A Method for Automated Adjustment of the Rental Rate for Agricultural Land in the Region Based on Cluster Analysis
DOI:
https://doi.org/10.14529/cmse260307Keywords:
machine learning, investment attractiveness, clustering, rental rateAbstract
In the context of the need to improve the efficiency of managing the land and property complex, methodological approaches to the assessment of agricultural land are considered. Modern approaches to the methodology of investment attractiveness analysis are based on machine learning algorithms, which surpass traditional regression approaches in the accuracy of predicting nonlinear dependencies and interactions between variables. This article discusses a method for the automatic generation of recommendations for adjusting the rental rate of land plots based on an index of investment indicators. It is founded on methods of cluster and regression analysis, as well as machine learning techniques. As a result of a comparative analysis, a method for estimating the optimal number of clusters was identified, and a quantitative indicator reflecting investment attractiveness was defined, making it understandable to the end user who makes prompt and high-quality management decisions. The article presents and substantiates a step-by-step implementation of an algorithm for the automatic generation of recommendations for adjusting the rental rate of land plots based on a comprehensive analysis of their investment indicators. The algorithm utilizes a methodology for forming an investment attractiveness index for unclaimed agricultural lands. A quantitative indicator reflecting the investment attractiveness of agricultural lands has been determined. The proposed method evaluates the economic attractiveness of an object based on its location relative to the cluster center. It can significantly simplify the management of large real estate portfolios by providing a transparent and well-founded pricing strategy. An automatic approach to forming indexes and determining the optimal rental rate will reduce the risks associated with the human factor and improve the overall quality of management decisions.
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