Affiliations 

  • 1 United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
  • 2 Unit of Applied Artificial Intelligence, Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain
  • 3 Department of Forest Analytics, Texas A&M Forest Service, Dallas, TX 75252, USA
  • 4 Department of Agricultural and Forest Sciences and Engineering, University of Lleida, Av. Alcalde Rovira Roure 191, 5198 Lleida, Spain
  • 5 Spatial Ecology and Conservation (SPEC) Lab, School of Forest, Fisheries, and Geomatics Sciences, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
  • 6 Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
  • 7 Department of Geography, College of Arts and Social Sciences, Sultan Qaboos University, Muscat, P.O. Box 50, Oman
  • 8 BIOFIX Research Center, Federal University of Paraná (UFPR), Curitiba 80210-170, Brazil
  • 9 Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), University of Florida, USA
Heliyon, 2023 Oct;9(10):e20408.
PMID: 37842597 DOI: 10.1016/j.heliyon.2023.e20408

Abstract

Urban forests provide direct and indirect benefits to human well-being that are increasingly captured in residential property values. Remote Sensing (RS) can be used to measure a wide range of forest and vegetation parameters that allows for a more detailed and better understanding of their specific influences on housing prices. Herein, through a systematic literature review approach, we reviewed 89 papers (from 2010 to 2022) from 21 different countries that used RS data to quantify vegetation indices, forest and tree parameters of urban forests and estimated their influence on residential property values. The main aim of this study was to understand and provide insights into how urban forests influence residential property values based on RS studies. Although more studies were conducted in developed (n = 55, 61.7%) than developing countries (n = 34, 38.3%), the results indicated for the most part that increasing tree canopy cover on property and neighborhood level, forest size, type, greenness, and proximity to urban forests increased housing prices. RS studies benefited from spatially explicit repetitive data that offer superior efficiency to quantify vegetation, forest, and tree parameters of urban forests over large areas and longer periods compared to studies that used field inventory data. Through this work, we identify and underscore that urban forest benefits outweigh management costs and have a mostly positive influence on housing prices. Thus, we encourage further discussions about prioritizing reforestation and conservation of urban forests during the urban planning of cities and suburbs, which could support UN Sustainable Development Goals (SDGs) and urban policy reforms.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.