House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China
This provides a more accurate method for real estate appraisal in spatially heterogeneous markets like Shenzhen, though it is incremental as it builds on existing GWR techniques.
The authors tackled the problem of spatial heterogeneity in real estate valuation by proposing a Geographical Neural Network Weighted Regression (GNNWR) model, which improved predictive accuracy over traditional Geographically Weighted Regression (GWR) on a Shenzhen house price dataset, as validated through 10-fold cross-validation.
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.