I put 10 years of Denver Metro housing data through a Random Forest machine learning model – 24 market variables – to find out.
The model explained 88.6% of the variation in DIM with an average error of just 4.3 days.
๐๐ฒ๐ฟ๐ฒ’๐ ๐๐ต๐ฎ๐ ๐ฟ๐ผ๐๐ฒ ๐๐ผ ๐๐ต๐ฒ ๐๐ผ๐ฝ:
๐๐ฎ๐๐ ๐บ๐ผ๐ป๐๐ต’๐ ๐๐ฎ๐๐ ๐ถ๐ป ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐ – the market moves in momentum, not sudden shifts
๐๐น๐ผ๐๐ฒ-๐๐ผ-๐๐ถ๐๐ ๐ฅ๐ฎ๐๐ถ๐ผ – when buyers stop bidding aggressively, homes sit. Pretty simple.
๐ฅ๐ผ๐๐ป๐ฑ๐ถ๐ป๐ด ๐ผ๐๐ ๐๐ต๐ฒ ๐๐ผ๐ฝ ๐ฑ: transaction volume by financing type. How many homes are actually closing โ and how buyers are paying โ carries more weight than you might expect.
๐ป๐๐๐’๐ ๐คโ๐ฆ ๐กโ๐๐ก ๐๐๐ก๐ก๐๐๐ :
Cash buyers skip appraisal contingencies entirely and close fastest. Conventional buyers are financially stronger and close with less friction. When both are active, the market is confident and liquid โ homes move.
When those numbers fall, friction rises. More FHA and VA transactions mean stricter appraisals, longer underwriting, and more deal fallouts. Homes sit longer – not because of price, but because of transaction complexity.
The model is reading financing mix as a proxy for market confidence and buyer quality. That signal is powerful.
๐๐ป๐ฑ ๐๐ต๐ฎ๐ ๐ฏ๐ฎ๐ฟ๐ฒ๐น๐ ๐บ๐ผ๐๐ฒ๐ฑ ๐๐ต๐ฒ ๐ป๐ฒ๐ฒ๐ฑ๐น๐ฒ?
๐ ๐ฒ๐ฑ๐ถ๐ฎ๐ป ๐๐น๐ผ๐๐ฒ๐ฑ ๐ฃ๐ฟ๐ถ๐ฐ๐ฒ
๐ฃ๐ฟ๐ถ๐ฐ๐ฒ ๐ฝ๐ฒ๐ฟ ๐ฆ๐พ๐๐ฎ๐ฟ๐ฒ ๐๐ผ๐ผ๐
Price alone doesn’t predict how long a home sits. The conditions surrounding the price do.
๐๐ผ๐ฟ ๐ฏ๐๐๐ฒ๐ฟ๐: watch the Close-to-List Ratio. When it starts dropping, you have more negotiating room.
๐๐ผ๐ฟ ๐๐ฒ๐น๐น๐ฒ๐ฟ๐: the market’s current momentum matters more than your list price. Timing and positioning are everything.
Denver Metro (SMDRA) | 10 years of data | 89.5% model accuracy
