Sydney: sense-checking listings around A$782,800

P

plantsAndBirch

Seller
A citywide average offers context, but comparing only within our two preferred neighbourhoods seems more relevant—and may leave too few genuine matches. Our Sydney sample centres on A$782,800, ranges from A$626,200 to A$939,400 and consists mainly of villas. The typical listing has been visible for 26 days.

I initially thought energy performance might explain why some sell quickly while others linger. A well-positioned villa in poorer condition could easily be a counterexample, especially if buyer financing or ownership arrangements differ.

Which street-level factors would you use to match recent completed sales: villa type, bedroom count, condition, ownership structure, main-road position or price-cut timing? I also want to exclude withdrawn and relisted stock from the days-on-market comparison.
 
Start with recent completed sales inside each neighbourhood, then narrow by villa type, size and condition. Asking prices only show seller expectations. Also, 26 days visible is not necessarily 26 days until sale; withdrawn, relisted and duplicated properties can distort that figure.
 
Which two neighbourhoods, and how are you drawing their boundaries? A main road, school catchment or a few blocks can split buyer demand. I’d also want to know bedroom count and whether all the villas have comparable ownership arrangements before treating them as one group.
 
I wouldn’t make energy performance the leading explanation yet. It could be condition, layout, noise, parking or an ambitious asking price. Unless your notes compare genuinely similar properties and isolate the energy-related differences, that theory is doing too much work.
 
Buyer financing could also create the pattern. A property may attract interest but still sit if buyers near their borrowing limit cannot bridge the seller’s expectation. That matters across a bracket as broad as A$626,200 to A$939,400.
 
Track withdrawals separately from completed sales. A stale listing disappearing does not tell you whether it sold, changed agents, paused, or returned with a different presentation. Counting every disappearance as a transaction would make the faster end of your sample look stronger than it is.
 
A simple table would help: first-seen date, original ask, current ask, status, neighbourhood, property attributes and condition. Add completed price only when known. You can then compare the two areas without letting the A$782,800 citywide headline dictate the result.
 
One more distinction: compare completed prices with the asking prices that were current when buyers acted, not merely today’s active listings. Otherwise you are mixing seller hopes from one group with achieved outcomes from another.
 
How many new listings entered while you were measuring those 26 days? If one neighbourhood had a burst of fresh stock, older properties may look unusually stale simply because buyers gained alternatives. The typical age alone misses that competition.
 
I disagree that the citywide number is completely useless. It can be a rough plausibility check, provided it never substitutes for local comparisons. If one neighbourhood’s matched villas sit far from A$782,800, that gap is a prompt to investigate rather than proof of mispricing.
 
Seller motivation may explain some price-cut timing. Two similar villas can follow different paths if one seller is prepared to wait and another wants certainty. You cannot observe motivation directly, but repeated reductions or a withdrawal can be useful clues when interpreted cautiously.
 
On cuts, record both the amount and when it happened. A reduction after a short test is different from one after a long unsuccessful campaign. Don’t reset the clock when the advertised price changes.
 
Energy performance is still worth recording, just not as a catch-all. Note whatever comparable information is actually available, alongside orientation, visible condition and likely upgrade needs. If those details are missing for most listings, the data cannot support a strong energy conclusion.
 
The range itself may be swallowing the neighbourhood signal. A villa near A$626,200 probably should not be treated as interchangeable with one near A$939,400 without strong evidence that size, condition and location are comparable. Split the sample before looking for a single explanation.
 
I’d also separate listings by sales campaign format. Different formats can make “days visible” mean different things, particularly when a campaign has a scheduled decision point. The useful comparison is between properties marketed in broadly similar ways.
 
Completed sales can lag the market you are currently watching. Use the most recent ones available, but read their campaign dates as well as their completion dates. A completed result may reflect buyer conditions from earlier than the active listings.
 
Matched pairs might be more informative than averages: two nearby villas with similar accommodation, but different condition or energy-related features. If the better-performing one consistently moves sooner or achieves more, the hypothesis becomes more credible. One pair would still be anecdotal.
 
What exactly does “visible for 26 days” mean in your notes: first appearance anywhere, first time you recorded it, or uninterrupted time under the same listing? Clara’s relisting point matters because those definitions can produce very different answers.
 
That definition also affects duplicate advertisements. If the same property appears under more than one entry, count the property once and preserve the earliest confirmed date. Otherwise both listing volume and time-on-market calculations become noisy.
 
Pulling the suggestions together: define the two neighbourhood boundaries, deduplicate the villas, segment by key attributes and condition, then log new listings, cuts, withdrawals and completed results. Only after that test whether energy information explains any remaining difference.
 
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