u/Dangerous_Crab8719

Body

I found four 1BRs in JVC that looked pretty similar on the listings.

Same general size, similar asking prices, same bedroom count.

739-785 sqft, 950k-1.15M ask, all 1BR. Three buildings; two of the four were in Binghatti Aurora.

Not identical comps, but close enough that a normal portal search would put them in the same shortlist.

Then I reconstructed their listing histories.

The history told a different story.

What happened

Unit	Sqft	Exposure	Listings	Ask	DLD	Gap to Ask
Pantheon Elysee II	753	1d	1	950k	900k	-5.3%
Binghatti Lavender	739	42d	2	~970k	856k	-11.8%
Binghatti Aurora	785	5d	1	1.03M	1.025M	-0.5%
Binghatti Aurora	782	4d	1	1.15M	1.05M	-8.7%

Gap to Ask = DLD transaction price vs. the last recorded asking price.

The two Auroras are what really caught my attention.

Same building. Almost the same size. One listing each. Four vs. five days of exposure.

Yet:

1.03M to 1.025M (-0.5%)

versus

1.15M to 1.05M (-8.7%)

Same building, same rough size, almost the same exposure.

Very different outcomes.

It made me wonder how useful a building-wide asking price really is for one specific unit.

Then there's Lavender.

739 sqft. ~970k ask. Looked normal on the listing card.

Then the history:

42 days on market. 2 listings. Ask barely moved. DLD at 856k.

Comparable 1BRs in the same building closed across a wide range, roughly 840k-1.05M.

So I'm not saying 856k was "the market price."

The interesting part is that this particular unit spent 42 days around 970k before closing at 856k.

That building-level range doesn't tell you why this unit took 42 days to sell.

Pantheon is the quick contrast.

753 sqft. 950k ask. One listing. About a day of exposure. DLD at 900k (-5.3%).

Even a fresh listing doesn't necessarily close near ask.

This is where I started thinking about competition differently.

It's not only about which apartments look similar today.

It's also about which comparable units were likely competing with yours while it was on the market, and what happened to those units afterward.

A listing is a snapshot.

Competition is a history.

So I tried something.

For one listing, I'm trying to piece together:

• which comparable units were likely competing with it during its exposure
• how long they'd really been on market
• whether the ask moved, or the listing disappeared and came back
• which ones transacted
• where they closed vs. the last ask

The question I'm testing isn't "what does a similar apartment cost today?"

It's "what happened to the apartments this unit was competing with?"

If you currently have a 1BR for sale in JVC, drop the listing URL in DM, or comment with building + ask + sqft.

You don't need to send me any transaction details. The current listing is enough for me to start.

I'll pick 1-2 and try to reconstruct the competitive set here in the thread.

I'm more interested in whether the method is useful than in getting more listings.

Sanity check for agents: does this match how you think about pricing a listing, or am I over-reading listing history?

One question

If you have a specific 1BR for sale today and the seller asks "why 950k?", what would you want on the screen first?

Current competitive set: which comparable units are competing with this one now Real exposure: how long those units have been on market Exit history: how comparable units that already sold ended, and where they closed vs. ask

Reply with a number or combination, e.g. 2 -> 3 -> 1.

107 high-resolution listing → transaction matches · Jun–Aug 2026

Following up on my listing-age post here — I started on the next question: when a listing disappears from a portal, what actually happened to it? Still early days, the match set keeps growing. Genuinely curious if this is the kind of breakdown you'd want to see more of.

What actually happens to an apartment after its listing disappears from a portal?

Was it sold? If so, for how much?

And can you even tell whether a transaction registered later belongs to the same unit that was on the market at a specific price?

Harder than it sounds. One tower can hold hundreds of similar apartments. Sizes differ. Listings come and go. The transaction registry on its own won't tell you: this is the exact listing you saw a month ago.

So I went the other way and narrowed the sample hard.

I only kept cases where one registered transaction was the only candidate I could find, and the unit size differed by no more than 1%.

That left 107 high-resolution matches.

The dataset is still growing, so these numbers will shift as new matches land. Here's the current snapshot.

What happened to the price?

Median observed gap across all 107 matches: -8.6%

Beds	Matches	Median ask	Median sold	Median gap
1BR	67	1.20M	1.05M	-8.6%
2BR	25	1.80M	1.57M	-9.5%
3BR	11	2.50M	2.28M	-9.7%

1BR units account for 63% of the matched sample, so the overall median price level should not be read as a "typical Dubai apartment" price.

Roughly similar across beds in this slice: -8.6% for 1BR, -9.5% for 2BR, -9.7% for 3BR.

Across all 107 matches:

• median asking price: 1.40M AED
• median transaction price: 1.275M AED
• median observed gap: -8.6%

That's about 125K AED between the two medians. Context matters though — the gap swings a lot unit to unit and area to area.

The median is only the beginning.

Out of 107 cases:

• 15 — transaction above the observed asking price
• 2 — exactly at asking
• 90 — below asking
• 46 (43%) — observed gap of -10% or more
• 32 (30%) — observed gap of -15% or more

The spread is wide.

