AI-Based on Machine Learning Methods for Urban Real Estate Prediction: A Systematic Survey

Tekouabou, Stéphane C. K.; Gherghina, Ştefan Cristian; Kameni, Eric Désiré; Filali, Youssef; Idrissi Gartoumi, Khalil

doi:10.1007/s11831-023-10010-5

Cited by 8 publications

(2 citation statements)

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“…These cities are not just innovative paradigms but also data-driven ecosystems that significantly improve the quality of life of their inhabitants while promoting sustainability and resilience [1,2]. They are at the forefront of addressing some of the most pressing challenges of our times-from complex environmental challenges like environmental degradation, resource strain, and escalating demands on infrastructure to efficient urban governance and growing digitization, leading to more efficient, data-driven, and citizen-oriented decisionmaking models [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Trindade Neves,

Aparicio,

de Castro Neto

2024

Applied Sciences

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In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

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