Dataset Information

Online housing search dataset: Information flows of real estate platform users.

ABSTRACT: This data article describes user-generated data of Funda.nl, the largest online housing market website of the Netherlands. The data contain the inflow and outflow of hits (mouse clicks, opening of webpages, etc.) at the municipality level. The municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. The data capture real behavior of the platform users. The flow data are based on 1.1 billion hits that are made by the users of the website in the first six months of 2018. The underlying data are collected by Google Analytics, the web analytics tool of Google. Funda utilizes the data for platform stability, security, product development, etc. The proprietary data of Funda are used to generate the information flows between municipalities. In the full sample we have 148,216 information flows between municipalities in the Netherlands, among which 313 zero flows. The data include subsamples for different types of platform users as user search intentions range from serious to fully recreational. The data enable researchers to analyze housing search behavior from a novel perspective. The data are, for instance, relevant for housing market researchers, digital economists, and economic geographers.

SUBMITTER: Steegmans J

PROVIDER: S-EPMC8411214 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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

Online housing search dataset: Information flows of real estate platform users.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets