Chicago one-bed student housing: are active listings distorting my 86-day figure?

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NiaRose

First-time buyer
Founding Member
I’m trying to work out whether Chicago’s one-bed student-housing market actually changed this month. In the $380,000–$570,000 range, the listings still online have been sitting for roughly 86 days. Most of the apparent outliers seem connected with transaction fees.

Does recent completed-sale activity support that picture, or is the 86-day figure mainly an active-listing bias? Asking-price data is easy to find, but I’m struggling to make a sensible comparison with completed deals.
 
The active listings are likely skewing it. Anything priced well and sold quickly disappears from that group, while stale stock remains and gets older. Also, 86 days online is not necessarily 86 days “to find a buyer” if those properties have not sold. I’d compare recent completions with withdrawals and current listings rather than treating the active group alone as the market.
 
How are you defining student housing and the Chicago area? A one-bed marketed to students can still compete with ordinary apartments, and moving a neighbourhood boundary could change a small sample substantially. I’d also check whether relisted properties have had their marketing time reset, otherwise the apparent 86 days may not be comparable across listings.
 
I’m not convinced the outliers can be attributed mostly to transaction fees without looking at condition and financing. A lower-priced unit needing work may sit longer even if its fees look acceptable. Conversely, a motivated seller can cut early and sell before an otherwise similar property. The timing of reductions matters almost as much as the latest asking price.
 
One practical approach is to build a fixed cohort: record everything newly listed within the same boundaries and price range, then track whether each listing sells, remains available, gets reduced or is withdrawn. That avoids mixing fresh stock with properties already carrying months of exposure. Keep condition and any stated fees in separate columns rather than assuming either explains the result.
 
Fees may still be part of the explanation, especially when buyers compare the total cost rather than just the headline price. But enelson’s point stands: you need like-for-like properties before drawing that conclusion. I’d also note the first price cut date. A seller waiting a long time to adjust is showing different motivation from one who reacts quickly.
 
Completed sales bring their own timing issue because they reflect properties marketed earlier, not just demand this month. I’d use them, but alongside new-listing volume and withdrawn stock. If new supply rises while completions stay similar, active days can increase without proving that comparable homes suddenly became much harder to sell.
 
That helps. I was treating the current listings as though they represented completed marketing periods, which they clearly do not. I’ll rerun it with fixed neighbourhood boundaries, separate newly listed and relisted stock, and track reductions, withdrawals and completions independently. I’ll also avoid assigning the outliers to fees until condition and financing differences have been accounted for.
 
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