Johannesburg new-build flats: does 92 days signal financing pressure?

A

AishaSlate

Homeowner
Established
Waiting for softer sellers could help, but it could also leave me choosing from a larger pool of flats I still would not buy. My Johannesburg sample mainly covers new-build units asking from ZAR 3,567,000 to ZAR 5,351,000, and a typical advert remains visible for 92 days.

I suspect borrowing costs contribute to the split between quick deals and stale stock, although seller motivation and repeated adverts within one development may explain some of it. At street level, would you start with completed prices, reductions and withdrawals, or first separate the data by neighbourhood, development and new-listing volume?
 
Financing may be part of it, but 92 days alone cannot establish that. With new builds, several listings can represent similar units in one development, and an advert may stay up while different units are marketed. Group your sample by development and address first. Otherwise one slow project could distort the whole picture.
 
Which neighbourhoods are included, and are all the flats completed and ready to occupy? A city-wide sample can hide major differences between nearby areas. Pre-completion marketing could also make the visible period look longer than the time a finished unit has genuinely been available.
 
I’d separate the questions. Completed sales indicate what buyers ultimately paid, while withdrawn listings show where sellers failed to meet the market—or simply changed plans. Neither automatically explains financing. Track asking-price changes and disappearance dates, then see whether the stale properties share condition, layout or development rather than just price.
 
I wouldn’t dismiss the financing theory. At this price level, a buyer’s monthly commitment can change meaningfully even when the asking price has barely moved. But motivated sellers and developers may respond differently: one cuts the price, another offers no visible reduction and waits. Your sample needs some measure of seller motivation.
 
The neighbourhood boundaries are probably doing more work than the broad Johannesburg label suggests. Two flats at similar prices may compete for quite different buyers if their immediate surroundings, access and building condition differ. I’d make the map smaller before drawing conclusions from the listing count.
 
Also, what does “typical” mean here—median, average or simply the most common range? And how did you treat a property that disappeared and later returned? If relisted stock looks new in your notes, both the 92-day figure and the apparent rise in new listings could be misleading.
 
A workable split might be: genuinely new listing, continuing listing, reduced listing, withdrawn listing and returned listing. Then add one row per actual flat or development rather than per advert. After a month or two, you should be able to see whether supply is growing or merely being recycled.
 
There’s another selection issue in “not many I would buy.” The appealing, correctly priced flats may leave quickly, so the remaining online sample naturally becomes dominated by compromised or ambitiously priced stock. That would create a gap between quick sales and stale listings even if buyer financing were unchanged.
 
Agreed on selection bias, although completed sales have their own timing problem: they describe earlier negotiations, not necessarily today’s mood. I’d still collect them where available, but compare like with like—same development, similar size and condition—rather than treating every sale in the price bracket as equivalent.
 
For practical next steps, shortlist five flats you would genuinely consider and five stale ones you would reject. Record the exact reasons: price, condition, layout, location, completion status or something else. If the rejected group shares obvious defects, financing is probably not the main explanation. If the groups look comparable, seller expectations and buyer funding become stronger possibilities.
 
One addition: note the first price cut, not just the current asking price. A reduction after a long quiet period says something different from a property launched at a realistic figure. You could also ask agents whether units were withdrawn, sold or simply paused, while treating those answers as context rather than proof.
 
I’d avoid choosing between “buy now” and “wait” from the 92-day figure alone. Clean the duplicates, narrow the neighbourhoods, separate completed from unfinished stock, and follow price changes on the few flats you would actually own. If suitable units still disappear quickly, waiting for a broad market discount may not help; if they linger and cut, you have grounds to negotiate.
 
You must log in or register to reply here.
Back
Top