Machine learning for property price prediction and price valuation: A systematic literature review

Machine learning is a branch of artificial intelligence that allows software applications to be more accurate in its data predicting, as well as to predict current performance and improve for future data. This study reviews published articles with the application of machine learning techniques for p...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Planning Malaysia
المؤلف الرئيسي: 2-s2.0-85119593185
التنسيق: Review
اللغة:English
منشور في: Malaysian Institute Of Planners 2021
الوصول للمادة أونلاين:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119593185&doi=10.21837%2fPM.V19I17.1018&partnerID=40&md5=b6f44288ba3363d8ede1c8c10800f28a
الوصف
الملخص:Machine learning is a branch of artificial intelligence that allows software applications to be more accurate in its data predicting, as well as to predict current performance and improve for future data. This study reviews published articles with the application of machine learning techniques for price prediction and valuation. Authors seek to explore optimal solutions in predicting the property price indices, that will be beneficial to the policymakers in assessing the overall economic situation. This study also looks into the use of machine learning in property valuation towards identifying the best model in predicting property values based on its characteristics such as location, land size, number of rooms and others. A systematic review was conducted to review previous studies that imposed machine learning as statistical tool in predicting and valuing property prices. Articles on real estate price prediction and price valuation using machine learning techniques were observed using electronics database. The finding shows the increasing use of this method in the real estate field. The most successful machine learning algorithms used is the Random Forest that has better compatibility to the data situation. © 2021 Malaysian Institute Of Planners. All rights reserved.
تدمد:16756215
DOI:10.21837/PM.V19I17.1018