From prediction to explanation for price–volume dynamics in real estate market

Abstract

Abstract Accurate and interpretable real estate forecasting is difficult because housing markets contain heterogeneous price trends, irregular transaction volumes and horizon-dependent price–volume interactions. This study proposes an explainable Kolmogorov–Arnold network (EX-KAN) framework for price forecasting with joint lagged price–volume inputs and competitive transaction-volume prediction. EX-KAN uses lagged price and volume information to forecast future market conditions and applies elasticity-based diagnostics to explain how historical price and transaction volume contribute to predicted prices. Experiments are conducted on an Australian suburban real estate panel with 1826 suburbs and 256 monthly observations from January 2003 to April 2024. EX-KAN is evaluated against KAN, long short-term memory, time-series mixing (TSMixer) and Transformer under a fair joint-input setting across 3-, 6- and 12-month horizons. The results show that EX-KAN achieves the strongest overall price forecasting performance under mean absolute error, mean absolute percentage error (MAPE) and symmetric MAPE, ranking first in six of nine price comparisons and second in the remaining three. Against TSMixer, EX-KAN reduces price MAPE by 17.00% and 8.72% at the three- and six-month horizons, respectively. For transaction-volume forecasting, EX-KAN remains competitive, although KAN is slightly stronger overall. Elasticity, regime-specific and suburb-level analyses show that volume–price relationships vary across horizons, market states and local suburbs. This article is part of the theme issue ‘Data driven modelling for living systems’.