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• W3120147006 abstract "Recent studies on property valuation models have been using a growing number of factors to improve their accuracies, such as physical characteristics, location, accessibility, and environmental factors. However, beyond such ‘hard’ location factors, also ‘soft’ factors such as the aesthetic of appearance and street visual features, have an impact on housing prices. From an economic perspective, a place with good perceptual value will bring more value for users since it has a positive impact on achieving the goal of diverse health, social, economic, and environmental public policy. Thus, residents are willing to pay more to have better conditions. Hence, the issue of the street perceptual value is important, but it is not used in property valuation models (e.g., hedonic price models) due to its complexity to be modelled. In recent years, street view image as a new data has been widely used to explore the relationship between street visual features or street visual quality and socio-economic variations such as crime rate, income, population density, etc. Inspired by the mentioned above, this study aims to explore the impact of street visual features extracted from the street view images on housing prices in Xi’an. To achieve this goal, the study first uses Fully Convolutional Networks to extract 17 categories features from the street view image. At the same time, for comprehensively analyze key factors affecting housing prices and improve the accuracy of the property valuation model, the auxiliary geospatial data, which constituted the main independent variables in the traditional research (such as location characteristics, house characteristics, and surrounding infrastructure characteristics), also contained in this work. Then, to test the importance of particular variables with respect to the model accuracy, the study using random forest builds three property valuation models with different data sources. The results show that the street visual features can explain the majority of the variance of the house price. By comparing the results of three models, the model using geospatial data performs better than the model using street view image data. More specifically, the results show that there are non-linear relationships between different street visual features and property value. In addition, compared with the hedonic model, this study shows that the random forest regression model can more accurately estimate the housing prices." @default.
• W3120147006 created "2021-01-18" @default.
• W3120147006 creator A5068452582 @default.
• W3120147006 date "2020-01-01" @default.
• W3120147006 modified "2023-09-27" @default.
• W3120147006 title "The relationship between street visual features and property value using deep learning" @default.
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