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

SSRN, ISSN: 1556-5068
2021
  • 1
    Citations
  • 2,091
    Usage
  • 4
    Captures
  • 0
    Mentions
  • 6
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
    • Citation Indexes
      1
  • Usage
    2,091
    • Abstract Views
      1,705
    • Downloads
      386
  • Captures
    4
  • Social Media
    6
    • Shares, Likes & Comments
      6
      • Facebook
        6
  • Ratings
    • Download Rank
      154,852

Article Description

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.

Bibliographic Details

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