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<p>Recent statistical research in real estate markets focuses on the spatio-temporal dynamics of house prices. Bayesian methods are common but often slow for a large dataset in that regard. We propose a divide-and-conquer (D&amp;C) approach, partitioning the data into subsets and applying Gaussian process models in parallel. The results are combined using the Wasserstein barycenter technique to obtain global parameters. This method allows for multiple observations per spatial and time unit. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. A couple of our major findings is that different types of property command different levels of premiums across the region; and that lower carbon emissions correlate positively with house prices, though the magnitude of this premium has diminished over recent years. Overall, our proposed model offers a better fit than competing approaches, attaining the highest <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> (0.796) and lowest root mean squared error (0.211). Specifically, it improves the error by approximately 50% compared to traditional hedonic and temporal models, while achieving improvements of 28% over fixed-effects model, and improvement of 12% over a spatial model. Most importantly, the D&amp;C spatial variant achieves a 40-fold computational speed-up with minimal accuracy reduction. For unobserved properties, predictions remain highly reliable, achieving a mean absolute percentage error below 10% in over 98% of cases.</p>

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A divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London

  • 期刊论文

  • 发表日期：

    2025年11月12日

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  • Kapil Gupta,
  • Soudeep Deb

摘要

Recent statistical research in real estate markets focuses on the spatio-temporal dynamics of house prices. Bayesian methods are common but often slow for a large dataset in that regard. We propose a divide-and-conquer (D&C) approach, partitioning the data into subsets and applying Gaussian process models in parallel. The results are combined using the Wasserstein barycenter technique to obtain global parameters. This method allows for multiple observations per spatial and time unit. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. A couple of our major findings is that different types of property command different levels of premiums across the region; and that lower carbon emissions correlate positively with house prices, though the magnitude of this premium has diminished over recent years. Overall, our proposed model offers a better fit than competing approaches, attaining the highest \(R^2\) (0.796) and lowest root mean squared error (0.211). Specifically, it improves the error by approximately 50% compared to traditional hedonic and temporal models, while achieving improvements of 28% over fixed-effects model, and improvement of 12% over a spatial model. Most importantly, the D&C spatial variant achieves a 40-fold computational speed-up with minimal accuracy reduction. For unobserved properties, predictions remain highly reliable, achieving a mean absolute percentage error below 10% in over 98% of cases.