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

Document Type

Article

Publication Title

Annals of Operations Research

Abstract

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 R2 (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.

Publication Date

12-11-2025

Publisher

Springer Nature

Volume

Vol.363

Link to Full Text
Request Access

Share

COinS