Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

Модели массовой оценки недвижимости в XXI веке: систематический обзор литературы
Daikun Wang, Victor Jing Li
2019-12-08

AI-based modelsGIS-based modelsmass appraisalreal estate valuationsystematic literature review
With the increasing volume and active transaction of real estate properties, mass appraisal has been widely adopted in many countries for different purposes, including assessment of property tax. In this paper, 104 papers are selected for the systematic literature review of mass appraisal models and methods from 2000 to 2018. The review focuses on the application trend and classification of mass appraisal and highlights a 3I-trend, namely AI-Based model, GIS-Based model and MIX-Based model. The characteristics of different mass appraisal models are analyzed and compared. Finally, the future trend of mass appraisal based on model perspective is defined as “mass appraisal 2.0”: mass appraisal is the appraisal procedure of model establishment, analysis and test of group of properties as of a given date, combined with artificial intelligence, geo-information systems, and mixed methods, to better model the real estate value of non-spatial and spatial data.
1
A systematic review analyzed 104 papers on real estate mass appraisal models and methods published between 2000 and 2018.
2
Mass appraisal 2.0 is defined as establishing, analyzing, and testing models for groups of properties at a given date.
3
The literature shows a 3I trend in mass appraisal: artificial-intelligence-based, GIS-based, and mixed-method models.
4
The proposed “mass appraisal 2.0” combines artificial intelligence, geographic information systems, and mixed methods to model property values using non-spatial and spatial data.
5
The review classifies, compares, and analyzes the characteristics of different mass appraisal models and methods.

mass appraisal models and methods for real estate properties

application trends, classification, characteristics, comparative performance, and future development of mass appraisal models using AI, GIS, and mixed methods

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2019-12-08
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Daikun Wang
Victor Jing Li
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