Estimating land market values from real estate offers: A replicable method in support of biodiversity conservation strategies
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While cost estimation is a very positive tool for spatial conservation prioritisation, there are few examples where costs (in monetary terms) are applied. We present a repeatable method to estimate and map field values in monetary terms using common correlative models. We modelled, with a resolution of 1 km2, the information obtained by several real estate’s agencies with a set of eleven environmental, climatic, and anthropogenic variables. Land cover was the main influencing factor, but further variables were affecting bids on field sales according to the socio-economic specificity of each administrative province. The estimated values were related to endemic plant species richness, their conservation status and altitudinal ranges. Richest areas in endemics have lower values given current market conditions and, within these endemic rich areas, values near the coast were generally higher than the rest of endemic-rich territories. Despite their limits, our method offers an alternative perspective on the challenges of simplifying the extrapolation of useful information for planning and disseminating the conservation of many ecosystem services providers.
KeywordsConservation planning Decision making Endemic vascular plants Generalised Linear Models Land prices modelling Mediterranean islands
We would like to thank all people who provided field and unpublished data. We are also grateful to the Editor and the anonymous reviewers for their critical comments and suggestions.
- Ball, I.R., H.P. Possingham, and M. Watts. 2009. Marxan and relatives: software for spatial conservation prioritization. In Spatial conservation prioritisation: Quantitative methods and computational tools, ed. A. Moilanen, K. Wilson, and H. Possingham, 185–195. Oxford: Oxford University Press.Google Scholar
- Balmford, A., K.J. Gaston, S. Blyth, A. James, and V. Kapos. 2003. Global variation in terrestrial conservation costs, conservation benefits, and unmet conservation needs. Proceedings of the National Academy of Sciences of United States of America 100: 1046–1050. https://doi.org/10.1073/pnas.0236945100.CrossRefGoogle Scholar
- Barbosa, A.M., J.A. Brown, A. Jimenez-Valverde, and R. Real. 2014. ModEvA: Model Evaluation and Analysis. R Package, version 1.1. http://modeva.r-forge.r-project.org/modEvA-tutorial.html. Retrieved 1 March 2017.
- Disselhoff, T. 2015. Alternative ways to support private land conservation. Natura 2000, LIFE Programme, Report E.3-PO/07.020300/2015/ENV, Berlin, Germany.Google Scholar
- Fois, M., G. Bacchetta, A. Cuena-Lombraña, D. Cogoni, M.S. Pinna, E. Sulis, and G. Fenu. 2018a. Using extinctions in species distribution models to evaluate and predict threats: a contribution to plant conservation planning on the island of Sardinia. Environmental Conservation 45: 11–19. https://doi.org/10.1017/S0376892917000108.CrossRefGoogle Scholar
- Haase, D., N. Larondelle, E. Andersson, M. Artmann, S. Borgström, J. Breuste, E. Gomez-Baggethun, Å. Gren, et al. 2014. A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio 43: 413–433. https://doi.org/10.1007/s13280-014-0504-0.CrossRefGoogle Scholar
- Hijmans, R., and J. van Etten. 2014. Raster: Geographic data analysis and modeling. R package version 2.2-31. https://cran.r-project.org/web/packages/raster/index.html.
- Hwang, M., and J. Quigley. 2004. Selectivity, quality adjustment and mean reversion in the measurement of house values. Journal Real Estate Finance and Economics 28: 191–214. https://doi.org/10.1023/B:REAL.0000011152.40485.12.CrossRefGoogle Scholar
- ISTAT. 2014. Italy in numbers (In Italian). Roma: Istituto Nazionale di Statistica.Google Scholar
- Mallawaarachchi, T., M.D. Morrison, and R.K. Blamey. 2006. Choice modelling to determine the significance of environmental amenity and production alternatives in the community value of peri-urban land: Sunshine Coast, Australia. Land Use Policy 23: 323–332. https://doi.org/10.1016/j.landusepol.2004.11.004.CrossRefGoogle Scholar
- Mendoza-Fernández, A.J., F.J. Pérez-García, F. Martínez-Hernández, E. Salmerón-Sánchez, J.M. Medina-Cazorla, J.A. Garrido-Becerra, M.I. Martínez-Nieto, M.E. Merlo, and J.F. Mota. 2015. Areas of endemism and threatened flora in a Mediterranean hotspot: Southern Spain. Journal for Nature Conservation 23: 35–44. https://doi.org/10.1016/j.jnc.2014.08.001.CrossRefGoogle Scholar
- Moilanen, A., and H. Kujala. 2008. Spatial conservation planning framework and software v2.0: User manual. Helsinki: Department of Biological and Environment Sciences, University of Helsinki.Google Scholar
- R Development Core Team. 2010. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
- Rosen, S. 1974. Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy 82: 34–55. http://www.jstor.org/stable/1830899.
- Sanderson, E.W., M. Jaiteh, M.A. Levy, K.H. Redford, A.V. Wannebo, and G. Woolmer. 2002. The Human Footprint and the Last of the Wild: The human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience 52: 891–904. https://doi.org/10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2.CrossRefGoogle Scholar
- Sutton, N.J., S. Cho, and P.R. Armsworth. 2016. A reliance on agricultural land values in conservation planning alters the spatial distribution of priorities and overestimates the acquisition costs of protected areas. Biological Conservation 194: 2–10. https://doi.org/10.1016/j.biocon.2015.11.021.CrossRefGoogle Scholar
- Walsh, C., and R. Nally. 2008. Hier.Part: Hierarchical Partitioning. R Package Version 1.0-3. https://cran.r-project.org/web/packages/hier.part/index.html. Retrieved 20 February 2016.
- Wildlife Conservation Society (WCS), Center for International Earth Science Information Network (CIESIN)/Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Influence Index (HII) Dataset (Geographic), Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4BP00QC. Retrieved 1 February 2015.