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Why is Intermediating Houses so Difficult? Evidence from iBuyers

Author

Listed:
  • Greg Buchak
  • Gregor Matvos
  • Tomasz Piskorski
  • Amit Seru

Abstract

We examine frictions in dealer intermediation in durable consumer goods markets through the lens of “iBuyers,” technology-driven entrants that facilitate transactions via online platforms and algorithmic pricing. iBuyers provide liquidity to households by bypassing the lengthy household-to-household sale process and earn a positive gross spread. However, their intermediation is limited to relatively liquid and easierto- value homes. We build and calibrate a dynamic search model with intermediaries facing adverse selection to quantify the economic frictions in this market. The central trade-off is that while providing liquidity requires fast transactions, this leads to less accurate valuations and exposes intermediaries to adverse selection. iBuyer technology offers a limited middle ground, enabling fast transactions with limited information loss, but it works best for liquid, easy-to-value homes. We then use our model to explore intermediation in durable goods markets, adjusting key asset and market properties based on (i) informational asymmetry, (ii) market liquidity, and (iii) the benefits of search driven by subjective value dispersion. Illiquid and hard-to-price assets, like homes, experience less intermediation, especially if underutilized during the process. In contrast, the greater homogeneity and easier pricing of goods like cars, along with their higher liquidity due to mobility, may help explain why intermediation in these markets has historically been higher.

Suggested Citation

  • Greg Buchak & Gregor Matvos & Tomasz Piskorski & Amit Seru, 2020. "Why is Intermediating Houses so Difficult? Evidence from iBuyers," NBER Working Papers 28252, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28252
    Note: AP CF IO PE PR
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    Cited by:

    1. Runshan Fu & Ginger Zhe Jin & Meng Liu, 2022. "Does Human-algorithm Feedback Loop Lead to Error Propagation? Evidence from Zillow’s Zestimate," NBER Working Papers 29880, National Bureau of Economic Research, Inc.
    2. Michael J. Seiler & Liuming Yang, 2023. "Gun‐ownership disclosure and localized home prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(6), pages 1399-1436, November.
    3. Brendan Daley & Thomas Geelen & Brett Green, 2024. "Due Diligence," Journal of Finance, American Finance Association, vol. 79(3), pages 2115-2161, June.
    4. Michael J. Seiler & Liuming Yang, 2023. "The burgeoning role of iBuyers in the housing market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 721-753, May.
    5. Kristopher Gerardi & Franklin Qian & David Hao Zhang, 2024. "Mortgage Lock-in, Lifecycle Migration, and the Welfare Effects of Housing Market Liquidity," FRB Atlanta Working Paper 15, Federal Reserve Bank of Atlanta.
    6. David M. Harrison & Michael J. Seiler & Liuming Yang, 2024. "The Impact of iBuyers on Housing Market Dynamics," The Journal of Real Estate Finance and Economics, Springer, vol. 68(3), pages 425-461, April.
    7. Paul Goldsmith‐Pinkham & Kelly Shue, 2023. "The Gender Gap in Housing Returns," Journal of Finance, American Finance Association, vol. 78(2), pages 1097-1145, April.

    More about this item

    JEL classification:

    • G0 - Financial Economics - - General
    • G2 - Financial Economics - - Financial Institutions and Services
    • G5 - Financial Economics - - Household Finance
    • L0 - Industrial Organization - - General
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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