Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression

Purpose: This study aims to quantify the influence of urbanization on housing prices at the districtbased level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

Design/methodology/approach: The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system Generalized Method of Moments is employed to evaluate how urbanization influences district-based housing prices through three spectrometrics: Urban Index (𝑈𝐼), Normalized Difference Built-up Index (𝑁𝐷𝐵𝐼), and Built-Up Index (𝐵𝑈𝐼). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo Simulation.

Findings: The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60𝑡ℎ percentile of housing prices, while its effect on the 10𝑡ℎ and 90𝑡ℎ percentile is comparatively weaker.

Originality/value: This study employs a novel method of utilizing remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.