Determinants of housing prices are particularly significant for monitoring and understanding housing prices. Traditional variables are measured through official statistics or questionnaire surveys, which are labour intensive and time-consuming. New forms of data, such as point of interest or street view imagery, have been used to extract housing location and neighbourhood features, but they cannot capture how different individuals recognised and evaluated the properties nearby, which may also be relevant in the house price estimation. Therefore, this study investigates whether user-generated images may be used to monitor and understand housing prices and how they influence real estate values. Within this context, perceived scenes features are extracted and quantified to blend with commonly used determinants of housing prices. Two machine learning algorithms, random forest and gradient boosting machines, are utilised and deployed for integration with a typical housing price modelling-hedonic price model. By comparing the performance and interpretability of different models, the relative importance of features and how they influence the estimation power of the models is visualised and analysed. The findings suggest that random forest predictions perform the best and are interpretable, with geotagged Flickr images adding 4.6% to the model’s accuracy (R²) from 61.9% to 66.5%. Although user-generated images increase minor value in house price estimation, they may be used as a supplementary data source to capture perception features for house price estimation. This could help the restructuring and optimisation of residential areas in future regional construction, planning and development.

房价的决定因素对于监测和了解房价尤为重要。传统变量是通过官方统计或问卷调查来衡量的，这些都是劳动密集型和耗时的。新形式的数据，例如兴​​趣点或街景图像，已被用于提取住房位置和邻里特征，但它们无法捕捉到不同的个人如何识别和评估附近的房产，这也可能与房价估算有关. 因此，本研究调查用户生成的图像是否可用于监控和了解房价以及它们如何影响房地产价值。在这种情况下，感知场景特征被提取和量化，以与常用的房价决定因素相融合。使用并部署了两种机器学习算法，随机森林和梯度提升机器，以与典型的房价建模 - 特征价格模型集成。通过比较不同模型的性能和可解释性，对特征的相对重要性以及它们如何影响模型的估计能力进行可视化和分析。研究结果表明，随机森林预测表现最好并且是可解释的，带有地理标记的 Flickr 图像使模型的准确性提高了 4.6%（R 可视化和分析特征的相对重要性以及它们如何影响模型的估计能力。研究结果表明，随机森林预测表现最好并且是可解释的，带有地理标记的 Flickr 图像使模型的准确性提高了 4.6%（R 可视化和分析特征的相对重要性以及它们如何影响模型的估计能力。研究结果表明，随机森林预测表现最好并且是可解释的，带有地理标记的 Flickr 图像使模型的准确性提高了 4.6%（R² ) 从 61.9% 到 66.5%。尽管用户生成的图像在房价估计中增加了较小的价值，但它们可以用作补充数据源来捕获房价估计的感知特征。这有助于在未来的区域建设、规划和发展中对居住区进行重组和优化。