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Predicting days on market to optimize real estate sales strategy
ID
Castelli, Mauro
(
Author
),
ID
Dobreva, Maria
(
Author
),
ID
Henriques, Roberto
(
Author
),
ID
Vanneschi, Leonardo
(
Author
)
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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.
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
Views:
1064
Downloads:
378
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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
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