Abstract

This paper analyzes the effect of macro-economic, financial and commodity market indicators on housing markets. We compare the efficiency of the models generated by Generalized Linear Models (GLM) and Multivariate Adaptive Regression Splines (MARS) according to method free measures for estimating the housing market trend. These models are used for the first time to identify the influence of macro-economic indicators on housing markets and the estimation of the trend in housing markets to our best knowledge. The empirical analysis focuses on the US housing market, and the illustration of the proposed models is done through the monthly historical realizations of S\&P/Case-Shiller National Home Price Index (HPI) and the US macro-economic indicators over the period from 1999-January to 2018-June. It contributes to the literature by highlighting the interaction between macro-economic indicators and housing markets and analyzing the mechanism of housing markets. The findings indicate that the house price trends are estimated with more accuracy and these models capture the joint influence of explanatory variables. Further, the MARS method is shown to outperform GLM compared to the prediction and forecasting power.

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