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The Housing Market(s) of San Diego

Author

Listed:
  • Tim Landvoigt
  • Monika Piazzesi
  • Martin Schneider

Abstract

This paper uses an assignment model to understand the cross section of house prices within a metro area. Movers' demand for housing is derived from a lifecycle problem with credit market frictions. Equilibrium house prices adjust to assign houses that differ by quality to movers who differ by age, income and wealth. To quantify the model, we measure distributions of house prices, house qualities and mover characteristics from micro data on San Diego County during the 2000s boom. The main result is that cheaper credit for poor households was a major driver of prices, especially at the low end of the market.

Suggested Citation

  • Tim Landvoigt & Monika Piazzesi & Martin Schneider, 2012. "The Housing Market(s) of San Diego," NBER Working Papers 17723, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17723
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    References listed on IDEAS

    as
    1. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 2016. "Understanding Booms and Busts in Housing Markets," Journal of Political Economy, University of Chicago Press, vol. 124(4), pages 1088-1147.
    2. Jack Favilukis & Sydney C. Ludvigson & Stijn Van Nieuwerburgh, 2017. "The Macroeconomic Effects of Housing Wealth, Housing Finance, and Limited Risk Sharing in General Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 125(1), pages 140-223.
    3. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, September.
    4. Patrick Bayer & Fernando Ferreira & Robert McMillan, 2007. "A Unified Framework for Measuring Preferences for Schools and Neighborhoods," Journal of Political Economy, University of Chicago Press, vol. 115(4), pages 588-638, August.
    5. Nobuhiro Kiyotaki & Alexander Michaelides & Kalin Nikolov, 2011. "Winners and Losers in Housing Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(2‐3), pages 255-296, March.
    6. Caplin, Andrew & Leahy, John, 2014. "A graph theoretic approach to markets for indivisible goods," Journal of Mathematical Economics, Elsevier, vol. 52(C), pages 112-122.
    7. Atif Mian & Amir Sufi, 2009. "The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis," The Quarterly Journal of Economics, Oxford University Press, vol. 124(4), pages 1449-1496.
    8. Satyajit Chatterjee & Burcu Eyigungor, 2009. "Foreclosures and house price dynamics: a quantitative analysis of the mortgage crisis and the foreclosure prevention policy," Working Papers 09-22, Federal Reserve Bank of Philadelphia.
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    More about this item

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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