently fast-moving group could be overstated.
 
Buyer financing may also separate the quick and slow stock. Not because one can assume how each buyer will fund a purchase, but because building condition and deal structure can affect whether a buyer can proceed on schedule. Add a simple field for any visible condition or transaction complication, then see whether those listings stay exposed longer.
 
I’d be careful not to let that field become speculation. Record only what the listing actually discloses, and use “not stated” rather than filling gaps. The same applies to neighbourhood boundaries: use a consistent map definition. Marketing labels can make nearby properties sound as if they belong to different submarkets—or the reverse.
 
A practical worksheet could have: first observed date, current-listing date, asking price history, broad location, building condition as described, new/withdrawn/unknown status, and confirmed completion where available. Then group by location and condition before calculating a typical exposure period. With a modest sample, the individual cases may still tell you more than one overall 57-day figure.
 
One disagreement: I wouldn’t discard the 57-day figure. It is useful as a description of the sample, just not as a Phoenix-wide market measure. Keep it as the starting point, then compare the listings above and below it. The outliers may reveal whether price cuts, condition or a particular pocket is driving the split.
 
Also take snapshots at fixed intervals. New-listing volume matters because a property can become stale without changing at all if several close substitutes arrive. Conversely, a withdrawal can reduce apparent supply without indicating stronger demand. Tracking the flow should make the street-level picture much clearer than a single count.
 
The cleanest next step is to test three explanations separately: location, physical condition and seller behaviour. Only after those are controlled would I attribute the remaining difference to local supply. If recent completed sales cluster around certain building types or pockets while similar asking listings sit elsewhere, that would be more persuasive than days visible on its own.
 
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