Sydney serviced apartments: is 110 days a real signal or listing bias?

readTheKite

Property investor
I’m comparing serviced apartments in Sydney between A$1,386,000 and A$2,079,000. The active listings in my sample have been online for roughly 110 days, with most outliers appearing to involve transaction fees.

Should that affect our offer strategy, or is the sample distorted by stale listings? I’d like to compare recent completed sales, but citywide averages seem irrelevant to the two neighbourhoods we’re considering.
 
Active listings alone will exaggerate the time needed to sell because the quick sales have already disappeared. Start with completed deals in the same neighbourhoods, then add withdrawn properties separately. A withdrawal is useful evidence of resistance, but it is not a sale at the asking price.
 
How are you drawing the two neighbourhood boundaries? A few streets can move a property into a different buyer pool. Also, does your 110-day figure cover only comparable serviced apartments, or are different conditions and building types mixed together?
 
I wouldn’t assume transaction fees caused the long marketing periods without checking timing. Were those fees visible from day one, or did they only become apparent later? The outliers might instead share poor condition, optimistic pricing or less motivated sellers.
 
One more distinction: record the date of the first price cut, not just the original listing date. A property sitting for 80 days and then cutting its price before selling tells a different story from one accepted near its initial ask.
 
I partly disagree that 110 days is simply distorted and therefore unhelpful. It describes the stock a buyer can actually choose from today. It just doesn’t prove that a correctly priced new listing will also take 110 days.
 
A small table would settle much of this: first listed date, original ask, each reduction, final result if known, withdrawal date, condition and stated fees. Keep the two neighbourhoods separate. Even a modest clean sample is better than a broad Sydney average.
 
What changed my view was noticing how few fresh comparables may be entering the two neighbourhoods. An old-looking pool is not automatically proof that apartments cannot sell; it may simply reflect limited new supply. Conversely, if plenty were listed recently and only the long-running ones remain, survivor bias becomes a stronger explanation.

I’d verify monthly listing additions and removals alongside the first-listed dates in the proposed table. That gives the 110-day figure some context before it affects the offer.
 
Do you know whether the stalled listings have anything in common that could narrow the financed-buyer pool? I wouldn’t draw a lending conclusion yourself, but it is worth asking the selling agents what kind of buyer interest they have actually received.
 
Seller motivation may explain more than the calendar. Look for repeated cuts, relisting with altered presentation, or no price movement at all. Those patterns can help separate a seller testing the market from one who may respond to a supported offer.
 
Another possible issue is the wide price band. A$1,386,000 and A$2,079,000 may attract different comparisons even within one neighbourhood. I’d split the sample into tighter groups before treating 110 days as a single figure.
 
Thanks—all fair points. My 110-day number came mainly from listings still online, so I was giving the survivors too much authority. I’m now separating the neighbourhoods, tightening the price comparisons and marking withdrawals and price-cut dates. I’ll also verify exactly what the transaction fees cover rather than treating them as the cause.
 
That should produce a more usable range. For offer strategy, I’d pay closest attention to completed sales resembling the specific apartment, then use the stale listings only as evidence about seller expectations. Don’t average the two groups together.
 
Condition still needs its own column. A renovated or better-presented apartment and one needing work can sit in the same price bracket but face very different buyer reactions. Otherwise a condition discount may be mistaken for a neighbourhood trend.
 
The phrase “transaction fees” may be hiding several different things. Are these included in the advertised figure, added separately, or merely mentioned in the listing? Until that is consistent across the sample, I’d flag those properties rather than exclude them.
 
Completed sales also arrive with a timing lag, so they won’t perfectly describe this month’s mood. Pair them with current new listings and recent withdrawals. That gives you three views: what cleared, what failed to clear and what sellers are trying now.
 
When contacting agents, ask factual questions: when did serious interest begin, was the price changed, did an earlier deal fail to complete, and is the seller working to a timeline? Answers may be incomplete, but inconsistencies can still identify listings needing more investigation.
 
At this point I’d treat 110 days as a description of Theo’s current search results, not a Sydney selling-time estimate. The next decision should come from the closest completed deals, adjusted cautiously for condition, fees and boundaries, with stale stock used to test how much negotiating room might exist.
 
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