Artificial intelligence algorithms to predict Italian real estate market prices

IF 1.6 Q3 BUSINESS, FINANCE
Luca Rampini, F. Re Cecconi
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引用次数: 6

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.
预测意大利房地产市场价格的人工智能算法
目的房地产价格的评估取决于多种因素,而传统的评估方法往往难以完全理解这些因素。房价尤其是更好地了解建筑环境及其特征的基础。最近,机器学习(ML)技术作为人工智能的一个子集,在解决房价预测等复杂非线性问题方面势头越来越大。因此,本研究采用了三种流行的ML技术来预测意大利两个城市的房价。设计/方法/方法通过API协议收集了意大利北部两个城市(布雷西亚和瓦雷泽)的大量房价数据集。这些数据用于训练和测试三个最流行的ML模型,即ElasticNet、XGBoost和人工神经网络,以预测具有六个不同特征的房价。发现使用平均绝对误差(MAE)评分来评估模型的性能。结果表明,人工神经网络在预测房价方面比其他模型表现更好,其MAE比第二好的模型(XGBoost)低5%。研究局限性/含义所有模型在预测最昂贵的情况时的准确性都有所下降,可能是由于缺乏数据。实际含义所提出的模型的可访问性和易用性将允许未来用户使用不同的数据集预测房价。或者,进一步的研究可以使用神经网络实现不同的模型,因为他们知道它们更适合这类任务。原创性/价值迄今为止,这是对预测房价时通常使用的三种最流行的ML模型的首次比较。
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来源期刊
CiteScore
3.50
自引率
23.10%
发文量
33
期刊介绍: Fully refereed papers on practice and methodology in the UK, continental Western Europe, emerging markets of Eastern Europe, China, Australasia, Africa and the USA, in the following areas: ■Academic papers on the latest research, thinking and developments ■Law reports assessing new legislation ■Market data for a comprehensive review of current research ■Practice papers - a forum for the exchange of ideas and experiences
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