Long Short-Term Memory (LSTM) neural networks in predicting fair price level in the road construction industry

Abstract Running a tender procedure in the construction industry a client expects receiving reasonable prices in the tenders. However, the market competition and the contractors’ will to win a contract to work out a profit, sometimes make the prices far from expected. They can be really low, almost impossible to be kept during the contract execution. Oppositely, the offered prices can be far above a client’s expectation. If they are extremely high, a client has to decide, to accept one of them (even bid-rigging is suspected), or to cancel the procedure. Nevertheless, cancelling a procedure means the considerably postponed the start of the construction. The analysis of almost 400 completed tender procedures (in the Polish road construction industry) and the market trends proved that unexpectedly high prices do not always have to point the collusive behaviours of the offerors. As aforementioned analysis is based on past cases, there is a need to propose the predicting tool, to help clients making decisions in the new tender procedures: to accept the high price as a market-based, or to reject all of the offers as a bid-rigging is suspected. The above-mentioned analysis are based on the simple moving average tool. For predictions the autoregressive tool is proposed – long short-term memory (LSTM) neural networks.