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Area of Science:

  • Computational Social Science
  • Urban Economics
  • Digital Marketing Analytics

Background:

  • Online platforms generate vast user-generated data.
  • Understanding user search behavior is crucial for market analysis.
  • Traditional economic models are increasingly applied to digital interactions.

Purpose of the Study:

  • To apply a gravity framework to user-generated data from an online housing market.
  • To analyze the inflow and outflow of user engagement (hits) between municipalities.
  • To differentiate search patterns between serious and recreational property searchers.

Main Methods:

  • Utilized a gravity framework model.
  • Analyzed user-generated data from a large online housing platform.
  • Categorized users into 'serious' and 'recreational' searchers based on behavior.

Main Results:

  • Gravity model successfully describes geographic patterns of user engagement between municipalities.
  • Recreational search is more localized around the user's municipality than serious search.
  • Differences in search localization are attributed to border effects, not distance effects.

Conclusions:

  • Physical location significantly influences economic behavior even in virtual environments like online housing markets.
  • The gravity framework provides a robust method for analyzing digital geographic search patterns.
  • Distinguishing user intent (serious vs. recreational) refines the understanding of location-based search behavior.