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As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.
Principles of revitalization of industrial areas in Ukrainian cities
Vol 6, Issue 1, 2025
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Abstract
The study of industrial areas damaged by the war in 2022–2024 in more than twenty Ukrainian cities and their adaptation to other functions is extremely relevant in our time. The purpose of the article is to reveal the basic principles of the revitalization of industrial and residential areas of the Ukrainian cities of Volnovakha, Rubizhne, Severodonetsk, Lysychansk, Popasna, Shchastia, Toretsk, Bakhmut, Soledar, Avdiivka, Maryinka, Krasnohirka, Vugledar, Mariupol, Donetsk region, Orikhiv (Zaporizhzhya region), Kharkiv, Kupyansk, Bakaliya, Izyum (Kharkiv region), Kherson, Irpin, Bucha (Kyiv region), which were destroyed or damaged by a full-scale war, ensuring environmental sustainability and economic efficiency, taking into account socio-cultural aspects. The article explores the core principles of industrial site revitalization and provides successful examples of their implementation. The transformation of abandoned areas and the redistribution of their functions are analyzed. The article’s methodology is based on general scientific methods, including literature analysis, case comparisons, and synthesis of collected data to develop a professional approach to revitalizing neglected industrial sites. The research incorporates an analysis of international analogs of industrial site revitalization, as well as practical visits and field inspections of abandoned industrial areas in Ukraine, which help identify the primary methods and principles applied in revitalization projects. The study emphasizes the importance of revitalization as a key tool for transforming spaces into functional and aesthetically appealing architectural objects. Based on an analysis of international experience in revitalization, recommendations are developed for effective planning and implementation of revitalization projects for neglected industrial areas in Ukrainian cities. The findings provide a theoretical foundation for understanding the revitalization of abandoned urban spaces, promoting sustainable urban development, improving urban functionality, and offering practical applications.
Keywords
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
