论文标题
基于地理神经网络加权回归的房屋价格评估模型：中国深圳的案例研究
House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China
论文作者
论文摘要
面对房地产市场的空间异质性，一些传统研究利用地理加权回归（GWR）来估计房价。但是，其内核函数是非线性，难以捉摸且复杂的，可以选择带宽，也可以提高预测能力。因此，已应用一种新颖的技术，即地理神经网络加权回归（GNNWR），可在神经网络的帮助下提高房地产评估的准确性。基于深圳房屋价格数据集，这项工作明显地捕获了深圳房地产市场的不同变体的重量分布，GWR很难实现。此外，我们专注于GNNWR的性能，验证其鲁棒性和优越性，通过10倍的交叉验证来完善实验过程，将其应用领域从自然到社会经济的地理空间数据扩展。这是一种评估房价的实用且坦率的方式，我们证明了GNNWR对复杂的社会经济数据集的有效性。
Confronted with the spatial heterogeneity of real estate market, some traditional research utilized Geographically Weighted Regression (GWR) to estimate the house price. However, its kernel function is non-linear, elusive, and complex to opt bandwidth, the predictive power could also be improved. Consequently, a novel technique, Geographical Neural Network Weighted Regression (GNNWR), has been applied to improve the accuracy of real estate appraisal with the help of neural networks. Based on Shenzhen house price dataset, this work conspicuously captures the weight distribution of different variants at Shenzhen real estate market, which GWR is difficult to materialize. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, refine the experiment process with 10-fold cross-validation, extend its application area from natural to socioeconomic geospatial data. It's a practical and trenchant way to assess house price, and we demonstrate the effectiveness of GNNWR on a complex socioeconomic dataset.
