The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model

TL;DR

This paper introduces a spatial agent-based model for the Australian housing market that captures social influences and provides detailed area-specific forecasts, validated with Sydney data.

Contribution

A novel graph-based spatial agent-based model that incorporates social and economic factors for housing market forecasting in Sydney.

Findings

Accurate overall market predictions for Sydney.

Area-specific forecasts at local government level.

Insights into different agent behaviors, including buyers and investors.

Abstract

Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graph-based approach for incorporating a spatial component in a large-scale urban housing agent-based model (ABM). The model explicitly captures several social and economic factors that influence the agents' decision-making behaviour (such as fear of missing out, their trend following aptitude, and the strength of their submarket outreach), and interprets these factors in spatial terms. The proposed model is calibrated and validated with the housing market data for the Greater Sydney region. The ABM simulation results not only include predictions for the overall market, but also produce area-specific forecasting at the level of local government areas within Sydney as arising from individual buy and sell decisions. In addition, the simulation results elucidate agent preferences in submarkets, highlighting differences in agent behaviour, for example, between first-time home buyers and investors, and between both local and overseas investors.