Bid Forecasting in Public Procurement

Abstract: Public procurement amounts to a significant part of Sweden's GDP. Nevertheless, it is an overlooked sector characterized by low digitization and inefficient competition where bids are not submitted based on proper mathematical tools. This Thesis seeks to create a structured approach to bidding in cleaning services by determining factors affecting the participation and pricing decision of potential buyers. Furthermore, we assess price prediction by comparing multiple linear regression models (MLR) to support vector regression (SVR). In line with previous research in the construction sector, we find significance for several factors such as project duration, location and type of contract on the participation decision in the cleaning sector. One notable deviant is that we do not find contract size to have an impact on the pricing decision. Surprisingly, the performance of MLR are comparable to more advanced SVR models. Stochastic dominance tests on price performance concludes that experienced bidders perform better than their inexperienced counterparts and companies place more competitive bids in lowest price tenders compared to economically most advantageous tenders (EMAT) indicating that EMAT tenders are regarded as unstructured. However, no significance is found for larger actors performing better in bidding than smaller companies.