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Optimal portfolio choice with predictability in house prices and transaction costs

Author

Listed:
  • Corradin, Stefano

    (European Central Bank)

  • Fillat, Jose L.

    (Federal Reserve Bank of Boston)

  • Vergara, Carles

    (IESE Business School)

Abstract

Are housing returns predictable? If so, do households take them into account when making their housing consumption and portfolio decisions? We document the existence of housing return predictability in the U.S. at the aggregate, census region, and state level. We study a portfolio choice model in which housing returns are predictable and adjustment costs must be paid when a house is purchased or sold. We show that two state variables affect the agent's decisions: 1) her wealth-to-housing ratio; and 2) the time-varying expected growth rate of house prices. The agent buys (sells) her housing assets only when the wealth-to-housing ratio reaches an optimal upper (lower) bound. These bounds are time-varying and depend on the expected growth rate of house prices. Finally, we use household level data from the PSID and SIPP surveys to test and support the model's main implications.

Suggested Citation

  • Corradin, Stefano & Fillat, Jose L. & Vergara, Carles, 2012. "Optimal portfolio choice with predictability in house prices and transaction costs," IESE Research Papers D/948, IESE Business School.
  • Handle: RePEc:ebg:iesewp:d-0948
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    References listed on IDEAS

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    More about this item

    Keywords

    Portfolio choice; predictability; house prices; household finance;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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