Chapter 18
Using Clustering of Panel Data
to Examine Housing Demand
of Expatriate Turks and Foreigners:
An Application of k-prototype Algorithm
Özlem Akay, Cahit Çelik, and Gülsen Kıral
Abstract In recent years, development in the Turkey’s economic structure and
implementation of the “economic and financial policy” shows significant effects on
the housing sector. For this reason, according to economic factors affecting housing
demand, it is important to find cities and countries showing common characteristics
and to examine the developments in the housing market. In this study, it was aimed
to determine the common characteristics of the provinces that preferred by the Turks
living abroad and foreigners and of the countries that prefer housing demand from
these provinces in Turkey. Two different panel data sets were created for expatriate
Turks and foreigners who prefer the demand for housing in Turkey. Cluster analysis was performed to these data sets using k-prototypes algorithm Cluster validity
indexes were calculated to determine the appropriate number of clusters for both
data sets. According to the findings, while the optimal number of clusters for data
set defined for the expatriate Turk preferring housing demand in Turkey was found
to be six, the optimal number of clusters for the data set defined for foreign countries
preferring housing demand from Turkey was found to be nine.
Keywords Housing demand · Clustering of panel data · k-prototypes algorithm ·
Turkey
Ö. Akay (B)
Department of Statistics, Cukurova University, 01330 Adana, Turkey
e-mail: oakay@cu.edu.tr
C. Çelik · G. Kıral
Department of Econometrics, Cukurova University, 01330 Adana, Turkey
e-mail: cahit.celik@outlook.com
G. Kıral
e-mail: gkiral@cu.edu.tr
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
D. Uzun Ozsahin et al. (eds.), Application of Multi-Criteria Decision Analysis in
Environmental and Civil Engineering, Professional Practice in Earth Sciences,
https://doi.org/10.1007/978-3-030-64765-0_18
183
Using Clustering of Panel Data
to Examine Housing Demand
of Expatriate Turks and Foreigners:
An Application of k-prototype Algorithm
Özlem Akay, Cahit Çelik, and Gülsen Kıral
Abstract In recent years, development in the Turkey’s economic structure and
implementation of the “economic and financial policy” shows significant effects on
the housing sector. For this reason, according to economic factors affecting housing
demand, it is important to find cities and countries showing common characteristics
and to examine the developments in the housing market. In this study, it was aimed
to determine the common characteristics of the provinces that preferred by the Turks
living abroad and foreigners and of the countries that prefer housing demand from
these provinces in Turkey. Two different panel data sets were created for expatriate
Turks and foreigners who prefer the demand for housing in Turkey. Cluster analysis was performed to these data sets using k-prototypes algorithm Cluster validity
indexes were calculated to determine the appropriate number of clusters for both
data sets. According to the findings, while the optimal number of clusters for data
set defined for the expatriate Turk preferring housing demand in Turkey was found
to be six, the optimal number of clusters for the data set defined for foreign countries
preferring housing demand from Turkey was found to be nine.
Keywords Housing demand · Clustering of panel data · k-prototypes algorithm ·
Turkey
Ö. Akay (B)
Department of Statistics, Cukurova University, 01330 Adana, Turkey
e-mail: oakay@cu.edu.tr
C. Çelik · G. Kıral
Department of Econometrics, Cukurova University, 01330 Adana, Turkey
e-mail: cahit.celik@outlook.com
G. Kıral
e-mail: gkiral@cu.edu.tr
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
D. Uzun Ozsahin et al. (eds.), Application of Multi-Criteria Decision Analysis in
Environmental and Civil Engineering, Professional Practice in Earth Sciences,
https://doi.org/10.1007/978-3-030-64765-0_18
183