Bangkok two-bed villas: +8.3% movement, 84 days on market—wait or negotiate?

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path.slow

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I’m tracking two-bedroom villas across Bangkok from THB 39,890,000 to THB 59,830,000. The set shows +8.3% movement and roughly 84 days on market. Listing volume is up, but few properties are genuinely appealing, and discounts vary sharply with condition. Would you wait for better stock or negotiate now? Please identify the neighbourhood and property type in any comparison.
 
Eighty-four days alone would not persuade me that buyers have leverage. Withdrawn and relisted properties can distort that figure, while duplicate listings make supply look deeper than it is. I’d first separate genuinely new villas from recycled stock, then compare the eventual cuts.
 
What exactly does the +8.3% measure—asking-price growth, a change in your tracked listings, or completed-sale prices? Those would support very different conclusions. Also, how tightly have you drawn the neighbourhood boundaries?
 
That distinction matters. If it is movement in asking prices, one ambitious new listing can shift a small sample without showing what buyers will pay. Completed sales would be more useful, although condition and plot differences still make villa comparisons messy.
 
I’d also split the range. A THB 39,890,000 buyer and a THB 59,830,000 buyer may not be considering the same alternatives, even if both properties have two bedrooms. One overall days-on-market number could hide two separate patterns.
 
I’m not fully convinced supply is the main driver. Financing and the size of the qualified buyer pool can affect negotiation just as much. More advertisements do not necessarily mean more sellers who are able or willing to accept a lower figure.
 
Fair caveat. Seller motivation is probably the missing variable: occupied home, empty property, unfinished renovation, or speculative listing. They can sit for the same 84 days and respond completely differently to an offer.
 
David, are these concentrated around one part of Bangkok or spread across several areas? Grouping, say, different parts of Sukhumvit with more suburban villa locations would make the supply conclusion difficult to test.
 
Bedrooms are a weak comparison point here. Plot size, internal area, parking, access, age and renovation scope could explain the spread before neighbourhood demand enters the picture. I’d build matched groups rather than treating all two-bedroom villas as peers.
 
Another useful date is the first price cut, not just the listing date. A seller holding firm for 70 days and cutting on day 71 tells a different story from one making small reductions every few weeks.
 
Can you distinguish cancelled listings from completed sales? If a property disappears, marking it as sold would overstate activity. If it reappears with new photos or another agent, marking it as new would understate its true marketing time.
 
Condition-dependent discounts may actually support the opposite interpretation. Buyers could be paying for move-in-ready stock while rejecting projects, meaning the apparent surplus is concentrated in homes needing work rather than spread across the whole villa market.
 
Exactly. “More listings, fewer worth buying” describes low-quality supply, not necessarily excess supply. I’d track acceptable and unacceptable properties separately, with a short written reason for each rejection. That prevents the rough stock from setting your expectations for the good stock.
 
There is also a timing problem with asking-price cuts: the visible reduction may already follow an earlier private negotiation that failed. It is useful evidence of motivation, but not a guaranteed discount available to the next buyer.
 
For neighbourhood boundaries, use the buyer’s actual substitution area. If someone would not realistically choose a villa across a particular commute or access divide, those listings should not be in the same comparison merely because the address still says Bangkok.
 
I would ask agents the same neutral questions for every property: original launch date, whether it was previously listed elsewhere, reason for sale, current occupancy and work needed. Answers may be incomplete, but inconsistencies can still help identify recycled or unmotivated stock.
 
Useful pushback. The +8.3% is movement in asking levels within my tracked set, not completed-sale appreciation, so I was giving it too much weight. I’m also combining several Bangkok areas. I’ll split the list by realistic substitute area, price band and condition, and separately flag withdrawals, relistings and first-cut dates.
 
That makes the picture much clearer. Once the areas are split, the sample may become small, so avoid turning every change into a trend. A simple property-by-property timeline will probably tell you more than a single percentage.
 
Any update after reorganising it? I’d be interested to know whether the 84-day figure survives when relisted villas keep their original date, especially in the move-in-ready group.
 
A practical spreadsheet could use one row per physical property, not per advertisement: first-seen date, latest asking price, cut dates, condition, area grouping, duplicate agents, withdrawal date and confirmed outcome if known. That should expose whether “new volume” is actually new.
 
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