Abstract

One of the expected consequences of the post-pandemic era has been the global inflationary process with multiscalar characteristics, where construction prices in Chile have experienced unpredictable fluctuations, severely affecting various types of projects, both public and private, from large companies to small public initiatives. On the other hand, the growing demand for housing in the midst of the housing crisis in Chile has been hindered by the increase in material prices. In this scenario, it is relevant to generate an ecosystem of predictive models for construction costs that take into account the external factors that influence construction costs and, therefore, the price of new homes and public infrastructure. This study contributes to the development of this ecosystem by using a vector autoregressive (VAR) model to identify the predictive factors that influence construction costs in Chile, particularly exploring the influence of macroeconomic variables on construction costs by considering a set of incidental variables that are not always considered in this type of analysis and that involve fundamentals from sectors other than construction. The methodological results allow for the testing of predictive models with a good level of statistical significance, as well as clearly identifying those external factors that impact construction material prices.