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 …

This paper describes a comparative study where several regression and artificial
intelligence (AI)-based methods are used to assess properties in Louisville, Kentucky. Four …

This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential
housing prices. as one type of machine learning algorithm, the proposed SVM encompasses …

This paper develops an artificial intelligence based automated valuation model (AI-AVM)
using the boosting tree ensemble technique to predict housing prices in Singapore. We use …

Zadeh's fuzzy set theory for imprecise or vague data has been followed by other successful
models, inclusive of Molodtsov's soft set theory and hybrid models like fuzzy soft sets. Their …

The main purpose of this study is to propose an interoperable land valuation data model for
residential properties as an extension of the national geographic data infrastructure (GDI) …

Drawing useful predictions from vast accumulations of data is becoming critical to the
success of an enterprise. Organizations' databases grow exponentially from transactions …

The present research takes into account the current and widespread need for rational
valuation methodologies, able to correctly interpret the available market data. An innovative …

Automated valuation models are widely used in real estate to provide estimates for property
prices. Such models are typically developed through regression approaches. This study …

The experiments aimed to compare machine learning algorithms to create models for the
valuation of residential premises, implemented in popular data mining systems KEEL …