Influence of a traditional flea market on property prices in its surroundings – a case study in Porto Alegre, Brazil

Marco Aurelio Stumpf González

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

Accepted: 2024-06-22 | Published Online: 2024-06-29

DOI: 10.36253/aestim-15481

ABSTRACT

Flea markets are important as economic and cultural phenomenon in several cities around the world. There is little attention to their influence on real estate prices. The “Brique da Redenção” occur every weekend since 1978 in Porto Alegre, Brazil. There could be positive effects on surrounding properties. However, the positioning of this flea market implies on reduce the accessibility on weekends to properties placed in the same street and it could reduce property prices. The aim of this paper is to evaluate the influence of this flea market in residential prices. It was developed hedonic models to explore these effects, with a sample of more than 5.3 thousand apartment sales. The analysis shows a satisfactory statistical performance of the hedonic model. The study indicates that Brique’ effect is capitalized in the market prices, with an average loss on value around to 7.8% to properties placed in front to the Brique.

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Simultaneous evaluation of dairy farmers’ behaviour and intention to adopt technological devices

Roberta Selvaggi^1,*, Raffaele Zanchini², Carla Zarbà¹, Biagio Pecorino¹, Gioacchino Pappalardo¹

¹ Department of Agriculture, Food and Environment, University of Catania, Italy

² Department of Agricultural, Forest and Food Sciences, University of Turin, Italy

Accepted: 2024-04-09 | Published Online: 2024-05-06

DOI: 10.36253/aestim-15362

ABSTRACT

Society’s awareness of livestock production conditions has increased interest in animal welfare (AW), prompting farmers to consider it in their strategies. However, the adoption of digital devices and sensors to ensure AW is still relatively low. The aim of this study was to assess simultaneously the stated behaviour and intention of dairy farmers towards adopting technological tools for AW. The extended Theory of Planned Behaviour (e-TPB) was selected as theoretical base. It is “extended” since new predictors are integrated in the standard framework of the TPB. The research questions were addressed using a partial least squares structural equation modelling. The findings suggest the existence of a gap between farmers’ intentions and behaviour. Perceived Behavioural Control plays a significant role in behaviour, indicating the predominant influence of self-confidence in farmers’ choices. Operating margin and technological specialization of the farms are significant predictors of farmers’ behavior.

Machine learning models in mass appraisal for property tax purposes: a systematic mapping study

Carlos Augusto Zilli^1,*, Lia Caetano Bastos², Liane Ramos da Silva²

¹ Federal Institute of Santa Catarina (IFSC), Florianópolis, Brazil
² Federal University of Santa Catarina (UFSC), Florianópolis, Brazil

Accepted: 2024-04-02 | Published Online: 2024-04-24

DOI: 10.36253/aestim-15792

ABSTRACT

The use of machine learning models in mass appraisal of properties for tax purposes has been extensively investigated, generating a growing volume of primary research. This study aims to provide an overview of the machine learning techniques used in this context and analyze their accuracy. We conducted a systematic mapping study to collect studies published in the last seven years that address machine learning methods in the mass appraisal of properties. The search protocols returned 332 studies, of which 22 were selected, highlighting the frequent use of Random Forest and Gradient Boosting models in the last three years. These models, especially Random Forest, have shown predictive superiority over traditional appraisal methods. The measurement of model performance varied among the studies, making it difficult to compare results. However, it was observed that the use of machine learning techniques improves accuracy in mass property appraisals. This article advances the field by summarizing the state of the art in the use of machine learning models for mass appraisal of properties for tax purposes, describing the main models applied, providing a map that classifies, compares, and evaluates the research, and suggesting a research agenda that identifies gaps and directs future studies.