I’m sense-checking a Bengaluru sample priced from ₹38,410,000 to ₹57,620,000, mostly warehouses. The typical listing has remained visible for 82 days. My working impression is that renovated or ready-to-use properties move quickly, while the rest sit and eventually get price cuts. Does maintenance really explain that split, or am I overlooking financing, seller motivation or neighbourhood differences?
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Bengaluru warehouses: is condition really driving the 82-day divide?
- Thread starter luca.east
- Start date
GreenTimber
Property manager
Condition is plausible, but “no longer visible” is not necessarily a completed sale. Some properties may be withdrawn, relisted or moved between agents. I’d separate confirmed completed sales from disappeared listings before concluding that renovated stock moves faster.
How wide is your Bengaluru boundary, and what counts as renovated? A usable roof, drainage, loading access and suitable services matter more to a warehouse buyer than fresh paint. If very different locations and levels of work are in one condition category, the 82-day figure could hide several markets.
I’d track the sample as a fixed group rather than repeatedly looking at whatever is currently advertised. For each property, note first-seen date, price changes, removal date and whether the outcome was sold, withdrawn or unknown. Also count new listings entering the bracket. Otherwise fresh stock can make the market look slower or faster without any real change.
One addition: divide condition into cosmetic work and operational work. Buyers can often tolerate an unattractive building if the required work is predictable. Uncertainty about what must be repaired, and how much disruption that creates, may be the larger obstacle.
I’m not convinced maintenance is the main cause. Seller motivation and initial pricing can produce exactly the same pattern. A well-kept warehouse at an ambitious price can sit, while an imperfect one priced with the work already reflected can attract interest. Compare the first asking price with the eventual outcome, not just renovated versus unrenovated.
Buyer financing could also distort the comparison. Do your notes distinguish vacant properties from occupied ones, or buyers using finance from those who are not? Even where two buildings look similar, uncertainty over condition or use can lengthen a financed buyer’s timeline. That may leave the cleaner listings with a larger pool of buyers able to proceed.
Price-cut timing may reveal seller intent. A reduction soon after listing suggests a different situation from a cut only after months of testing the market. I’d record days to the first cut and whether the listing vanished shortly afterward. Sellers who do not need to transact can remain visible for a long time regardless of condition.
That’s a useful distinction. I’d also note whether a cut follows signs of buyer interest or simply a lack of enquiries, if an agent will say. The former can mean the price was close but negotiations failed; the latter may point to a more fundamental mismatch in location, condition or expectations.
Neighbourhood boundaries need tightening before drawing a citywide conclusion. “Bengaluru” on a listing can group properties that buyers would not regard as substitutes. Plot the warehouses and compare only those competing for the same type of buyer, with similar access and surrounding use. The overall 82-day midpoint may then break into much clearer clusters.
gentle_plan
Real estate agent
Recent completed sales would be the most useful comparison, but advertised prices alone will not provide them. I’d ask agents about a small number of removed listings and classify each answer carefully: completed, under negotiation, withdrawn or unknown. Don’t turn silence into a sale. That exercise may also show whether apparently quick properties were genuinely renovated or merely presented better.
Putting the suggestions together, a workable sheet would include location, first asking price, current price, first-seen date, first price-cut date, condition category, occupancy, financing mentioned, removal date and confirmed outcome. Keep uncertain fields blank rather than guessing. After that, compare time on market within the same area and price band. If the condition gap remains, the maintenance theory becomes much stronger.
And keep new-listing volume as the denominator. If ready-to-use warehouses are rare, a few quick removals can look like a strong trend even though the sample is tiny. I’d wait for several confirmed outcomes and compare them with withdrawn stock before treating 82 days as a reliable picture of the Bengaluru market.