Dataset Information

Real estate announcements monitoring dataset for Latvia 2018.

ABSTRACT: The dataset represents a collection of real estate announcements published in 2018 in the Latvian leading advertisement website www.ss.com [1]. In the Latvian case, mentioned advertisement website is alternative information source in contrast with several (5-7) large real estate agencies. The mentioned advertisement website has no important competitors in Latvia, closer competitor reklama. lv [2] is 4-5 times smaller. Advertisement website www.ss.com represents information from small and medium size agencies, as well from individuals, who want to take part in the real estate market. The collected dataset reflects the observation dynamics of 12 months during 2018, including in total 238 thousand observations. Dataset has 24 dimensions, such as in announcement mentioned price for real estate, deal types, dimensions of location of real estate, such as region, district, address; characteristics of real estate, such as real estate type (land, flat and so on), size and main characteristics for each real estate type, such as land area or bad rooms in apartments. The dataset is hosted in Data Archiving and Networked Services (DANS) repository [3].

SUBMITTER: Skribans V

PROVIDER: S-EPMC7096678 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Real estate announcements monitoring dataset for Latvia 2018.

Skribans Valerijs V Jurušs Māris M Demianchuk Maryna M Maslii Natalia N Pastory Dickson D

Data in brief 20191231

The dataset represents a collection of real estate announcements published in 2018 in the Latvian leading advertisement website www.ss.com [1]. In the Latvian case, mentioned advertisement website is alternative information source in contrast with several (5-7) large real estate agencies. The mentioned advertisement website has no important competitors in Latvia, closer competitor reklama. lv [2] is 4-5 times smaller. Advertisement website www.ss.com represents information from small and medium ...[more]

PMID: 32226813

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Project description:With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

Dataset Information

Real estate announcements monitoring dataset for Latvia 2018.

Publications

Real estate announcements monitoring dataset for Latvia 2018.

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