Predicting Days on Market to Optimize Real Estate Sales Strategy. (25th February 2020)

• Record Type: Journal Article
• Authors: Castelli, Mauro; Dobreva, Maria; Henriques, Roberto; Vanneschi, Leonardo
• Other Names: Tajani Francesco Guest Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-02-25
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/4603190
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.