  Statistical research in real estate markets, particularly in understanding
the spatio-temporal dynamics of house prices, has garnered significant
attention in recent times. Although Bayesian methods are common in
spatio-temporal modeling, standard Markov chain Monte Carlo (MCMC) techniques
are usually slow for large datasets such as house price data. To tackle this
problem, we propose a divide-and-conquer spatio-temporal modeling approach.
This method involves partitioning the data into multiple subsets and applying
an appropriate Gaussian process model to each subset in parallel. The results
from each subset are then combined using the Wasserstein barycenter technique
to obtain the global parameters for the original problem. The proposed
methodology allows for multiple observations per spatial and time unit, thereby
offering added benefits for practitioners. 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. The
methodology provides insightful findings about the effects of various
amenities, trend patterns, and the relationship between prices and carbon
emissions. Furthermore, as demonstrated through a cross-validation study, it
shows good predictive accuracy while balancing computational efficiency.
