Analyzing Customers’ Demands for Different Housing Features in Buildings Using a Data Mining Method
Auteur(s): |
Abdullah Emre Keleş
Yusuf Can Arıkan |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 14 février 2023, n. 2, v. 13 |
Page(s): | 555 |
DOI: | 10.3390/buildings13020555 |
Abstrait: |
There are many options and factors in the production phase of housing. In the marketing phase, houses are presented to the customer’s taste. Therefore, it is clear that a customer-oriented approach is necessary to establish a supply–demand balance in housing production on the basis of quality. This study aimed to determine customers’ housing demands in the construction sector. Within the scope of the study, 303 surveys were conducted in 30 different provinces of Turkey. The data obtained were analyzed by WEKA software with association rule extraction as the data mining method. The distribution of other attributes was determined according to two different class labels, namely the ownership status of the houses (tenant or homeowner) and customers’ expectations of the houses. As a result of the study, it is clear that people living in Turkey prefer a south-facing facade when purchasing a house. In addition, it is seen that the property owners demand 4 + 1 independent units. It is remarkable that individuals who are tenants want the living room to be spacious. The results of the study also revealed that female individuals have higher expectations of housing than male individuals. At the same time, it is understood that people’s expectations of housing differ according to the variables of age, education level, and the number of family members. The majority of the results in this study had a confidence value of 90% and above. This study was intended to serve as a guide for housing developers in Turkey to better understand and meet the demands of buildings’ residents. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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sur cette fiche - Reference-ID
10712751 - Publié(e) le:
21.03.2023 - Modifié(e) le:
10.05.2023