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The Effect of Listing Price Strategy on Transaction Selling Prices


While true underlying home values are expected to be randomly distributed, actual residential listing prices tend to be highly clustered. Particularly, more than 75 % of the homes in our sample are associated with a round or “just below” round asking price. This study provides a theoretical and empirical examination of how the thousands digit in a home’s asking price is related to the final transaction price relative to its true underlying value. Our findings suggest that, on average, homes listed using a “just below” pricing strategy are associated with the greatest discount negotiated relative to the asking price. However, the higher initial degree of list overpricing reflected in “just below” pricing compared with other strategies more than offsets the greater discount. Therefore, “just below” is the most effective pricing strategy for the seller in terms of a greater dollar yield relative to value. These empirical findings have economic significance and are robust across both “buyer” and “seller” housing markets, new versus existing homes, and across multiple home price ranges.

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  1. 1.

    As a robustness test, we also examine the effect of the ten-thousands and hundreds digits on discount and the degree of overpricing. We find no evidence that these digits are related to the negotiated price discount or settlement price relative to the underlying value of the house. For brevity, these results are omitted from this version of the paper.

  2. 2.

    In Australia, coins smaller than 5 cents have not been used since 1992, yet still today, prices continue to be listed in fractions (such as $3.98) for which proper change cannot be given.

  3. 3.

    Only 3 % of prices end in a “1, 2, 3, 4, 6, 7, or 8.”

  4. 4.

    Datasets in both studies ended before both the dramatic run up and precipitous decline in the most recent real estate markets.

  5. 5.

    If the seller employs a real estate agent to market and sell their home, the real estate agent will often provide their own opinion to the seller about the home’s fair market value.

  6. 6.

    Constrained sellers, such as underwater sellers, may be forced to price at a certain level and only accept an offer that is above this threshold, regardless of the value of their home.

  7. 7.

    The reservation price for a home is often a mental threshold (Seiler et al. 2008) and therefore sellers are more likely to set a round reservation price. As a result, a reservation price such as $190,000 is more likely than $188,000, which is more likely than a reservation price of $188,392, for example.

  8. 8.

    For robustness, we repeat this analysis for the ten-thousands and hundreds digit. We find no evidence on the relation between these two order digits and discount that can be supported with statistical significance. For brevity we exclude these results from this version of the paper.

  9. 9.

    For example, if a particular house was on the market for 78 days before it was sold, TOM will be assigned a value of 2.56 (78/365*12).

  10. 10.

    We also used other specifications instead of Eq. (5), and the overall results were largely unaffected. For brevity, we exclude all other specification from this version of the paper.

  11. 11.

    As a robustness check, alternative hedonic model specifications were tested and generally yielded similar results.

  12. 12.

    Once again, for robustness, we repeat this analysis for the ten-thousands and hundreds digits. We find no evidence on the relation between these two order digits and the degree of overpricing that can be supported with statistical significance. For brevity, we exclude these results from this version of the paper.

  13. 13.

    The standard deviation of the thousands digit number frequency for new construction is 4.95 % compared with 11.24 % for existing homes.


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We would like to thank Oded Palmon, Ben Sopranzetti, Paul Anglin, and Tom Springer for comments on earlier drafts of this study. We also wish to thank the Virginia Association of Realtors® (VAR) for access to their sample of homeowners for the experimental component of this study. We specifically acknowledge the special assistance of Stacey Ricks at VAR. We would also like to thank Real Estate Information Network (REIN) in Hampton Roads, Virginia for providing transactions data for the empirical aspect of this study. All errors and omissions remain our own.

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Correspondence to Michael J. Seiler.

Appendix: Definition of Variables

Appendix: Definition of Variables


Living area of the house measured in square feet


Age of the house measured in years


Number of bedrooms in the house


Number of bathrooms in the house


Number of half bathrooms in the house


Dummy variable indicating the year during which the transaction took place. i can take an integer value between 1994 and 2011.

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Beracha, E., Seiler, M.J. The Effect of Listing Price Strategy on Transaction Selling Prices. J Real Estate Finan Econ 49, 237–255 (2014).

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  • List pricing strategies
  • Time on the market
  • Residential real estate