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

Yıl 2018, Cilt: 7 Sayı: 1, 124 - 129, 30.03.2018

Semra Erpolat Tasabat , Olgun Aydin Ali Hepsen

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

Öz

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. 

Anahtar Kelimeler

LSTM, real estate, residential gross yield rates, big data

Kaynakça

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