Long Run Expectations, Learning and the U.S. Housing Market
Daniel Tortorice ()
No 85, Working Papers from Brandeis University, Department of Economics and International Businesss School
This paper examines key facts about the U.S. housing market. The price to rent ratio is highly volatile and significantly autocorrelated. Returns on housing are positively autocorrelated. The price to rent ratio is negatively correlated with future returns on housing and future rent growth. Finally, housing returns exhibit significant time varying volatility. I show that a benchmark rational expectations general equilibrium asset pricing model is inconsistent with these facts. I modify the model in two ways to improve its fit with the data. First, I allow for pricing frictions so prices adjust slowly to their fundamental value. Second, I assume the agent does not know if housing fundamentals, captured by rental flows, are stationary or non-stationary and has changing beliefs depending on how well each model fits the current data. I find that these modifications allow the model to increase the volatility of the price to rent ratio and to match the autocorrelation of housing returns. The price to rent ratio then negatively forecasts returns and rent growth. Finally the model generates time varying volatility consistent with the data.
Keywords: Learning; Expectations; Housing Demand; Asset Pricing (search for similar items in EconPapers)
JEL-codes: D83 D84 G12 R21 E03 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge and nep-ure
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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP85.pdf First version, 2015 (application/pdf)
Journal Article: Long-Run Expectations, Learning and the US Housing Market (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:brd:wpaper:85
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