Artificial intelligence algorithms to predict Italian real estate market prices

Abstract

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

“…For instance, the combination of low interest rates and high demand might push prices up more significantly than expected by considering each factor separately. Therefore, real estate markets exhibit non-linearity and joint effects, which machine learning algorithms adeptly handle [27,30,70].…”

“…The real estate market, characterized by its complexity and dynamism, necessitates using advanced predictive tools capable of deciphering the interplay of numerous variables. In recent years, tree-based machine learning models and artificial neural networks (ANN) have emerged as front-runners to enhance prediction accuracy for real estate prices [27][28][29][30][31]. Due to their capacity to capture non-linear relationships and interactions, these models consistently outperform traditional linear models [29,32,33].…”

“…Like in many sectors, the importance of interpretability cannot be overstated, and in the real estate market, stakeholders, including investors, policymakers, and urban planners, often demand clear and comprehensible explanations behind predictions as they plan and make informed decisions [4]. Here, XAI techniques provide insights into the factors influencing selling prices and help stakeholders make informed decisions [27,30,32,35,44].…”

Profitability and financial performance of Italian real estate companies: quantitative profiles

“…Italian real estate has also been the subject of specialised analyses of closed-end real estate funds as part of a broader analysis of real estate portfolio management (Mattarocci and Scimone, 2021), as well as applications of a subset of artificial intelligence, “machine learning”, to detect market information transparency (Gabrielli et al ., 2022) and to predict house prices (Rampini and Re Cecconi, 2022).…”

Artificial intelligence feasibility in construction industry

“…Specifically, with ML in this study, we refer to shallow models that do not include DL techniques, considered in a separate category. Many shallow learning techniques have been used to solve construction related problems (e.g., Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees (DT), K-nearest Neighbors (KNN), and shallow neural networks) (Rampini & Re Cecconi 2021). ML approaches were mainly used in five common parts of construction project management, including risk assessment and reduction (Gondia et al 2020), construction site safety management (Harirchian et al 2020), cost estimation and prediction (Rafiei et al 2018), schedule management (Son et al 2012), and building energy demand prediction (Rahman et al 2018).…”