Author(s):
Castelli, Mauro ; Dobreva, Maria ; Henriques, Roberto ; Vanneschi, Leonardo
Date: 2020
Persistent ID: http://hdl.handle.net/10362/94978
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT;
info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT;
Subject(s): 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).
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.
