...Emmanuel Oluwapelumi Odedele; Oladele Bidemi Ajayi Purpose This study aims to develop and evaluate a machine learning framework for predicting residential property prices in Ireland using the complete Irish Property Price Register, a population-complete data set of 736,002 arm’s-length...

...Hassan Raza Purpose This study develops and evaluates machine-learning ( ML ) models to predict residential property prices in Islamabad, Pakistan, addressing the acute information asymmetry and valuation opacity that characterize real estate markets in emerging economies. The purpose...

... to develop a more accurate and transparent comparative market analysis framework by integrating multisource property data and explainable machine learning, thereby improving both valuation reliability and interpretability for heterogeneous market segments. Design/methodology/approach The study integrates...

...Mohammad Amin Gharehgozli Purpose This paper aims to examine how artificial intelligence ( AI ) can enhance risk governance in housing development by integrating stakeholder-centric network modeling, predictive machine learning and adaptive reinforcement learning ( RL ). Design/methodology...

...Ian Lenaers; Lieven De Moor Purpose This study aims to explore the impact of energy efficiency on rental prices with advanced predictive modeling techniques. It aims to analyze the evolution of energy efficiency’s role in Belgian residential rental markets from 2014 to 2023 using machine learning...

... to inaccurate property pricing. To address these issues, machine learning (ML)-based methods offer the potential to provide more accurate predictions by leveraging historical data and identifying complex patterns. This study aims to analyze and evaluate the accuracy of various ML algorithms in assessing...

...Ömer Gökberk Erbulut; Zeynep Çolak Purpose This study aims to explore alternative methodologies by comparing popular and effective machine learning models for housing price prediction. The primary objective is to develop a hybrid Stacking Regressor model combining multiple regression algorithms...

...Mohammad A. Shbool; Rand Al-Dmour; Bashar Awad Al-Shboul; Nibal T. Albashabsheh; Najat Almasarwah Purpose This study aims to enhance real estate price prediction accuracy using advanced machine learning models, minimizing biases and inconsistencies inherent in traditional appraisal methods...

... to ascertain the factors exerting the most significant impact on the challenge of paying for house prices, an influencing factor model is meticulously crafted. This involves the meticulous screening, training and cross-validation of extensive data sets and multiple regressors through machine learning...

...Visar Hoxha Purpose The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina. Design/methodology/approach Using Python...

... data and population information to predict house prices and inventory. © Emerald Publishing Limited 2023 Emerald Publishing Limited Licensed re-use rights only Regression Forecasting House prices House sales Machine learning The real estate market in the United States has been...

...Ian Lenaers; Kris Boudt; Lieven De Moor Purpose The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added...

... coverage Word embeddings Dictionary-based approach Machine learning Residential real estate markets In an increasingly digital economy, newspapers occasionally appear like a relict from the past, at least in paper form. Today, information flows rapidly on digital platforms, on social media, TV...

...Yu Zhang; Arnab Rahman; Eric Miller Purpose The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices...