Cluster Analysis for Housing Market Segmentation

Yıl 2021, , 11 - 32, 30.07.2021

Shihomi Ara Aksoy , Elena Irwin

https://doi.org/10.17233/sosyoekonomi.2021.03.01

Cited By: 1

Öz

Cluster analysis is often used to determine housing submarkets. However, commonly used methods cannot handle mixed-mode data when variables of different types and units are combined. We propose new similarity measures that handle both continuous and categorical variables using normalization and discretization steps and partial match criteria. These measures are used in agglomerative hierarchical clustering with a formulation where the optimal number of clusters is automatically determined without a priori information regarding the number of submarkets. The experiments using housing sales data show that the proposed measures perform better than the commonly used standardized Euclidean distance in identifying submarkets.

Anahtar Kelimeler

Housing Market Segmentation, Hierarchical Clustering, Mixed-Mode Data, Hedonic Price Model

Destekleyen Kurum

National Science Foundation Grant

Proje Numarası

DEB-0410336

Konut Piyasası Bölümlendirmesinde Kümelenme Analizi

Öz

Kümeleme analizi, konutların bir dizi değişkene dayalı olarak benzerliklerine göre gruplandırıldığı alt pazarları belirlemek için kullanılan popüler bir yöntemdir. Ancak, yaygın olarak kullanılan yöntemler, farklı tür ve birimlerdeki değişkenlerin bir arada kullanıldığı verileri doğrudan işleyemez. Bu çalışmada, düzgeleme ve ayrıklaştırma adımlarını ve kısmî eşleşme kriterlerini kullanarak hem sürekli hem de kategorik değişkenleri aynı çerçevede ele alabilen yeni benzerlik ölçümleri öneriyoruz. Bu ölçümler, alt pazarların sayısına ilişkin ön bilgi olmadan optimum küme sayısının otomatik olarak belirlendiği bir formülasyon ile aglomeratif hiyerarşik kümelemede kullanılmaktadır. Konut satış verilerini kullanan deneylerde, önerilen benzerlik ölçümleri, alt pazarların belirlenmesinde yaygın olarak kullanılan standartlaştırılmış Öklid mesafesinden daha iyi performans göstermektedir.

Anahtar Kelimeler

Konut Piyasası Bölümlendirmesi, Hiyerarşik Kümeleme, Karışık Veri, Hedonic Fiyat Modeli

Proje Numarası

DEB-0410336

Kaynakça

• Aksoy, S. & R.M. Haralick (2001), “Feature normalization and likelihood-based similarity measures for image retrieval”, Pattern Recognition Letters, 22(5), 563-582.
• Anselin, L. (1988), Spatial Econometrics: Methods and Models, Dordrecht: Kluwer Academic Publishers.
• Anselin, L. (1990), “Spatial dependence and spatial structural instability in applied regression analysis”, Journal of Regional Science, 30(2), 185-209.
• Bates, L.K. (2006), “Does Neighborhood Really Matter? Comparing historically Defined neighborhood boundaries with housing submarkets”, Journal of Planning Education and Research, 26, 5-17.
• Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer, New York, USA.
• Boberg, J. & T. Salakoski (1993), “General formulation and evaluation of agglomerative clustering methods with metric and non-metric distances”, Pattern Recognition, 26(9), 1395-1406.
• Bourassa, S.C. & F. Hamelink & M. Hoesli & B.D. MacGregor (1999), “Defining housing submarkets”, Journal of Housing Economics, 8, 160-183.
• Clapp, J.M. & Y. Wang (2006), “Defining neighborhood boundaries: Are census tracts obsolete?”, Journal of Urban Economics, 59, 259-284.
• Day, B. (2003), “Submarket identification in property markets: A hedonic housing price model for Glasgow”, Technical Report, The Centre for Social and Economic Research on the Global Environment, School of Environmental Sciences, University of East Anglia, Norwich, UK.
• Everitt, B.S. & S. Landau & M. Leese (2001), Cluster Analysis, Fourth Edition. Arnold, London, UK.
• Galster, G.G. (2003), “Neighborhood dynamics and housing markets”, in: T. O’Sullivan & K. Gibb (eds), Housing Economics and Public Policy, Oxford: Blackwell.
• Gillen, K. & T.G. Thibodeau & S. Wachter (2001), “Anisotropic autocorrelation in house prices”, Journal of Real Estate Finance and Economics, 23(1), 5-30.
• Goetzmann, W.N. & S.M. Wachter (1995), “Clustering methods for real estate portfolios”, Real Estate Economics, 23(3), 271-310.
• Goodman, A.C. & T.G. Thibodeau (2003), “Housing market segmentation and hedonic prediction accuracy”, Journal of Housing Economics, 12, 181-201.
• Gower, J.C. (1971), “A general coefficient of similarity and some of its properties”, Biometrics, 27, 857-872.
• Hoesli, M. & C. Lizieri & B. MacGregor (1997), “The spatial dimensions of the investment performance of UK commercial property”, Urban Studies, 34(9), 1475-1494.
• Kelejian, H. & I.R. Prucha (1998), “A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances”, Journal of Real Estate Finance and Economics, 17, 99-121.
• Kim, K. & J. Park (2005), “Segmentation of the housing market and its determinants: Seoul and its neighbouring new towns in Korea”, Australian Geographer, 36(2), 221-232.
• Maclennan, D. & Y. Tu (1996), “Economic perspectives on the structure of local housing systems”, Housing Studies, 11(3), 387-406.
• Milligan, G.W. & M.C. Cooper (1985), “An examination of procedures for determining the number of clusters in a data set”, Psychometrika, 50, 159-179.
• Theodoridis, S. & K. Koutroumbas (2006), Pattern Recognition, 3rd edition. Elsevier, USA.
• Tu, Y. & H. Sun & S.M. Yu (2007), “Spatial autocorrelations and urban housing market segmentation”, Journal of Real Estate Finance and Economics, 34, 385-406. Witten, I.H. & E. Frank (2005), Data Mining, 2nd edition. Elsevier, USA.