PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS

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dc.contributor.authorTemur, Ayşe Soy
dc.contributor.authorAkgün, Melek
dc.contributor.authorTemur, Gunay
dc.date.accessioned2020-04-30T23:21:14Z
dc.date.available2020-04-30T23:21:14Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionTemur, Gunay/0000-0002-7197-5804en_US
dc.descriptionWOS: 000485882100006en_US
dc.description.abstractHaving 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.en_US
dc.identifier.doi10.3846/jbem.2019.10190en_US
dc.identifier.endpage938en_US
dc.identifier.issn1611-1699
dc.identifier.issn2029-4433
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage920en_US
dc.identifier.urihttps://doi.org/10.3846/jbem.2019.10190
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4159
dc.identifier.volume20en_US
dc.identifier.wosWOS:000485882100006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherVilnius Gediminas Tech Univen_US
dc.relation.ispartofJournal Of Business Economics And Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjecthouse sales forecasten_US
dc.subjecthybrid modelen_US
dc.subjectrecurrent neural networken_US
dc.subjectARIMAen_US
dc.subjectLSTM networken_US
dc.subjectdata estimation methodologyen_US
dc.subjecttime series analysisen_US
dc.subjecthousing sales in Turkeyen_US
dc.titlePREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELSen_US
dc.typeArticleen_US

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