Optimal portfolio choice with predictability in house prices and transaction costs
AbstractWe 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
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Bibliographic InfoPaper provided by European Central Bank in its series Working Paper Series with number 1470.
Date of creation: Sep 2012
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Other versions of this item:
- 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.
- Stefano Corradin & José L. Fillat & Carles Vergara-Alert, 2010. "Optimal portfolio choice with predictability in house prices and transaction costs," Risk and Policy Analysis Unit Working Paper QAU10-2, Federal Reserve Bank of Boston.
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
- D91 - Microeconomics - - Intertemporal Choice - - - Intertemporal Household Choice; Life Cycle Models and Saving
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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