FORECASTING SPATIALLY CORRELATED TARGETS: SIMULTANEOUS PREDICTION OF HOUSING MARKET ACTIVITY ACROSS MULTIPLE AREAS

Abstract

This study involved the development of an approach to forecast house prices and trading volumes across multiple areas simultaneously. Spatially correlated targets, such as house prices, can be predicted more accurately by leveraging the correlations across adjacent areas. A multi-output recurrent neural network, a deep learning algorithm specifically developed to analyze sequence data, was utilized to forecast the house prices and trading volumes in the four chosen study areas. The forecasting accuracy of future house prices in one of the four geographical areas clearly improved; this area was found to be a price-lagging area, and the forecasting accuracy of this area significantly increased by exploiting the information of a price-leading area. As for the prediction of trading volumes, the difference in performance between the multi-output recurrent neural network and conventional models was very small. The results of this study are expected to promote the use of deep learning to predict the housing market activity through a simultaneous forecasting framework.