Delhi villas: is 98 days meaningful without micro-market sales data?

[finn_winter]

I’m trying to decide how much weight to give listing age before making offers in Delhi. My sample runs from ₹67,130,000 to ₹100,700,000, is mostly villas, and the typical listing has been visible for 98 days. I initially suspected energy performance might separate quick sales from stale stock, but the citywide average seems useless for the two neighbourhoods we like. What street-level evidence would you gather first: completed sales, price cuts, withdrawn listings, or something else?

 

[frame.able]

I wouldn’t make energy performance the main explanation yet. In that bracket, plot, exact street, condition and seller expectations could overwhelm it. Start with recent completed sales inside each neighbourhood rather than Delhi-wide figures, then compare those properties with the stale listings as closely as possible.

 

[teaAndBridge]

How did you calculate the 98 days? Is it continuous exposure under one listing, or could withdrawn and relisted properties appear newer than they really are? Also, are the two neighbourhood boundaries fixed in your sample? A few streets added or removed could change the picture substantially.

 

[NoorRose]

Completed prices matter, but so does the path to completion. Note the original asking price, each reduction and the gap between the last cut and disappearance from the market. You won’t always know whether disappearance means sold or withdrawn, so keep those in separate columns rather than treating every vanished listing as a transaction.

 

[EarlyBrick]

Before offers start going out, I would collect both completed prices and the current stock pattern. The trade-off is that sales provide firmer price evidence, while they may reflect negotiations made well before today’s owners decided whether to cut, withdraw or hold firm.

For a narrow Delhi villa search between ₹67,130,000 and ₹100,700,000, new listings and removals within the same fixed neighbourhood boundaries can show present seller pressure. I would not substitute them for sales, though. Use completed transactions to anchor value, then use the flow of new, reduced and withdrawn properties to judge whether a villa sitting for 98 days is genuinely negotiable.

 

[teaAndBridge]

That distinction helps. A listing that sits for 98 days without a cut says something different from one reduced twice, and both differ from a property withdrawn after a month. I’d also flag whether financing is likely to matter, because a buyer dependent on approval may evaluate price and condition differently from someone with fewer financing constraints.

 

[ames_miles]

Energy performance may be acting as a proxy for overall condition rather than driving the result by itself. A renovated villa can look more efficient while also needing less immediate work. Unless the listings describe energy-related features consistently, separate observable condition from energy claims; otherwise the comparison risks giving one vague field too much explanatory power.

 

[teaAndBridge]

A workable sheet would have one row per property and fields for exact micro-area, first-seen date, asking-price history, visible condition, whether it is new or relisted, and final status: active, apparently sold, or withdrawn. Then compare only genuinely similar villas. That should expose whether the 98-day figure is broad market drag or just a handful of mismatched properties.

 

[emery_leases]

Don’t overlook seller motivation. Two nearly identical homes can behave differently if one owner is prepared to wait and the other has a firm timeline. You probably won’t know the reason, but price-cut timing gives a clue: early, repeated reductions suggest a different stance from a seller holding the same ask for months.

 

[EarlyBrick]

One more caveat on boundaries: use the smallest area that still leaves enough comparable properties to be useful. Drawing it too broadly hides street differences; drawing it around only the preferred streets can leave you interpreting noise. I’d prepare a strict set for the two target neighbourhoods and a wider secondary set, clearly labelled rather than blended.

 

[emery_leases]

So the practical order seems to be: verify whether the 98 days includes relisting, split the two neighbourhoods, collect recent comparable outcomes, and track new, reduced and withdrawn stock separately. Only then test whether condition or energy-related features line up with faster movement. That avoids choosing the explanation first and forcing the listings to fit it.