Why are similar-priced San Francisco warehouses moving at different speeds?

[JulesLinden]

114 days is the typical time visible in my saved sample, yet some San Francisco warehouse listings vanish much sooner. Asking prices run from $216,000 to $324,000, so price alone does not explain the split.

Buyer financing is one possibility, particularly if properties with similar prices differ in condition or permitted use. But I may also be mixing neighbourhoods, seller expectations and withdrawn listings with genuine sales. How would you separate those effects? I would like to track completed transactions, new-listing volume and the timing of price reductions rather than treating every disappearance as a sale.

 

[zane_homes]

First separate “no longer listed” from “sold.” A withdrawn warehouse can look like a quick sale in saved-search notes, while a completed transaction gives you a usable price and timeline. I’d make three columns: sold, withdrawn and still active. Then record the first price cut rather than only the current asking price.

 

[GoodSignal]

What does “warehouse” mean within this sample? Are these comparable standalone buildings, small units, or properties with materially different condition and permitted use? At this price range, one major repair issue or a difficult layout could overwhelm the effect of financing costs. The neighbourhood labels may also hide very different blocks.

 

[dev_keys]

I’m not convinced financing is the main explanation yet. If it were, I would expect the whole bracket to slow in a more consistent way. The split could instead reflect unrealistic initial pricing: good stock sells, while compromised properties remain visible until the seller cuts or withdraws them. The timing of reductions against the 114-day figure would be revealing.

 

[zane_homes]

That is fair, but financing can still produce an uneven result. Two similarly priced properties may not be equally financeable if their condition, occupancy or intended use differs. I’d compare completed sales only after matching those basics. Also note whether stale listings return with new photos or a changed description, because that can reset the visible marketing history without changing the underlying property.

 

[noor_grove]

A citywide sample gives more observations, but tight block-level groups give better comparisons. I would not rely entirely on either approach, because a very small cluster can make one unusual warehouse look like a market pattern.

Start by dividing the records into a few defensible geographic groups, then match properties on condition, configuration and use. Within each group, record the original and current asks, first reduction, days visible and whether the outcome was sold, withdrawn or still active. If financing still lines up with the slow group after those filters, the theory gains weight. If the split follows particular blocks or compromised buildings, funding probably was not the main cause.