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

arXiv: https://arxiv.org/abs/2202.04358 · HTML: https://mlsanity.com/paper/2202.04358/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)

## Built on by (most cited first, 3 of 3)

- [Boosting House Price Estimations with Multi-Head Gated Attention](https://mlsanity.com/paper/2405.07456/) (2024)
- [Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation](https://mlsanity.com/paper/2506.15723/) (2025)
- [Semi-Supervised Learning under Spatially Biased Sampling](https://mlsanity.com/paper/2609.07982/) (2026)

## Abstract (arXiv)

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.