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.

Construction price indices play a critical role in shaping construction activity and determining the economic success of building projects in Germany. This paper presents a detailed forecasting framework based on 35 sub-construction-price indices, providing granular insights for enhanced cost management and greater planning certainty. We employ regularized vector autoregressive models with exogenous variables (VARX) implemented via the BigVAR package in RStudio, estimating models across differing historical sample windows. We benchmark these high-dimensional BigVAR models against compact VARX specifications that forecast each target index jointly with macroeconomic variables (GDP, CPI, three-month interbank rate). Candidate models are evaluated using mean absolute percentage error (MAPE) and oot mean square error (RMSE), and final specifications are selected by MAPE to generate forecasts for the index components. Our findings show that regularized VARX effectively captures dynamic interdependencies among sub-indices and, relative to the macro-augmented VARX benchmarks, delivers more accurate forecasts. These results offer practical guidance for contractors, planners, and policymakers aiming to optimize budgeting and mitigate risk in the German construction sector.

Several methods to localise sources of vibrations have been established in the literature. A great amount of those methods are based on databases with features of known impact positions. Great effort needs to be put into highly expensive experiments that deliver those databases. In this paper, we propose several simulation techniques that may replace the expensive experiments for source localisation. The paper compares the localisation accuracy of simulated and experimental data for two different localisation approaches, the reference database method and neural networks. Both methods process signal arrival time differences from several positions on the structure. The methods are exemplarily applied to a complex small-scale structure from the automotive industry: The small dimensions of the brake disk hat and the inclusion of holes is a challenging task for the accuracy of the applied localisation techniques. Results show that simulated data can replace experimentally gained data well in case of the reference database method, whereas the neuronal networks approach should stick to experimentally gained data. The evaluations show that, despite the small dimension, the relative localisation accuracy is within accepted ranges of literature.