Abstract
References
- Akerlof, G.A. (1970). The Market for "Lemons": Quality Uncertainty and the Market Mechanism, Quarterly Journal of Economics, vol. 84, no. 3, pp. 488-500.
- 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.
- 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.
- Elephas: Distributed Deep Learning with Keras & Spark, https://github.com/maxpumperla/elephas
- Goetzmann, W. N., Wachter, S. M. (1995). Clustering methods for real estate portfolios. Real Estate Economics, 23, 271-310.
- 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.
- Jackson, C. (2002). Classifying local retail property markets on the basis of rental growth rates. Urban Studies, 39, 1417-1438.
- Keras: Deep Learning Library for Theano and TensorFlow, https://keras.io/
- Lee, S. (2001). The relative importance of property type and regional factors in real estate returns., Journal of Real Estate Portfolio Management, 7(2), 159-167.
- Mahamad, A. K., Sharifah S., Takashi H. (2010). Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics with Applications. 60, 1078-1087.
- Ratchatakulpat T., Miller P., Marchant T. (2009). Residential real estate purchase decisions in Australia: is it more than location?, International Real Estate Review, 12, 273-294.
- REIDIN, September 2017 Report, http://blog.reidin.com/reidin-turkey-real-estate-indices-september-2017-results/
- TurkStat, Press Releases, http://www.turkstat.gov.tr/PreTabloArama.do?metod=search&araType=hb_x
- Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., and Stoica, I. (2010). Spark: cluster computing with working sets. HotCloud, 10, 1010.
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.
References
- Akerlof, G.A. (1970). The Market for "Lemons": Quality Uncertainty and the Market Mechanism, Quarterly Journal of Economics, vol. 84, no. 3, pp. 488-500.
- 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.
- 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.
- Elephas: Distributed Deep Learning with Keras & Spark, https://github.com/maxpumperla/elephas
- Goetzmann, W. N., Wachter, S. M. (1995). Clustering methods for real estate portfolios. Real Estate Economics, 23, 271-310.
- 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.
- Jackson, C. (2002). Classifying local retail property markets on the basis of rental growth rates. Urban Studies, 39, 1417-1438.
- Keras: Deep Learning Library for Theano and TensorFlow, https://keras.io/
- Lee, S. (2001). The relative importance of property type and regional factors in real estate returns., Journal of Real Estate Portfolio Management, 7(2), 159-167.
- Mahamad, A. K., Sharifah S., Takashi H. (2010). Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics with Applications. 60, 1078-1087.
- Ratchatakulpat T., Miller P., Marchant T. (2009). Residential real estate purchase decisions in Australia: is it more than location?, International Real Estate Review, 12, 273-294.
- REIDIN, September 2017 Report, http://blog.reidin.com/reidin-turkey-real-estate-indices-september-2017-results/
- TurkStat, Press Releases, http://www.turkstat.gov.tr/PreTabloArama.do?metod=search&araType=hb_x
- Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., and Stoica, I. (2010). Spark: cluster computing with working sets. HotCloud, 10, 1010.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | March 30, 2018 |
| Published in Issue | Year 2018 Volume: 7 Issue: 1 |
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