International acceptance of automated modern tools use must-have for sustainable real estate market development

Renigier‐Biłozor, Małgorzata; Źróbek, Sabina; Walacik, Marek; Borst, Richard A.; Grover, Richard; d’Amato, Maurizio

doi:10.1016/j.landusepol.2021.105876

Cited by 21 publications

(13 citation statements)

References 22 publications

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“…At the same time, taking into account all possible factors influencing prices can increase the accuracy of such a forecast, so the scientific literature seeks to find them to identify relevant patterns. In particular, the possibilities of using computer mass systems, automated estimation models (Renigier-Biłozor et al, 2022) and artificial intelligence (Kuzior et al, 2019;Kwilinski & Kuzior, 2020;Melnychenko, 2020) are being studied. The relationship between property prices and construction costs is clear and logical (Brzezicka et al, 2018), and its most important components are labor, land, overheads and equipment and materials (Pulver, 1989;Assaf et al, 2001).…”

Section: Literature Reviewmentioning

confidence: 99%

Dependence of Housing Real Estate Prices on Inflation as One of the Most Important Factors: Poland’s Case

Melnychenko

Osadcha

Kovalev

et al. 2022

Real Estate Management and Valuation

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The study aimed to examine the impact of inflation on the real estate market using Polish panel data for the last 13 years. It is based on a panel model, where price changes of one square meter of housing are determined as a function in changes of inflation, the central bank’s base rate, dwellings built, as well as new mortgage loans. The quarterly dynamics of the average price of 1 square meter of housing in Poland’s eight largest cities in the 2009-2021 period was studied. This price was modeled and predicted using one of the Box-Jenkins time series models: the Holt-Winter model of exponential smoothing with a damped trend. The forecasting results showed a small (up to 4%) relative error in comparison with the actual data. In addition, the moment (2017) of the price trend change was found. Therefore, piecewise linear regressions with high regression coefficients were used when modeling the impact of inflation changes on the real estate market indicators under consideration. The results obtained provide valuable insight into the relationship of real estate market indicators, allowing consumers to predict available options and make decisions in accordance with their preferences.

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Section: Literature Reviewmentioning

confidence: 99%

Dependence of Housing Real Estate Prices on Inflation as One of the Most Important Factors: Poland’s Case

Melnychenko

Osadcha

Kovalev

et al. 2022

Real Estate Management and Valuation

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“…There are a wide range of mass valuation methods like MRA (Zurada, Levitan, and Guan 2011; Benjamin, Guttery, and Sirmans 2020; Yilmazer and Kocaman 2020), Hedonic Pricing (Peterson and Flanagan 2009; Lisi 2019; Yamani, Ettarid, and Hajji 2019), Nominal Valuation (Yomralioglu 1993; Yomralioglu and Nisanci 2004; Mete and Yomralioglu 2019), Geographically Weighted Regression (GWR) (Huang, Wu, and Barry 2010; Dimopoulos and Moulas 2016; Wang, Li, and Yu 2020), Ensemble Methods (Alfaro‐Navarro et al 2020; Aydinoglu, Bovkir, and Colkesen 2021; Gnat 2021), and ANN (Demetriou 2017; Lee 2022; Yalpir 2018). With the improvements in computing power, GIS and Artificial Intelligence, Computer‐Assisted Mass Appraisal (CAMA) applications have become widespread and Automated Valuation Models (AVMs) have been adopted in many countries (Wang and Li 2019; Renigier‐Biłozor et al 2022).…”

Section: Real Estate Valuationmentioning

confidence: 99%

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Mete

Yomralıoğlu

2022

Geographical Analysis

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Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation process. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass valuation of residential properties. GIS‐based Nominal Valuation Method was applied to carry out proximity, terrain, and visibility analyses using Ordnance Survey and OpenStreetMap data, than land value map of Great Britain was produced. Spatial criteria scores obtained from the GIS analyses were included in the price prediction process in which global and spatially clustered local regression models are built for England and Wales using Price Paid Data and Energy Performance Certificates data. Results showed that adding locational factors to the property price data and applying a novel nominally weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.

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“…The introduction of AI technologies though has brought a revolutionary change to [33], enabling the industry to employ the power of algorithms for more sophisticated and data-driven decision making [34,35] to give them a "far-reaching impact on individuals and society" [36]. One of the key applications of AI in sustainable real estate development lies in property valuation and is strongly connected with Automated Valuation Modeling (AVM), Mass Appraisal (MA), or computer-assisted Mass Appraisal (CAMA) [37,38]. The simultaneous utilization of Geographic Information Systems and machine learning for that purpose has also been thoroughly investigated in [39] and found its application.…”

Section: Ai-driven Algorithms In Real Estate Industry Solution Analysismentioning

confidence: 99%

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Walacik,

Chmielewska

2024

Sustainability

Self Cite

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As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

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International acceptance of automated modern tools use must-have for sustainable real estate market development

Cited by 21 publications

References 22 publications

Dependence of Housing Real Estate Prices on Inflation as One of the Most Important Factors: Poland’s Case

Dependence of Housing Real Estate Prices on Inflation as One of the Most Important Factors: Poland’s Case

A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

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