Research Article

PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK

Volume: 7 Number: 1 March 30, 2018
Journal of Business Economics and Finance Cover
Journal of Business Economics and Finance
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PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK

Abstract

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. 

Keywords

References

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  2. Alessandri, T., Cerrato, D. and Depperu, D. (2014). Organizational slack, experience, and acquisition behavior across varying economic environments, Management Decision, vol. 56, no. 5, pp. 967-982.
  3. Baker, B. (2001). Residential Rental Real Estate: An Investment in Need of a Theory. Pacific Rim Real Estate Society Conference, Christchurch, New Zealand, 20-23 January 2001.
  4. Elephas: Distributed Deep Learning with Keras & Spark, https://github.com/maxpumperla/elephas
  5. Goetzmann, W. N., Wachter, S. M. (1995). Clustering methods for real estate portfolios. Real Estate Economics, 23, 271-310.
  6. Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and Schmidhuber (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems. 99, 1-11.
  7. Jackson, C. (2002). Classifying local retail property markets on the basis of rental growth rates. Urban Studies, 39, 1417-1438.
  8. Keras: Deep Learning Library for Theano and TensorFlow, https://keras.io/

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Publication Date

March 30, 2018

Submission Date

January 22, 2018

Acceptance Date

-

Published in Issue

Year 2018 Volume: 7 Number: 1

DOI

https://doi.org/10.17261/Pressacademia.2018.801

IZ

https://izlik.org/JA77ZD33PM

Cite

RIS / Bibtex
APA
Erpolat Tasabat, S., Aydin, O., & Hepsen, A. (2018). PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK. Journal of Business Economics and Finance, 7(1), 124-129. https://doi.org/10.17261/Pressacademia.2018.801
AMA
1.Erpolat Tasabat S, Aydin O, Hepsen A. PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK. JBEF. 2018;7(1):124-129. doi:10.17261/Pressacademia.2018.801
Chicago
Erpolat Tasabat, Semra, Olgun Aydin, and Ali Hepsen. 2018. “PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK”. Journal of Business Economics and Finance 7 (1): 124-29. https://doi.org/10.17261/Pressacademia.2018.801.
EndNote
Erpolat Tasabat S, Aydin O, Hepsen A (March 1, 2018) PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK. Journal of Business Economics and Finance 7 1 124–129.
IEEE
[1]S. Erpolat Tasabat, O. Aydin, and A. Hepsen, “PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK”, JBEF, vol. 7, no. 1, pp. 124–129, Mar. 2018, doi: 10.17261/Pressacademia.2018.801.
ISNAD
Erpolat Tasabat, Semra - Aydin, Olgun - Hepsen, Ali. “PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK”. Journal of Business Economics and Finance 7/1 (March 1, 2018): 124-129. https://doi.org/10.17261/Pressacademia.2018.801.
JAMA
1.Erpolat Tasabat S, Aydin O, Hepsen A. PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK. JBEF. 2018;7:124–129.
MLA
Erpolat Tasabat, Semra, et al. “PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK”. Journal of Business Economics and Finance, vol. 7, no. 1, Mar. 2018, pp. 124-9, doi:10.17261/Pressacademia.2018.801.
Vancouver
1.Semra Erpolat Tasabat, Olgun Aydin, Ali Hepsen. PREDICTION OF RESIDENTIAL GROSS YIELDS BY USING A DEEP LEARNING METHOD ON LARGE SCALE DATA PROCESSING FRAMEWORK. JBEF. 2018 Mar. 1;7(1):124-9. doi:10.17261/Pressacademia.2018.801

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