Extreme Randomized Trees for Real Estate Appraisal with Housing and Crime Data

Pp: 83-97 (15)

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Abstract

Real estate appraisal plays a vital role in people’s daily life. People rely on the estimation of decisions on buying houses. It is well recognized that the criminal activities around the house have significant impacts on house prices. House buyers can make more reasonable decisions if they are aware of the criminal activities around the house. Therefore, a machine learning-based method is proposed by combining house attributes and criminal activities. Specifically, the method firstly infers the intensity of criminal activities from historical crime records.Then, a novel machine learning algorithm, extremely randomized trees (ExtraTrees), is implemented to estimate the house price based on the extracted comprehensive crime intensity and real-world house attributes.The experimental results show that the proposed method outperforms contemporary real estate appraisal methods by achieving higher accuracy and robustness.

Keywords: Ensemble learning, Extremely randomized trees, Machine learning, Real estate appraisal.

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