Predicting Days on Market to Optimize Real Estate Sales Strategy

Mauro Castelli, Maria Dobreva, Roberto Henriques and Leonardo Vanneschi

Complexity, 2020, vol. 2020, 1-22

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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/8503/2020/4603190.pdf (application/pdf)
http://downloads.hindawi.com/journals/8503/2020/4603190.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4603190

DOI: 10.1155/2020/4603190

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().