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Cluster Analysis for Housing Market Segmentation

Year 2021, Volume 29, Issue 49, 11 - 32, 30.07.2021
https://doi.org/10.17233/sosyoekonomi.2021.03.01

Abstract

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.

References

  • 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.

Konut Piyasası Bölümlendirmesinde Kümelenme Analizi

Year 2021, Volume 29, Issue 49, 11 - 32, 30.07.2021
https://doi.org/10.17233/sosyoekonomi.2021.03.01

Abstract

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.

References

  • 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.

Details

Primary Language English
Subjects Economics
Journal Section Articles
Authors

Shihomi ARA AKSOY (Primary Author)
Hacettepe University
0000-0003-3424-2561
Türkiye


Elena IRWİN This is me
The Ohio State University
0000-0000-0000-0000
United States

Supporting Institution National Science Foundation Grant
Project Number DEB-0410336
Publication Date July 30, 2021
Published in Issue Year 2021, Volume 29, Issue 49

Cite

Bibtex @research article { sosyoekonomi855985, journal = {Sosyoekonomi}, issn = {1305-5577}, address = {}, publisher = {Sosyoekonomi Society}, year = {2021}, volume = {29}, pages = {11 - 32}, doi = {10.17233/sosyoekonomi.2021.03.01}, title = {Cluster Analysis for Housing Market Segmentation}, key = {cite}, author = {Ara Aksoy, Shihomi and Irwin, Elena} }
APA Ara Aksoy, S. & Irwin, E. (2021). Cluster Analysis for Housing Market Segmentation . Sosyoekonomi , 29 (49) , 11-32 . DOI: 10.17233/sosyoekonomi.2021.03.01
MLA Ara Aksoy, S. , Irwin, E. "Cluster Analysis for Housing Market Segmentation" . Sosyoekonomi 29 (2021 ): 11-32 <https://dergipark.org.tr/en/pub/sosyoekonomi/issue/64335/855985>
Chicago Ara Aksoy, S. , Irwin, E. "Cluster Analysis for Housing Market Segmentation". Sosyoekonomi 29 (2021 ): 11-32
RIS TY - JOUR T1 - Cluster Analysis for Housing Market Segmentation AU - Shihomi Ara Aksoy , Elena Irwin Y1 - 2021 PY - 2021 N1 - doi: 10.17233/sosyoekonomi.2021.03.01 DO - 10.17233/sosyoekonomi.2021.03.01 T2 - Sosyoekonomi JF - Journal JO - JOR SP - 11 EP - 32 VL - 29 IS - 49 SN - 1305-5577- M3 - doi: 10.17233/sosyoekonomi.2021.03.01 UR - https://doi.org/10.17233/sosyoekonomi.2021.03.01 Y2 - 2021 ER -
EndNote %0 Sosyoekonomi Cluster Analysis for Housing Market Segmentation %A Shihomi Ara Aksoy , Elena Irwin %T Cluster Analysis for Housing Market Segmentation %D 2021 %J Sosyoekonomi %P 1305-5577- %V 29 %N 49 %R doi: 10.17233/sosyoekonomi.2021.03.01 %U 10.17233/sosyoekonomi.2021.03.01
ISNAD Ara Aksoy, Shihomi , Irwin, Elena . "Cluster Analysis for Housing Market Segmentation". Sosyoekonomi 29 / 49 (July 2021): 11-32 . https://doi.org/10.17233/sosyoekonomi.2021.03.01
AMA Ara Aksoy S. , Irwin E. Cluster Analysis for Housing Market Segmentation. Sosyoekonomi. 2021; 29(49): 11-32.
Vancouver Ara Aksoy S. , Irwin E. Cluster Analysis for Housing Market Segmentation. Sosyoekonomi. 2021; 29(49): 11-32.
IEEE S. Ara Aksoy and E. Irwin , "Cluster Analysis for Housing Market Segmentation", Sosyoekonomi, vol. 29, no. 49, pp. 11-32, Jul. 2021, doi:10.17233/sosyoekonomi.2021.03.01