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

TL;DR

This paper introduces a novel Geographical Neural Network Weighted Regression (GNNWR) model that improves house price prediction accuracy by capturing spatial heterogeneity more effectively than traditional methods, demonstrated on Shenzhen's real estate data.

Contribution

The paper develops and validates GNNWR, a new neural network-based approach that enhances real estate valuation accuracy over traditional GWR, with broader applicability to socioeconomic geospatial data.

Findings

GNNWR outperforms traditional GWR in accuracy.

The model demonstrates robustness across datasets.

Extended application to socioeconomic data shows versatility.

Abstract

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.