Among recent matches:

• Sobha Creek Vistas: -25.0%
• Creek Palace: -22.7%
• Hills Park: -15.9%
• Lake City Tower: +2.4%

One other match, Ontario Tower, came in at +10.5% above the observed asking price.

The absolute amounts can get big too. One matched case: 12.5M AED observed asking vs 8.6M AED registered transaction. 3.9M AED difference.

Another reason I don't want to collapse this into one "discount" number.

This isn't listing price → automatic discount. Some deals land near asking. Some go above. Some show very large gaps.

It also varies by area

The 107 matches aren't evenly spread across Dubai. Largest groups:

Area	Matches	Median observed gap	<=-10%
Jumeirah Village Circle	28	-5.5%	29%
Dubai Marina	10	-13.1%	60%
Business Bay	10	-10.9%	60%
Dubai Sports City	9	-4.4%	33%
Dubai Hills Estate	7	-8.2%	43%

So -8.6% overall is not a Dubai-wide estimate. It's the median of this matched sample, which is also uneven by area.

Even within JVC it changes in a narrower cut: 22 one-bedroom matches had a median observed gap of -4.8%.

Maybe the interesting question isn't "What is Dubai's asking-to-sale gap?"

Maybe it's: how much does the gap move by area, building, unit type, and segment?

But this is not a "Dubai discount" estimate

I'm not saying apartments in Dubai sell 8.6% below asking. Stronger claim than this supports.

-8.6% is the median observed gap within these matched cases. Not necessarily a negotiated discount. Doesn't prove asking price caused the transaction price. And 107 cases isn't a representative sample of the whole market.

What they do allow, more narrowly:

When you can link a specific listing to a specific registered transaction, the question from the start — what happened after the listing disappeared? — finally gets an observable answer, at least for that unit.

More interesting to me than another headline discount stat.

Next thing I want to look at: how much of the gap is area/building/unit type vs something specific to the individual listing?

If you had these 107 matches, what would you test next: area, building, unit type, listing duration — or the listing itself?

I've been collecting Dubai apartment listing snapshots since June. At some point I stopped treating every portal card as a brand new apartment and started trying to reconstruct chains for the same unit.

I'm not naming the portal or specific buildings. Not trying to call anyone out. I just want to talk about the pattern.

What this is: not a random sample of all Dubai inventory. These are live apartment-for-sale listings where I could reliably link cards into one chain — same tower, bedroom count, size range, same agent, no more than a 45-day gap between observations.

Snapshot: 13 Aug 2026. 8,835 live apartment cards → 559 reconstructed chains.

Important caveat: "earliest observed listing" is not the day the apartment first hit the market. It's just the first trace in my collection window.

Most chains had no price cut on record

Of the 559 chains, 493 (88%) had no recorded price reduction. That's the group that surprised me most.

For those 493:

Median current card age: 7 days

Median earliest observed point in the chain: 38 days

Median gap between those two: 17 days

52.5% had a gap over 14 days. 24.3% over 30 days. Only 3 apartments had a gap over 60 days. This isn't a handful of weird outliers.

Among these 493 chains, 58% had 2 cards, 12% had 3, and 31% had 4+ (max 22). For the 4+ group I treat that as chain complexity, not four clean relists — 134 of 151 had overlapping cards, not a simple take-down → repost sequence.

The part that got my attention

I also pulled out cases where one listing disappeared before another appeared for the same apartment. 169 sequential chains, still no recorded price cut.

Median current-card age: 26 days

Median earliest observed in chain: 49 days

Old card goes away. New card shows up. The new card looks much younger. The apartment doesn't.

I didn't see evidence in the chain that the apartment actually left the market.

A few live examples (one current card, no recorded price cut):

Downtown 2BR — 3 days on the card vs 63 days observed in the chain

JBR 2BR — 7 vs 65

Palm Jumeirah 1BR — 2 vs 61

Price changes look tied to exposure, not card age

When I split the 559 chains by whether price moved:

No cut (493): median current card 7 days, observed chain 38 days, gap 17 days

Price reduced (53): current 25 days, chain 48 days, gap 32 days

Price increased (13): current 26 days, chain 27 days, gap 4 days

Apartments with recorded cuts had a longer observed chain (48 days) than those without (38). Increases sat around 27 days with almost no gap. I'm not claiming causality — but price movement seems more closely associated with observed market exposure than with how fresh the current card looks.

Biggest gaps without a price cut (n≥8, median current → median observed):

Palm 10 → 54 · JBR 25 → 59 · JLT 11 → 40 · Dubai Hills 8 → 36 · Marina 18 → 43 · Downtown 20 → 44

Small gap doesn't always mean low relisting. Al Jaddaf 8 → 11, Majan 4 → 6 — multiple cards showed up close together.

The interesting bit isn't that listings disappear. It's that a listing can become young again while the apartment basically doesn't.

Question for the room: if you're a buyer, agent, or investor — does separating current listing age from observed exposure age actually tell you something useful? How would you use it?

Or is this just a data nerd rabbit hole, and the current listing is all that really matters?