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Defining Geographic Markets Through Choice Sets: An Empirical Method Applied to Real Estate Brokerage

Author

Listed:
  • Lindhe, Adam

    (Royal Institute of Technology (KTH))

  • Orrenius, Johan

    (Research Institute of Industrial Economics (IFN))

Abstract

We introduce a choice-set approach to defining markets and a novel method to empirically recover geographic markets using machine learning, Spatial and Categorical Bayesian Clustering (SCBC). SCBC leverages the identity of the seller for each observation to capture market structures in a novel way that is not captured by purely distance-based methods. Applied to real estate agents in Stockholm (Sweden), SCBC classifies sales more accurately than the baseline K-means algorithm. Finally, we investigate the correct number of clusters and find that the optimal number of clusters is close to the validation set based on industry knowledge.

Suggested Citation

  • Lindhe, Adam & Orrenius, Johan, 2026. "Defining Geographic Markets Through Choice Sets: An Empirical Method Applied to Real Estate Brokerage," Working Paper Series 1558, Research Institute of Industrial Economics.
    • Handle: RePEc:hhs:iuiwop:1558
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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