基于地理神经网络加权回归的房价评估模型--以深圳市为例
House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China
摘要：
面对房地产市场的空间异质性，传统的研究多采用地理加权回归（GWR）方法来估计房价。然而，其核函数是非线性的，难以捉摸的，和复杂的选择带宽，预测能力也可以提高。因此，一种新的技术，地理神经网络加权回归（GNNWR），已被应用到提高房地产估价的准确性与神经网络的帮助下。基于深圳房价数据，本文显著地刻画了深圳房地产市场上各种变量的权重分布，而GWR很难实现。此外，我们还重点研究了GNNWR的性能，验证了它的鲁棒性和优越性，通过10倍交叉验证改进了实验过程，将其应用范围从自然地理空间数据扩展到社会经济地理空间数据。这是一种评估房价的实用和明确的方法，我们在复杂的社会经济数据集上证明了GNNWR的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
