Delhi monthly property snapshot — April 2026

E

emery_leases

First-time buyer
Established
I’m deciding whether the April 2026 summary can fairly lead with a decline for Delhi mixed-use buildings. Current indications are 44 days on market, asking-price movement of -7.9%, and visible financing sensitivity around ₹23,380,000. These are discussion inputs, not an official index. Before updating the summary, I’d like completed-sale evidence plus clarity on inventory, neighbourhood coverage and the sample definition.
 
I would hold off on presenting -7.9% as a Delhi-wide decline. Is that movement month over month, year over year, or from each listing’s original ask? Also, does the 44-day figure include withdrawn and relisted properties? Those definitions could change the interpretation substantially.
 
Was the calculation based on the same properties observed over time, or on the median asking price of different April listings? If it is the latter, a shift toward cheaper stock could produce the drop without individual sellers cutting prices.
 
One more sample question: what qualifies as mixed-use here? A building with a shop and residence should not automatically be pooled with a larger office-and-retail property. At minimum, the summary needs a property-type breakdown.
 
I agree about the missing definitions, but I wouldn’t discard the figures entirely. They can still be a useful directional signal if the heading says “asking-price sample” and the sample size, coverage and comparison period appear beside them. The problem is certainty, not necessarily the observation itself.
 
Completed sales would help, but please distinguish agreement timing from whatever completion date is used in the evidence. Otherwise an April comparison may reflect negotiations from an earlier period. Asking prices and completed prices should also remain in separate columns rather than being blended.
 
A single Delhi figure is too broad for the claim being considered. Could the data be split by neighbourhood, even if some rows have to be marked as too thin to interpret? The 44 days may be driven by where the sampled buildings are located rather than by mixed-use property generally.
 
Inventory movement is the missing companion to days on market. If listings disappeared, were they sold, withdrawn, expired or relisted under a new entry? Treating every disappearance as a sale would make the market look faster than the available evidence supports.
 
What does “financing sensitivity around ₹23,380,000” mean operationally? More price reductions near that level, longer marketing periods above it, or simply fewer listings? Until the observed behaviour is stated, that phrase invites readers to infer more than the snapshot demonstrates.
 
Please show the price-band mix for both comparison periods. Even with unchanged neighbourhood coverage, April could contain a larger share of lower-priced buildings. That would pull down the aggregate ask while leaving prices within each band relatively stable.
 
A compact supporting table would resolve much of this: neighbourhood, mixed-use subtype, asking-price band, first-seen date, latest-seen date, status and whether the entry appears to be a relisting. Then add completed-sale evidence separately where available. Readers could see exactly what feeds each figure.
 
Completed-sale information may arrive later than listing data, so the April 2026 snapshot could be labelled provisional rather than delayed indefinitely. Give it a stated revision date and preserve the original figures when updating, so later evidence doesn’t silently rewrite the first release.
 
The subtype point from smallvictory_aya deserves priority. Shop-plus-residential, office-plus-retail and other combinations respond to different pools of buyers and occupants. If there are not enough observations to publish each separately, disclose the mix instead of implying one uniform category.
 
Can the underlying source links be attached to each figure rather than only listing general references? A link plus the date accessed would make later revisions easier to understand, particularly if an asking price or listing status changes after the April capture.
 
I’d be careful not to split the neighbourhoods so finely that every movement becomes noise. Start with the broadest defensible groups, display the number of observations in each, and suppress percentage changes where the sample is visibly thin. Granularity is useful only when the data can support it.
 
A sensible first revision would keep all three current indicators but narrow their wording: define the comparison behind -7.9%, explain the treatment of relistings in the 44 days, and replace “financing sensitivity” with the actual observed pattern around ₹23,380,000. Then add neighbourhood and subtype tables as evidence permits.
 
Also retain a count of listings that cannot be matched confidently across periods. That addresses the relisting problem without pretending every ambiguous entry can be resolved. If the revision log says what changed, why it changed and when, the provisional April snapshot can remain useful without being mistaken for an index.
 
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