Machine learning applied to estate pricing for residential rentals in dynamic urban markets: the case of São Paulo city (2024)
- Authors:
- USP affiliated authors: DAVID, SERGIO ADRIANI - FZEA ; SOUZA, WESLEY FERREIRA MAIA DE - ICMC
- Unidades: FZEA; ICMC
- DOI: 10.1016/j.enganabound.2024.105988
- Subjects: APRENDIZADO COMPUTACIONAL; PROCESSAMENTO DE LINGUAGEM NATURAL; SISTEMA DE INFORMAÇÃO GEOGRÁFICA; REGRESSÃO LINEAR; ALUGUEL
- Keywords: Rental pricing; Real estate analysis; Ensemble models; Multiple regression
- Language: Inglês
- Imprenta:
- Source:
- Título: Engineering Analysis with Boundary Elements
- ISSN: 0955-7997
- Volume/Número/Paginação/Ano: v. 169, p. 1-12, Dec. 2024
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MAIA, Wesley Ferreira e DAVID, Sérgio Adriani. Machine learning applied to estate pricing for residential rentals in dynamic urban markets: the case of São Paulo city. Engineering Analysis with Boundary Elements, v. 169, p. 1-12, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.enganabound.2024.105988. Acesso em: 24 fev. 2026. -
APA
Maia, W. F., & David, S. A. (2024). Machine learning applied to estate pricing for residential rentals in dynamic urban markets: the case of São Paulo city. Engineering Analysis with Boundary Elements, 169, 1-12. doi:10.1016/j.enganabound.2024.105988 -
NLM
Maia WF, David SA. Machine learning applied to estate pricing for residential rentals in dynamic urban markets: the case of São Paulo city [Internet]. Engineering Analysis with Boundary Elements. 2024 ; 169 1-12.[citado 2026 fev. 24 ] Available from: https://doi.org/10.1016/j.enganabound.2024.105988 -
Vancouver
Maia WF, David SA. Machine learning applied to estate pricing for residential rentals in dynamic urban markets: the case of São Paulo city [Internet]. Engineering Analysis with Boundary Elements. 2024 ; 169 1-12.[citado 2026 fev. 24 ] Available from: https://doi.org/10.1016/j.enganabound.2024.105988 - Automatic sign language to text translation using MediaPipe and transformer architectures
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Informações sobre o DOI: 10.1016/j.enganabound.2024.105988 (Fonte: oaDOI API)
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