NY studios: are monthly charges distorting the market picture?

A

AmaraLane

Property investor
I’m comparing New York studios listed from $744,000 to $1,116,000. The snapshot I have shows 0.2% movement and roughly 21 days on market, but negotiated discounts seem to vary much more with condition.

My decision is whether to pay more for a finished unit with lower service charges or pursue a discounted property that needs work. I suspect those recurring charges explain more of the spread than headline demand does. Citywide averages feel useless for the two neighbourhoods we like. Are recent completed sales, new listings or withdrawn stock giving the clearest signal?
 
One clarification: I’m not treating 21 days as proof that a listing is fresh or fairly priced. I’m trying to work out what evidence would actually change an offer—especially the timing of any price cuts and whether apparently comparable studios have materially different monthly charges.
 
Before comparing discounts, are these condos, co-ops or a mixture? That distinction could make the monthly figures and buyer pool hard to compare directly. I’d also define the neighbourhood boundaries very tightly. Two studios a few blocks apart may look like local comparables while belonging to different building markets.
 
Completed sales should carry more weight than the 21-day figure, provided the units really match on building type, condition and recurring charges. Days on market can become noisy when stock is withdrawn, relisted or has already had a cut.

I would track the original asking price, each reduction and the eventual sale price. That sequence says more about seller motivation than the final discount alone.
 
Condition also needs splitting into cosmetic work versus a unit requiring substantial work. Buyers can price those very differently even when the photographs make both look merely “dated.” For each comparable, I’d record condition and monthly charges separately rather than assuming one explains the other.
 
I’m not convinced service charges are necessarily the main driver. Financing can narrow the buyer pool, while a motivated seller can create a discount that has little to do with the building’s monthly costs. And 0.2% movement is too small to guide an individual offer without knowing what changed in the mix of listings.
 
Luca’s caveat is fair. A simple grid might resolve it: same property type, tightly drawn area, similar size, condition category, monthly charges, financing constraints, first list price, cut dates and final sale price. If the charge effect remains after those differences are controlled informally, Ibrahim’s theory becomes much stronger.
 
New-listing volume and withdrawn stock are useful together. More new listings may suggest choice is improving, but not if older sellers are quietly withdrawing instead of accepting lower offers. I’d also note whether cuts happen early or only after several weeks; late cuts can indicate that the original price was testing demand rather than reflecting it.
 
For the actual offer, I’d choose three to five genuinely close completed sales and prepare separate adjustments for condition and recurring charges. Then compare that result with current competing listings. If the two point in different directions, the active listings show competition, but the completed sales show what buyers and sellers have actually accepted.
 
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