Composition of Real Estate Values: Analyzing Time-Varying Credit and Market Data Using Neural Networks

This study analyses the time-varying composition of real estate values by using an artificial neural network approach to identify whether and how certain indicators’ impacts on property values fluctuate over time. Therefore, cross-sectional property and macroeconomic data from the United States is applied, spanning a period from 1999 to 2021. In times of normal economic activity, property values are made up of two-thirds of physical attributes and one-third of the macroeconomic environment. During crises periods and times of high uncertainty, like the Global Financial Crisis, the share of the economies impact increases by roughly 5%, meaning that sudden economic changes have a higher impact on property values during crises periods versus normal times. However, these changes in the composition of real estate values varies even from one crisis to another, which confirms the dynamic relationship between the US macroeconomy and the housing market. Moreover, this study provides evidence that neural networks are capable of detecting non-linearities in property values especially during times of financial volatility.