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Optimal portfolio choice with predictability in house prices and transaction costs

Author

Listed:
  • Corradin, Stefano
  • Fillat, José L.
  • Vergara-Alert, Carles

Abstract

We generalize the classic Grossman and Laroque (1990) (GL) model of optimal portfolio choice with housing and transaction costs by introducing predictability in house prices. As in the GL model, agents only move to more expensive (cheaper) houses when their wealth-to-housing ratios reach an optimal lower (upper) boundary. However, in our model, these boundaries are time-varying and depend on the dynamics of the expected growth rate of house prices. We find that households moving to a more expensive house in periods of high expected growth in house prices have significantly lower ex-ante wealth-to-housing ratios than those moving in periods of low expected growth. We also find that the share of wealth invested in risky assets is lower during periods of high expected growth in house prices and that it is higher right before moving during periods of low growth. The main implications of the model are robust to tests using household level data from the PSID and SIPP surveys. JEL Classification: G11, D11, D91, C61

Suggested Citation

  • Corradin, Stefano & Fillat, José L. & Vergara-Alert, Carles, 2012. "Optimal portfolio choice with predictability in house prices and transaction costs," Working Paper Series 1470, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20121470
    Note: 1103497
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    References listed on IDEAS

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    More about this item

    Keywords

    durable goods; housing returns predictability; optimal housing consumption and investment; transaction costs;
    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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