This study uses three machine learning algorithms including, support vector machine (SVM),
random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices …

Machine learning algorithms are being used for multiple real-life applications and in
research. As a consequence of digital technology, large structured and georeferenced …

House price prediction is a popular topic, and research teams are increasingly performing
related studies by using deep learning or machine learning models. However, because …

Profound insights have been gained into which characteristics determine housing prices.
These characteristics reflect two different aspects: those which are correlated with the …

Recent work on deep learning for tabular data demonstrates the strong performance of deep
tabular models, often bridging the gap between gradient boosted decision trees and neural …

This research seeks to demonstrate how machine learning, a branch of artificial intelligence,
is able to deliver more accurate pricing predictions, using the real estate market as an …

This study provides a fast and accurate Machine learning (ML) and optimization framework,
which allows a quick estimate for building costs, hence improving operational efficiency and …

Land price is an important economic factor in producing meaningful references for regional
planners by assisting them in urban planning, economic decision-making, and land …

Real estate price estimation has been an interesting subject in the literature from the
appearance of online real estate services like Zillow and Redfin. These websites and many …

Understanding the determinants of housing price movements is an ongoing subject of
debate. Estimating these determinants becomes a valuable tool for predicting price trends …