Exploring the Relationship Between Credit Expansion and Asset Bubble Formation in the Real Estate Sector

Posted: Apr 28, 2015

• Samuel Smith
• Sarah Davis
• Sarah Harris

Abstract

This research investigates the complex relationship between credit expansion and asset bubble formation in real estate markets through a novel computational framework that integrates agent-based modeling with machine learning techniques. Traditional economic models have struggled to capture the non-linear dynamics and heterogeneous behaviors that characterize real estate bubbles, often relying on simplified assumptions about market participants and credit mechanisms. Our approach introduces a multi-agent system where heterogeneous agents—including households, investors, developers, and financial institutions—interact within a simulated real estate market environment. The model incorporates adaptive learning mechanisms where agents update their expectations and behaviors based on market outcomes and credit availability. We employ deep reinforcement learning to model the strategic behavior of financial institutions in credit allocation, capturing the endogenous nature of credit creation during boom periods. The methodology represents a significant departure from conventional econometric approaches by emphasizing the emergent properties of complex systems rather than relying on equilibrium assumptions. Our results demonstrate that credit expansion alone is insufficient to generate sustained real estate bubbles; rather, the interaction between credit availability, heterogeneous expectations, and institutional behaviors creates the conditions for bubble formation. The model reveals threshold effects where credit growth beyond certain levels triggers self-reinforcing price dynamics that decouple from fundamental valuations. Furthermore, we identify specific patterns in the temporal evolution of credit-real estate relationships that serve as early warning indicators of bubble formation. These findings provide new insights for policymakers seeking to manage financial stability and suggest that traditional monetary policy tools may need to be supplemented with targeted macroprudential measures that account for the complex, adaptive nature of real estate markets.