I’m comparing Chicago student-housing listings priced from $688,000 to $1,032,000. The local snapshot shows +4.7% movement and roughly 26 days on market, yet the properties I saved are not moving together at all. Negotiated discounts appear to change sharply with condition. My working theory is that transaction fees and property-specific costs explain more of the spread than headline demand does. Does that fit what others are seeing? Please include the neighbourhood and whether the property is purpose-built student housing or another type rented to students.
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Chicago student housing: +4.7% movement and 26 days on market?
- Thread starter woodworksAndWorkshop
- Start date
I’d be cautious about attributing the spread mainly to transaction fees. Those costs may affect what a buyer can pay, but condition, financing options and seller motivation can produce much larger differences between apparently similar listings. Recent completed sales would be more useful than asking prices. Also count withdrawn properties; otherwise 26 days may describe only the stock that remains visible.
That’s fair. My saved group may be too broad, particularly around neighbourhood boundaries and what agents describe as student housing. Some need obvious work while others appear ready to operate, so one average is probably hiding several markets. How would you separate the sample without reducing it to only a handful of properties—condition first, exact location first, or property type first?
honest_chart
Property manager
Before splitting it, what exactly does the +4.7% measure: asking prices, completed prices, or listing volume? The same applies to 26 days—active listings and completed transactions answer different questions. I’d start with exact property type, then neighbourhood, then condition. Otherwise a renovated building near student demand can be compared with a conventional property that merely happens to have student tenants.
I disagree slightly on putting property type ahead of location. In Chicago, even the boundary used for a neighbourhood could change which listings appear comparable. Define a small area first, then separate purpose-built accommodation from other properties marketed toward students. After that, note the first price cut and whether it happened early or only after the listing had already gone stale.
rowan_rose
Property manager
A simple comparison sheet should expose the issue. For each listing, record exact area, property type, initial and current asking price, condition, days listed, price-cut date, whether it was withdrawn or relisted, and any visible financing complication. Keep completed sales in a separate section. Then compare properties within the same subgroup rather than trying to explain the whole $688,000–$1,032,000 range with one market figure.
Daniel’s layout is the practical way forward. I’d add seller motivation where it is actually known, but leave it blank rather than guessing. Fatima, once you split the properties that way, test the transaction-fee theory against pairs with similar location, type and condition. If the negotiated spread remains large, financing or motivation becomes the stronger explanation. Until the +4.7% measure is defined, I wouldn’t use it to support a wider United States comparison.