The use of urban indicators in forecasting a real estate value with the use of deep neural network

Bazan-Krzywoszańska, Anna; Bereta, Michał

doi:10.2478/rgg-2018-0011

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…While it is not a dominant factor, its consistent presence indicates it is a feature that the model reliably considers. These insights are aligned with the study by Bazan-Krzywoszanska et al (2018) [63], where the authors discuss the relative importance of "district safety", ranked as one of the top ten factors in their ANN model, highlighting the importance of safety in the district as a determinant of property value.…”

Section: Governancesupporting

confidence: 79%

“…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%

See 1 more Smart Citation

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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Section: Governancesupporting

confidence: 79%

Section: Data Sources and Datasetsmentioning

confidence: 99%

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

show abstract

“…Some of applications are shown in Table 2 . Not only single DNN model (Bazan-Krzywoszanska & Bereta, 2018 ; Feng et al, 2018 ; Kremsner et al, 2020; Lukman et al, 2020), but also hybrid models (Chatzis et al, 2018 ; Ding et al, 2019 ; Frey et al 2019 ; Galeshchuk & Mukherjee, 2017 ; He et al, 2019 ; Tan et al, 2020; Yuan & Lee, 2020; Zhong & Enke, 2019), often achieve higher classification or prediction accuracy than other benchmark methods.…”

Section: Frequently-used Deep Learning Models In Economics Applicationsmentioning

confidence: 99%

“…In order to identify a slum’s degree and predict data-driven index of multiple deprivation, CNN was utilized to capture features from 1,114 very high-resolution images (Ajami et al, 2019 ). In the aspect of transportation economics, various deep learning techniques, like DNN, LSTM, deep capsule network or their variants, were applied to predict transportation demand or estimate socioeconomic status (Bazan-Krzywoszanska & Bereta, 2018 ; Ding et al, 2019 ; He, 2021 ; Markou et al, 2020). As for real state economics, DNN was used to forecast a real estate value (Yao et al, 2018) and CNN was used to map fine-scale urban housing prices through images (Rafiei & Adeli, 2016).…”

Section: Applications Of Deep Learning In Economicsmentioning

confidence: 99%

Deep learning in economics: a systematic and critical review

2023

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From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.

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“…In order to look at research related to AI technology in the appraisal industry, it is necessary to review the literature since the early 1980s when the computerization of real estate appraisal began. Initially, it was approached in a way that automated the typical behavior of valuers or to develop prototypes [7]. Rossini et al (1992) proposed an automatic evaluation system for mass evaluation [8].…”

Section: Related Studiesmentioning

confidence: 99%

A Study on the Use and Risk of Artificial Intelligence in the Appraisal Industry

2021

ilkonline

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The purpose of this study is to investigate Korean appraisers' perception of the potential for the utilization of Artificial Intelligence (AI) and the related risks of using AI in the appraisal industry. We conducted a mobile survey for appraisers from February 10 to 18, 2020. We collected survey data from 193 respondents. Frequency Analysis and Multiple Response Analysis were performed for basic analysis. Factor Analysis was used to analyze various types of risks in using AI for appraisal industry. To visualize these risks, we drew positioning maps using the Multidimensional Scaling Method. Appraisers have a positive perception on the introduction of AI in the appraisal industry, but they also recognized the negative effects related to job losses and job replacements. They mainly considered collateral, consulting, and taxation appraisal as areas with high potential for using AI and areas with high replaceability of human labor. They were more aware of the substitution risk by AI in these areas. They were highly aware of accountability, privacy & security, and technical error risks. However, fairness, transparency, and reliability risks are generally perceived as low risk issues. While prior studies have mainly studied the analysis method of applying AI to the mass appraisal model, this study focused on the use and risk of AI. Understanding the perception of industry experts on the utilization of AI helps to minimize the potential risks that may arise when AI is introduced on a large scale.

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The use of urban indicators in forecasting a real estate value with the use of deep neural network

Cited by 7 publications

References 16 publications

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

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

Deep learning in economics: a systematic and critical review

A Study on the Use and Risk of Artificial Intelligence in the Appraisal Industry

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