Predicting Days on Market to Optimize Real Estate Sales Strategy

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2020
Outros Autores: Dobreva, Maria, Henriques, Roberto, Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/94978
Resumo: Castelli, M., Dobreva, M., Henriques, R., & Vanneschi, L. (2020). Predicting Days on Market to Optimize Real Estate Sales Strategy. Complexity, 2020, 1-22. [4603190]. https://doi.org/10.1155/2020/4603190 -------This work was supported by national funds through the FCT (Fundacao para a Ciencia e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCI-INF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
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spelling Predicting Days on Market to Optimize Real Estate Sales StrategyMachine learning algorithmsArtificial Neural NetworksLassoRidgeElastic NetComputer Science(all)GeneralSDG 9 - Industry, Innovation, and InfrastructureSDG 11 - Sustainable Cities and CommunitiesCastelli, M., Dobreva, M., Henriques, R., & Vanneschi, L. (2020). Predicting Days on Market to Optimize Real Estate Sales Strategy. Complexity, 2020, 1-22. [4603190]. https://doi.org/10.1155/2020/4603190 -------This work was supported by national funds through the FCT (Fundacao para a Ciencia e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCI-INF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Irregularities and frauds are frequent in the real estate market in Bulgaria due to the substantial lack of rigorous legislation. For instance, agencies frequently publish unreal or unavailable apartment listings for a cheap price, as a method to attract the attention of unaware potential new customers. For this reason, systems able to identify unreal listings and improve the transparency of listings authenticity and availability are much on demand. Recent research has highlighted that the number of days a published listing remains online can have a strong correlation with the probability of a listing being unreal. For this reason, building an accurate predictive model for the number of days a published listing will be online can be very helpful to accomplish the task of identifying fake listings. In this paper, we investigate the use of four different machine learning algorithms for this task: Lasso, Ridge, Elastic Net, and Artificial Neural Networks. The results, obtained on a vast dataset made available by the Bulgarian company Homeheed, show the appropriateness of Lasso regression.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCastelli, MauroDobreva, MariaHenriques, RobertoVanneschi, Leonardo2020-03-24T23:39:24Z2020-02-252020-02-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article22application/pdfhttp://hdl.handle.net/10362/94978eng1076-2787PURE: 17450350https://doi.org/10.1155/2020/4603190info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:42:59Zoai:run.unl.pt:10362/94978Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:38:10.549525Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting Days on Market to Optimize Real Estate Sales Strategy
title Predicting Days on Market to Optimize Real Estate Sales Strategy
spellingShingle Predicting Days on Market to Optimize Real Estate Sales Strategy
Castelli, Mauro
Machine learning algorithms
Artificial Neural Networks
Lasso
Ridge
Elastic Net
Computer Science(all)
General
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
title_short Predicting Days on Market to Optimize Real Estate Sales Strategy
title_full Predicting Days on Market to Optimize Real Estate Sales Strategy
title_fullStr Predicting Days on Market to Optimize Real Estate Sales Strategy
title_full_unstemmed Predicting Days on Market to Optimize Real Estate Sales Strategy
title_sort Predicting Days on Market to Optimize Real Estate Sales Strategy
author Castelli, Mauro
author_facet Castelli, Mauro
Dobreva, Maria
Henriques, Roberto
Vanneschi, Leonardo
author_role author
author2 Dobreva, Maria
Henriques, Roberto
Vanneschi, Leonardo
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Dobreva, Maria
Henriques, Roberto
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Machine learning algorithms
Artificial Neural Networks
Lasso
Ridge
Elastic Net
Computer Science(all)
General
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
topic Machine learning algorithms
Artificial Neural Networks
Lasso
Ridge
Elastic Net
Computer Science(all)
General
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
description Castelli, M., Dobreva, M., Henriques, R., & Vanneschi, L. (2020). Predicting Days on Market to Optimize Real Estate Sales Strategy. Complexity, 2020, 1-22. [4603190]. https://doi.org/10.1155/2020/4603190 -------This work was supported by national funds through the FCT (Fundacao para a Ciencia e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCI-INF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
publishDate 2020
dc.date.none.fl_str_mv 2020-03-24T23:39:24Z
2020-02-25
2020-02-25T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/94978
url http://hdl.handle.net/10362/94978
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1076-2787
PURE: 17450350
https://doi.org/10.1155/2020/4603190
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 22
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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