METHODS OF FORECASTING THE CURRENT MARKET PRICE OF CONSTRUCTION MATERIAL RESOURCES IN PRICE MONITORING SYSTEMS
Abstract
Construction price monitoring systems are essential tools for justifying construction costs, yet the collection of supplier price quotations inevitably produces incomplete datasets: not all suppliers provide prices for their full range of materials, sufficient quotations are not always gathered within the required timeframe, and prices themselves change during the market research period. This makes the forecasting of current market prices for construction material and technical resources a practically significant methodological challenge that remains insufficiently addressed in the existing literature. The study aims to systematize and substantiate methods for forecasting the current market price of construction resources under conditions of incomplete data in price monitoring systems. The research employs statistical imputation methods adapted from general statistical theory, including the ratio-of-means method, forward copying, median imputation, and the nearest-neighbor method, alongside the concepts of representative resources and analogue resources as dedicated forecasting tools. Machine learning-based imputation approaches, such as the EM algorithm, association rules models, and random forests, are also reviewed and evaluated for their applicability to construction price monitoring. A computational experiment was conducted using real price data from three suppliers of construction nails collected over 2020–2021. The results confirm a stable inverse relationship between nail size and price per kilogram across all surveyed suppliers, with a correlation coefficient of −0.74 between nail weight and market price. The experiment further demonstrates that conversion coefficients derived from the physical characteristics of products within a single commodity group provide a reliable and practical basis for forecasting missing prices. The proposed methodology is directly applicable to existing construction price monitoring systems and requires no complex software infrastructure, making it suitable for practical implementation without significant organizational or technical barriers.
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