Real Estate Valuation based on XGBoost

Real estate valuation is an important segment in the property market since it impacts investment decision, taxation, and financing. This is evidently for the reason that typical techniques like comparison of market value may take so much time and contain human errors. Here authors discuss the use of the XGBoost algorithm, a machine learning tool for improving accuracy in valuations of the real estate sector. XGBoost also mentioned for their high stability, fast rate and high flexibility in terms of large dataset having many features. My real estate case work first explores how to gather information about real properties as well as how to pre-process it, then deepens into the application of XGBoost for property estimation. Comparing of the results of the traditional approach with that of the XGBoost based valuations reveals prediction accuracy and computational gains. The study reaffirms the applicability of machine learning in real estate valuation process to provide a more accurate and up to date property assessment.

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