A Method for Merging Similar Zones to Improve Intelligent Models for Real Estate Appraisal
A method for property valuation based on the concept of merging different areas of the city into uniform zones reflecting the characteristics of the real estate market was worked out. The foundations of the method were verified by experimental testing the accuracy of the models devised for the prediction of real estate prices built over the merged zones. The experiments were conducted using real-world data of sales transactions of residential premises completed in a Polish urban municipality. Two machine learning techniques implemented in the WEKA environment were employed to generate property valuation models. The comparative analysis of the methods was made with the nonparametric Friedman and Wilcoxon statistical tests. The study proved the usefulness of merging of similar areas which resulted in better reliability and accuracy of predicted prices.
KeywordsReal estate appraisal Predictive models Machine learning Linear regression Decision trees
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