Green Space Externalities on Housing Markets Integrating Landscape Ecology and Machine Learning

Dear Colleagues,

Urban green–blue infrastructure (GBI) is a critical component of sustainable cities, providing ecosystem services such as heat mitigation, air purification, stormwater regulation, biodiversity support, and cultural and recreational amenities. These ecological functions are increasingly capitalized into housing prices, shaping housing market dynamics and urban spatial development; however, the magnitude, spatial heterogeneity, and equity implications of green space externalities remain insufficiently understood, especially across cities with varying ecological baselines, planning regimes, and socio-economic contexts.

While early studies primarily examined simple proximity to parks, recent advances in landscape ecology and geospatial analytics—including tree-based machine learning combined with explainable AI (XAI), GeoAI, spatial econometrics, and causal inference—offer new tools to uncover the mechanisms behind green space externalities. These approaches can incorporate landscape configuration, connectivity, and accessibility, and can detect nonlinearities, thresholds, and spillover effects that traditional models overlook. They also enable the evaluation of the distributional impacts of green amenities, contributing to evidence-based planning and more equitable housing policies.

This Special Issue focuses on advancing theoretical, methodological, and empirical research on how urban green–blue infrastructure generates externalities that are capitalized into housing markets. We particularly welcome studies that (i) develop novel measures of the ecological quality, accessibility, and connectivity of green–blue spaces (e.g., using remote sensing and landscape metrics) and link these measures to housing prices; (ii) apply explainable machine learning and causal inference to identify and quantify the impacts of green space on housing values; (iii) analyze spatial heterogeneity, nonlinearities, thresholds, and multi-scalar dynamics in green space–housing price relationships; and (iv) investigate the distributional effects of green space price premiums across socio-economic groups. We especially encourage contributions featuring reproducible workflows, comparative case studies, and open-source datasets or tools.

Areas of interest include, but are not limited to, the following topics:

• Impacts of green–blue infrastructure on housing prices and residential location choices;
• Landscape ecology metrics (fragmentation, connectivity, and habitat quality) and hedonic valuation;
• Causal inference approaches, including quasi-experimental designs (e.g., DiD, IV, RDD, and synthetic control) and causal machine learning methods (e.g., causal forests, double/debiased ML, and heterogeneous treatment effect estimation);
• GeoAI approaches combining tree-based machine learning (e.g., XGBoost, LightGBM, and GBDT) with explainable AI techniques (e.g., SHAP, PDP, GeoShapley) to analyze spatial heterogeneity, nonlinearities, and threshold effects;
• Remote sensing and big geospatial data for monitoring urban green–blue infrastructure;
• Accessibility-based assessment of parks, urban forests, and waterfronts;
• Distributional and equity impacts of green space externalities across socio-economic groups.

Submitted manuscripts are expected to have a solid theoretical basis, robust causal identification, and clear implications for spatial planning and policy design.

Dr. Yiyi (Frankie) Chen
Dr. Chun Yin
Prof. Dr. Colin A. Jones
Guest Editors

Manuscript Submission Information

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