# Spatial network characteristics and influencing factors of residential land prices under the background of coordinated development: A case study of the Wuhan metropolitan area in China

## TL;DR

This study examines how residential land prices are distributed and influenced in the Wuhan metropolitan area, using data from 2015 to 2021 to guide better urban development.

## Contribution

The paper introduces a novel application of the MGWR model to analyze spatial variability in residential land prices and identifies key influencing factors.

## Key findings

- Residential land prices in Wuhan show a decreasing pattern from the core to the periphery with spatial clustering.
- The MGWR model outperforms OLS and GWR in capturing spatial variability of land price factors.
- Shopping malls and secondary roads are the most influential factors affecting residential land prices.

## Abstract

Exploring the spatial structure of residential land prices within metropolitan areas is crucial for identifying regional development disparities. It holds significant practical value for guiding the rational allocation of resources, optimizing land use efficiency, and promoting collaborative development across the metropolitan region. Based on the residential land auction and sale data of 48 counties in the Wuhan metropolitan area, this paper analyzes the spatial and temporal evolution characteristics and network structure of regional residential land prices in 2015, 2018, and 2021 using spatial autocorrelation and social network analysis. Further, it analyzes the factors that influence residential land prices using the MGWR model. It is found that: (1) the residential land price in the Wuhan metropolitan area shows a circle characteristic of decreasing from Wuhan as the core to the periphery, with obvious polarization characteristics, and relatively relieved in 2021. Similar aggregation types exhibit a distinct cluster distribution in space. (2) The network structure of residential land prices in the Wuhan metropolitan area increases yearly, but the evolution speed is slow. (3) Compared to OLS and GWR, the MGWR model more accurately measures the impact and spatial variability of variables on residential land prices. The contributing factors, ranked by their influence, are: shopping malls > secondary roads > population > plot ratio > parks and squares > medical facilities > GDP > entertainment venues. With the exception of population and entertainment venues, all other factors exert a positive influence on residential land prices to varying extents. Resource sharing and city-specific policies are feasible ways to promote the healthy and stable development of the land market in the Wuhan metropolitan area.