Backcasting, Nowcasting, and Forecasting Residential Repeat-Sales Returns: Big Data meets Mixed Frequency

Matteo Garzoli, Alberto Plazzi and Rossen I. Valkanov
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Matteo Garzoli: University of Lugano
Rossen I. Valkanov: University of California, San Diego (UCSD) - Rady School of Management

No 21-21, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: The Case-Shiller is the reference repeat-sales index for the U.S. residential real estate market, yet it is released with a two-month delay. We find that incorporating recent information from 71 financial and macro predictors improves backcasts, now-casts, and short-term forecasts of the index returns. Combining individual forecasts with recently-proposed weighting schemes delivers large improvements in forecast accuracy at all horizons. Additional gains obtain with mixed-data sampling methods that exploit the daily frequency of financial variables, reducing the mean square forecast error by as much as 13% compared to a simple autoregressive benchmark. The forecast improvements are largest during economic turmoils, throughout the 2020 COVID-19 pandemic period, and in more populous metropolitan areas.

Keywords: Real estate; Case-Shiller; MIDAS; Forecasting; Big Data (search for similar items in EconPapers)
JEL-codes: C22 C53 R30 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-for and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2121

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