• Homes that linger on the market tend to sell for significantly less than their listing price: 5 percent less after 2 months.
• Pricing too aggressively can mean a home stays on the market for longer, but homes which sell above list price don’t fly of the market any faster than homes that sell at the list price.

Sellers know that overpricing your home can lead to a drawn-out sale – likely with price cuts – while pricing at or below market value will lead to a speedier sale. But how severe is the penalty of an inflated price, and how strong is the speed boost of a lower one?

Zillow tracked all homes listed over the course of a year to put some numbers to the relationship.

We found that in the United States as a whole, homes that sold more or less as soon as they hit the market had sale prices that were only about 1 percent below list price, while homes on the market for about two months sold at 5 percent below (figure 1). And homes listed for the longest amount of time (eleven months on average) fared worst, selling at 12 percent below list price.

Some readers may find it more intuitive to think of this relationship as a function of list and sale price (figure 2).[1] Homes where the sale price was 10 percent below the list price spent five times as long on the market as homes that sold at the list price. However, homes where the sale price was nearly 10 percent above the list price spent about as much time on the market as homes that sold at the list price.

This means there could be a strong penalty for overpricing but no speed bonus for pricing below list – since there’s likely a limit on how quickly a home can sell. This pattern in the data could also could be the manifestation of a specific pricing strategy, where sellers hope to inspire bidding wars by pricing low and then setting later dates for reviewing offers.

It’s worth noting that these are empirically-derived relationships, straight from the data. We didn’t test for causality or control for other variables.

[1] Note that we didn’t just flip the axes; rather, we grouped the data into ten equally-sized groups by the percent difference between sale price and list price and computed the median days on market for each group.