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 custom...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-22
Main Authors: Vanneschi, Leonardo, Henriques, Roberto, Dobreva, Maria, Castelli, Mauro
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Customers
Data analysis
Data mining
Forecasts and trends
Fraud
Housing prices
Interest rates
Legislation
Machine learning
Market entry
Neural networks
Prediction models
Product development
Real estate
Real estate sales
Real property
Startups
Statistical analysis
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/4603190