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Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models

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Veröffentlicht in: Journal of business economics and management 20(2019), 5, Seite 920-938
Personen und Körperschaften: Soy Temür, Ayşe (VerfasserIn), Akgün, Melek (VerfasserIn), Temür, Günay (VerfasserIn)
Titel: Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models/ Ayşe Soy Temür, Melek Akgün, Günay Temür
Format: E-Book-Kapitel
Sprache: Englisch
veröffentlicht:
2019
Gesamtaufnahme: : Journal of business economics and management, 20(2019), 5, Seite 920-938
, volume:20
Schlagwörter:
Aufsatz in Zeitschrift
Quelle: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
Details
Zusammenfassung: Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1) - 2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values ​​obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.
ISSN: 2029-4433
DOI: 10.3846/jbem.2019.10190
Zugang: Open Access