Dergiler / Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2022 / 4th International Conference on Advanced Engineering Technologies

Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini

Housing Demand Forecasting with Machine Learning Methods

Sayfa
36–52
Yayın Tarihi
2022-12-23
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met.
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Temel Bulgular & Veri:The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing.
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Sonuç & Katkı:As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Forecasting · Housing Demand · Housing Sales

Abstract

Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met. With the acceleration of urbanization, new needs arise, and the first of these is the need for housing. The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing. The need for adequate and accessible housing comes to the forefront in our country as well as in the world. Understanding and predicting the key features determining housing prices and value is an important consideration for urban planners and housing policymakers. In this study, machine learning and artificial neural network models were used to predict the housing demand of Konya, and their forecasting performances were compared. As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.

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Bu Makaleye Atıf Yap

EMEÇ, Ş., TEKİN, D. (2022). Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4th International Conference on Advanced Engineering Technologies, 36–52. https://ajindex.com/articles/859251

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