20.500.12556/RUL-114724 Predicting days on market to optimize real estate sales strategy 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. true false false Angleški jezik Angleški jezik Članek v reviji 2020-03-06 07:52:08 2020-03-06 07:57:05 2023-03-17 13:13:10 0000-00-00 00:00:00 2020 0 0 22 str. art. 4603190 Vol. 2020 2020 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 1099-0526 10.1155/2020/4603190 25563110 18282279 Predicting_Days_on_Market_to_Optimize_Real_Estate_Sales_Strategy.pdf Predicting_Days_on_Market_to_Optimize_Real_Estate_Sales_Strategy.pdf 1 BFF665A5BA7D6AF24792A7430D7DA3B1 ccab4afb23fc3effa04ee1bf945222d6a5bbfed0851cf55acee0c82ce70ece82 3febbebf-a1b8-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=126977 Ekonomska fakulteta 0 0 0