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Confronted with the spatial heterogeneity of real estate market, sometraditional research utilized Geographically Weighted Regression (GWR) toestimate 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 WeightedRegression (GNNWR), has been applied to improve the accuracy of real estateappraisal with the help of neural networks. Based on Shenzhen house pricedataset, this work conspicuously captures the weight distribution of differentvariants at Shenzhen real estate market, which GWR is difficult to materialize.Moreover, we focus on the performance of GNNWR, verify its robustness andsuperiority, refine the experiment process with 10-fold cross-validation,extend its application area from natural to socioeconomic geospatial data. It'sa practical and trenchant way to assess house price, and we demonstrate theeffectiveness of GNNWR on a complex socioeconomic dataset.

House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China