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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.
Analysis of territorial patterns of age population structure at the subnational level in Chile
Vol 4, Issue 1, 2023
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Abstract
From the evidence obtained in the 2017 Chilean population census pyramid for the country scale, the question arises whether the population structure by age is replicated homogeneously at the subnational level or, on the contrary, it presents heterogeneous local characteristics based on age groups. For each of the third-level subnational units (n = 346), the standard deviation of the different age classes was compared in relation to the national population pyramid. With these differences, the k-means method was used to identify eight groups of communes, and four demographic indicators were calculated. The results allow to affirm that the population structure by age and sex at the communal level presents heterogeneous characteristics. The analysis of territorial patterns identified groups of the aging population, households with active aging, transition to aging, adult population, men in terminal productive age, men in initial age, households with children under 15 years and households with children over 15 years.
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Copyright (c) 2023 Manuel Fuenzalida, María Paz Trebilcock, Paulette Landon, Francisco Maturana
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
