Predicting days on market to optimize real estate sales strategy

Castelli, Mauro (author), Dobreva, Maria (author), Henriques, Roberto (author), Vanneschi, Leonardo (author)

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.

• Language: English
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication version: Version of Record
• Year: 2020
• Number of pages: 22 str.
• Numbering: Vol. 2020, art. 4603190
• PID: 20.500.12556/RUL-114724
• UDC: 659.2:004
• ISSN on article: 1099-0526
• DOI: 10.1155/2020/4603190
• COBISS.SI-ID: 25563110
• Publication date in RUL: 06.03.2020

Record is a part of a journal

• Title: Complexity
• Shortened title: Complexity
• Publisher: Wiley
• COBISS.SI-ID: 18282279

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 06.03.2020

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe