Prediction of residential gross yields by using a deep learning method on large scale data processing framework
| Autor: | Semra Erpolat Taşabat, Olgun Aydin, Ali Hepsen |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Volume: 7, Issue: 1 124-129 Journal of Business Economics and Finance |
| ISSN: | 2146-7943 |
| DOI: | 10.17261/pressacademia.2018.801 |
| Popis: | Purpose- Households, investors and companies who want to make an investment on residential properties are interested in sales prices and rental values that vary depending on regional factors, location and attributes of residential units. It is the preference of investors to buy a new house with higher rental income. Real estate developers and real estate consultants as well as the real estate investors are also interested in investigating relationship between gross yield rate and location, regional factors, attributes of residential units. The purpose of this study is to examine the relationship between attributes of the residential units, location of the units and the gross yield rate. Methodology - In this study, the prediction model of residential gross yield rates with the help of city, county, district, residential attributes information, was created by using LSTM, which is a deep learning method, on big data platform Spark. Findings- According to test results, it has been proven that gross yield rates could be estimated with high accurate model by the aid of Long short term memories. With this model, researchers can predict gross yield rate of any specific flat. Conclusion- The LSTM network has been built in this study shows that the residential gross yield rate could be estimated using city, county, district, number of rooms, number of bathrooms, floor number, total floor attributes. This study also shows that the Spark framework can be used to deal with the growing size of data in real estate and to develop deep learning applications on distributed data processing platforms. |
| Databáze: | OpenAIRE |
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