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Housing Search Frictions: Evidence from Detailed Search Data and a Field Experiment

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  • Peter Bergman
  • Eric W. Chan
  • Adam Kapor

Abstract

We randomized school quality information onto the listings of a nationwide housing website for low-income families. We use this variation and data on families' search and location choices to estimate a model of housing search and neighborhood choice that incorporates imperfect information and potentially biased beliefs. We find that imperfect information and biased beliefs cause families to live in neighborhoods with lower-performing, more segregated schools. Families underestimate school quality conditional on neighborhood characteristics. If we had ignored this information problem, we would have estimated that families value school quality relative to their commute downtown by half that of the truth.

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  • Peter Bergman & Eric W. Chan & Adam Kapor, 2020. "Housing Search Frictions: Evidence from Detailed Search Data and a Field Experiment," NBER Working Papers 27209, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27209
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    Cited by:

    1. Fernando V. Ferreira & Maisy Wong, 2022. "Neighborhood Choice After COVID: The Role of Rents, Amenities, and Work-From-Home," NBER Working Papers 29960, National Bureau of Economic Research, Inc.
    2. Dionissi Aliprantis & Hal Martin & Kristen Tauber, 2020. "What Determines the Success of Housing Mobility Programs?," Working Papers 20-36R, Federal Reserve Bank of Cleveland, revised 19 Oct 2022.
    3. Patrick Kline & Evan K. Rose & Christopher R. Walters, 2024. "A Discrimination Report Card," American Economic Review, American Economic Association, vol. 114(8), pages 2472-2525, August.
    4. Aliprantis, Dionissi & Martin, Hal & Phillips, David, 2022. "Landlords and access to opportunity," Journal of Urban Economics, Elsevier, vol. 129(C).
    5. Chan, Eric W. & Fan, Yulian, 2023. "Housing discrimination in the low-income context: Evidence from a correspondence experiment," Journal of Housing Economics, Elsevier, vol. 59(PA).
    6. Mikhail Golosov & Michael Graber & Magne Mogstad & David Novgorodsky, 2024. "How Americans Respond to Idiosyncratic and Exogenous Changes in Household Wealth and Unearned Income," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 1321-1395.
    7. Robert Ainsworth & Rajeev Dehejia & Cristian Pop-Eleches & Miguel Urquiola, 2020. "Information, Preferences, and Household Demand for School Value Added," NBER Working Papers 28267, National Bureau of Economic Research, Inc.
    8. Fan, Ying & Fu, Yuqi & Yang, Zan & Chen, Ming, 2024. "Search frictions in rental markets: Evidence from urban China," China Economic Review, Elsevier, vol. 83(C).
    9. Jiarui Liu, 2021. "Sequential Search Models: A Pairwise Maximum Rank Approach," Papers 2104.13865, arXiv.org, revised Nov 2021.
    10. Fan, Ying & Fu, Yuqi & Yang, Zan & Chen, Ming, 2023. "Search Frictions in Rental Markets: Evidence from Urban China," Working Paper Series 23/11, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.

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    More about this item

    JEL classification:

    • I0 - Health, Education, and Welfare - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • R0 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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