I’ve been tracking Dubai properties priced from AED 572,500 to AED 858,800, and my sample currently points to roughly 17 days to find a buyer. The outliers mostly line up with property-tax entries, but I’m not sure that relationship is meaningful. Should recent completed sales confirm the 17-day figure, or am I overinterpreting listings that remain online? I’m also wondering how to handle withdrawn or relisted stock.
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Dubai listings at 17 days in the AED 572,500–858,800 range
- Thread starter GreenSignal
- Start date
If the 17 days mainly comes from listings still online, it is their current age rather than time to finding a buyer. Completed deals give you an outcome, while withdrawn listings stop you from counting only the successes. I’d separate completed, active and withdrawn properties before drawing a conclusion.
If the area grouping is too broad, the 17-day result could lead you to read a normal local difference as a tax effect. It is tempting to keep all homes in the AED 572,500–858,800 band together because the sample is larger, but condition and demand can change within a short distance. I’d split the data into smaller neighbourhoods and comparable property types first, then check whether the same outliers remain.
Completed sales are important, but I wouldn’t use them alone because they reflect listings that started earlier. The active stock may be showing a newer change. Anika’s neighbourhood point is key: group listings by their start period and area, then compare the outcomes. Seventeen days can still describe the age of current stock, just not completed selling time.
I’m cautious about attributing the outliers to property tax without more detail. Is property tax a distinct field in your data, or are you inferring it from listing descriptions? It may simply overlap with neighbourhood, condition or asking price. See whether the relationship survives after matching those factors.
Add the original listing date, first price-cut date and final outcome where known. A property that sells 17 days after a reduction but spent much longer at the first price is a different case from one priced correctly on day one. Relistings also need a cumulative timeline rather than a fresh clock.
HappyEmber
Homeowner
One extra check raises another question: did a large share of the active homes enter the market only recently? That would pull the apparent age toward 17 days even if demand had not changed. For each start period, compare the number added with the numbers sold and withdrawn. That should show whether the young sample reflects faster outcomes or simply a fresh wave of stock.
Seller motivation could explain part of the spread. Two similar properties may follow different timelines if one seller accepts an early offer and another holds to the asking price. Buyer financing can also affect how quickly an apparent match becomes a completed deal, although that may not be visible in the listing data. Mark both as unknown unless you have something concrete.
Agreed on seller motivation, but I’d be especially careful with financing. Unless the data explicitly identifies it, assigning slow deals to financed buyers could create a story that the sample cannot support. Price changes and withdrawal dates are observable; financing should stay in a separate unknown category.
A simple table should resolve most of this: neighbourhood, property type, condition, initial price, latest price, listing date, first reduction, withdrawal or completion date, and current status. Calculate active age separately from elapsed time for completed sales. That will also expose duplicates and relistings before they distort the 17-day result.
Jin’s distinction is the cleanest way to present it. I’d report two figures: current age for active listings and elapsed listing time for recent completed sales, with withdrawn stock shown separately. Then break each group down by narrow neighbourhood and condition. If the samples become too small after that split, describe the pattern as tentative rather than treating 17 days as a Dubai-wide result.