Exploring XAI techniques for enhancing model transparency and interpretability in real estate rent prediction: A comparative study

Lenaers, Ian; De Moor, Lieven

doi:10.1016/j.frl.2023.104306

Cited by 2 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SHAP values for the Train feature are spread out but less so than for Subway, reflecting a moderately positive effect on price. This finding validates previous research by Lenaers and De Moor (2023) [44] and implies that while train station accessibility to regional or national rail networks is a valued asset, it may not be as critical for daily commuting within the city as subway access is. The SHAP beeswarm plot also shows positive and negative impacts, and it could reflect the dual nature of train stations as hubs for opportunity and potential nuisances.…”

Section: Mobilitysupporting

confidence: 90%

“…Previous studies from Guliker et al (2022) [65], Lenaers et al ( 2023) [44], and Rampini et al (2021) [30] confirm the importance of EPC in the valuation process, and our results are aligned with these work's findings, further demonstrating that properties with higher EPC ratings tend to have a positive effect on predicted prices, reinforcing the value placed on energy efficiency in the housing market.…”

Section: Energy Performance Certificatesupporting

confidence: 89%

“…The open data gathered covers a wide range of specific urban indicators, from mobility (bus stops and proximity to subway and train stations) [44,55,56], quality of life and wellbeing (culture, commerce, education, health, leisure, and environment) [35,[57][58][59][60][61], and governance (housing licensing, safety, and security) [21,62,63] to broader macroeconomic and financial indicators (inflation rate, unemployment, gross domestic product, and bank appraisals) [24,33,64,65]. These indicators play a crucial role in influencing the functionality and growth of a smart city and are instrumental in cities' assessment and evaluation.…”

Section: Data Sources and Datasetsmentioning

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%

See 3 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Mobilitysupporting

confidence: 90%