Optimal portfolio choice with predictability in house prices and transaction costs
AbstractAre 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.
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Bibliographic InfoPaper provided by IESE Business School in its series IESE Research Papers with number D/948.
Length: 96 pages
Date of creation: 03 Feb 2012
Date of revision:
Portfolio choice; predictability; house prices; household finance;
Other versions of this item:
- 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.
- 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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