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Bidding Heterogeneity, Signaling Effect and its Implications on House Seller’s Pricing Strategy

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  • Hua Sun
  • Seow Ong

Abstract

This paper adopts a search model to examine individual seller’s pricing strategy under two market conditions: first, sellers have reference-dependent utility; second, the housing market is less heterogeneous, such as multi-unit residential market. Acknowledging the fact that units in the same building serve as close substitutes for each other, we show that within the same building, a recently realized transaction price may generate two signaling effects on potential buyers’ willingness to pay. First, the average willingness to pay among buyers is positively correlated with the observed price level, which we interpret as a spatio-temporal autocorrelation effect; second, after observing a price signal, the heterogeneity of potential buyer’s willingness to pay decreases, which urges the seller to mark down the asking price. Using a geo-coded dataset on condominium transactions in Singapore, we find significant evidence of the reference dependence effect. Meanwhile, we find that the spatio-temporal autocorrelation among the units in the same building is significantly higher than with the units in the neighboring buildings. Furthermore, after controlling for the autocorrelation effect, we find that sellers tend to mark down their asking prices if a recent transaction has occurred within the same building, consistent with the prediction from decreasing bidding heterogeneity. We also propose a measure on potential buyers’ outside option and find consistent evidence that the realized transaction price decreases with the outside option. Finally, using a proxy measure on the exogenous arrival rate of potential buyers, we show evidence that sellers’ asking prices tend to increase with the arrival rate, as predicted by the model. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Hua Sun & Seow Ong, 2014. "Bidding Heterogeneity, Signaling Effect and its Implications on House Seller’s Pricing Strategy," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 568-597, November.
  • Handle: RePEc:kap:jrefec:v:49:y:2014:i:4:p:568-597
    DOI: 10.1007/s11146-013-9409-0
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    References listed on IDEAS

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    1. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
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    6. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    7. Pace, R Kelley & Barry, Ronald & Clapp, John M. & Rodriquez, Mauricio, 1998. "Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 15-33, July.
    8. Hwang, Min & Quigley, John M., 2002. "Price Discovery in Time and Space: The Course of Condominium Prices in Singapore," Department of Economics, Working Paper Series qt7ph788mn, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
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    Cited by:

    1. Zhaohui Li & Qiang Li & Hua Sun & Li Sun, 2022. "Diffused effort, asset heterogeneity, and real estate brokerage," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(3), pages 707-742, September.
    2. Marcus T. Allen & Justin D. Benefield & Ronald C. Rutherford, 2023. "Co-Listing Strategies: Better Transaction Outcomes?," The Journal of Real Estate Finance and Economics, Springer, vol. 67(3), pages 517-544, October.
    3. Ka Shing Cheung & Julian TszKin Chan & Sijie Li & Chung Yim Yiu, 2021. "Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach," JRFM, MDPI, vol. 14(9), pages 1-22, September.

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