Forecasting Office Construction Price Indices for Cost Planning in Germany Using Regularized VARX Models

Construction price indices play a critical role in shaping construction activity and determining the economic success of building projects in Germany, where they can serve as central inputs to cost planning and to updating trade-level project budgets over the planning and construction horizon. This paper develops a forecasting framework for 35 sub-construction price indices for office buildings, providing granular inputs for cost escalation and risk assessment. We employ regularized vector autoregressive models with exogenous variables (VARX) implemented via the BigVAR package and estimate them in a model-vintage design for an unbalanced panel. These high-dimensional models are benchmarked against compact VARX and vector error-correction models (VECM) that jointly forecast each target index with a small macroeconomic block consisting of the gross domestic product (GDP) and the three-month interbank rate. Candidate specifications are evaluated using mean absolute percentage error (MAPE) and out-of-sample root mean square error (RMSE), and the final forecasting model for each index is selected based on ex post MAPE. The results show that regularized VARX models capture dynamic interdependencies among the sub-indices and, for most series, outperform the VARX and VECM benchmarks. The resulting forecasts provide practitioners with trade-specific escalation factors that can support budgeting, contract design, and the mitigation of cost risk in office-building projects.