Urban green infrastructure valuation: an economic method for the aesthetic appraisal of hedges

Andrea Dominici^1,*, Sandro Sacchelli²

¹ Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Italy

² Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Italy

Accepted: 2024-09-12 | Published Online: 2024-09-12

DOI: 10.36253/aestim-16603

ABSTRACT

The paper presents a parametric approach to quantify the economic value of hedges in urban green spaces. The model integrates indexes that allow for an aesthetic estimate of green infrastructure. Both field and desk phases are developed to depict and sample hedgerows in a case study in Italy (Cascine Park, Florence). Street view and Google Maps applications are used in the preliminary steps to spatialize hedges. An equation, incorporating nine variables including financial, dendrometric, and correction factors, is developed to appraise economic value. The results highlight the relevance of species, plant height, and the number of hedge rows for the unitary and total value of green infrastructures. Phytosanitary condition, the presence of gaps in linear traits, and the degree of tree canopy coverage also influence the economic performances of hedges. The technique facilitates application for both researchers and practitioners, potentially allowing for damage estimates and calibrated management of urban green in different locations.

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Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil

Antônio Augusto Ferreira de Oliveira¹, Fabián Reyes-Bueno², Marco Aurelio Stumpf González^3,*, Everton da Silva⁴

¹ Municipal treasury auditor of Fortaleza, Brazil

² Facultad de Ciencias Exactas y Naturales, Universidad Técnica Particular de Loja, Loja, Ecuador

³ Polytechnic School, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

⁴ Geosciences Department, Universidade Federal de Santa Catarina, Florianópolis, Brazil

Accepted: 2024-07-02 | Published Online: 2024-08-01

DOI: 10.36253/aestim-15344

ABSTRACT

Mass appraisal has significant applications, such as urban planning, real estate appraisal, and property tax. Due to the challenges of analyzing massive models, they are often developed using semi-automatic assessment methods and machine learning techniques. This article explores different appraisal model methods that utilize statistics and machine learning. It also looks at incorporating spatial information to see if the chosen method can effectively capture the typical spatial dependency of the real estate market. This can help reduce the spatial autocorrelation observed in the residuals. The study compared nine machine learning methods with traditional statistical approaches using a dataset of over 43,000 apartments in Fortaleza, Brazil. The results of the machine learning algorithms were similar. The XGBoost minimized spatial autocorrelation. The easiest interpretations were with MRA, M5P, and MARS techniques. Although, these techniques had the greatest residual spatial autocorrelations. There is a trade-off between the methods, depending on whether the aim is to improve accuracy or provide a clear explanation for property taxation.

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