Forecasting Interconnected Housing Prices

Abstract

While the existing literature highlights interconnections across housing markets, it largely overlooks their potential to enhance house price predictions. To address this gap, we integrate housing market links into a large-dimensional vector autoregressive (LDVAR) model and employ shrinkage techniques for estimation. Simulation outcomes reveal that the LDVAR model's predictive efficacy hinges on the sparsity and strength of the connections between interconnected markets. When the connections are sparse and weak, the LDVAR model which incorporates this information, underperforms univariate models. However, in the presence of denser and stronger connections, connectivity information is crucial for improving short-term forecasting accuracy. The improvement diminishes as the forecasting horizon extends. We examine three forecasting scenarios, each with its own degree of connectivity, by considering house prices within Sydney and Melbourne in Australia, as well as city-level house prices in China. Empirical findings corroborate the results of our simulation analysis.

Keywords: Housing prices, Connections, Sparsity, Large dimension, Shrinkage, Out-of-sample forecasting, Model confidence set

JEL Classification: R39, C32, C51, C53