Using Hierarchical Clustering Algorithms for Turkish Residential Market

Abstract

Clustering has a potentially important contribution to real estate portfolio analysis. In this study several hierarchical clustering algorithms are applied to rental returns for seventy-one metropolitan residential markets in Turkey. The aim is to develop homogeneous groupings for real estate portfolios. The historical clustering algorithms documented in this study provides a useful guideline for real estate investors to select appropriate market areas and formulate efficiently diversified investment portfolios. The empirical findings support the three-cluster partition of the districts that reveals a clear rental return distinction of residential markets in Turkey. Cluster 1 is composed of twenty nine districts, which have the lowest rental return levels over the period of 2007:M6 to 2011:M6. Thirty four districts are grouped in Cluster 2. The cities in this group have relatively higher rental returns. The rest eight cities belong to Cluster 3. Rental return levels are distinctively higher than the other two groups. On the other hand, high rental returns are associated with higher levels of risk (standard deviation), and vice versa.

Keywords: Clustering algorithm, Time series Data mining, Residential market, Rental returns

JEL Classification: E30, E44, L74, R10, R20, R30