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

Housing Demand Forecasting with Machine Learning Methods

Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini

Pages
36–52
Publication Date
2022-12-23
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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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Key Findings & Data:The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing.
💡
Conclusion & Impact:As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables: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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Cite This Article

EMEÇ, Ş., TEKİN, D. (2022). Housing Demand Forecasting with Machine Learning Methods. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4th International Conference on Advanced Engineering Technologies, 36–52. https://ajindex.com/articles/859251

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