The purpose of this thesis is to investigate the possibilities of predicting vacancies in the real estate market by using machine learning models in terms of classification. These models were mainly based on data from contracts between a Swedish real estate company and their tenants. Attributes such as annual renting cost and rental area for each contract were supplemented with additional data regarding financial and geographical information about the tenants. The data was stored in three different formats with the first having binary classes which aim is to predict if the tenant is moving out within a year or more. The format of the second and third version were both multi classification problems that aims to classify if the tenants might terminate their contract within a specific interval with the length of three and six months. Based on the results from Microsoft Azure Machine Learning Studio, it is discovered that the multi classification problems perform rather poorly due to the classes being unbalanced. Regarding the performance of the binary model, a more satisfying result was obtained but not to the extend to say that the model can be used to determine a vacancy with high accuracy. It should rather be used as a risk analysis tool to detect if a tenant is showing tendencies that could result in a future vacancy. A major pitfall of this thesis was the lack of data and the financial information not being specific enough. The performance of the models will likely increase with a larger dataset and more accurate financial information.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-357245 |
| Date | January 2018 |
| Creators | Alemayehu, Brook, Johnsons, Fredrik |
| Publisher | Uppsala universitet, Avdelningen för systemteknik, Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC STS, 1650-8319 ; 18034 |
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