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Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

Author

Listed:
  • Gružauskas Valentas

    (Kaunas University of Technology, Digitalization Scientific Group)

  • Čalnerytė Dalia

    (Kaunas University of Technology)

  • Fyleris Tautvydas

    (Kaunas University of Technology)

  • Kriščiūnas Andrius

    (Kaunas University of Technology)

Abstract

The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.

Suggested Citation

  • Gružauskas Valentas & Čalnerytė Dalia & Fyleris Tautvydas & Kriščiūnas Andrius, 2021. "Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities," Real Estate Management and Valuation, Sciendo, vol. 29(3), pages 39-51, September.
    • Handle: RePEc:vrs:remava:v:29:y:2021:i:3:p:39-51:n:4
    DOI: 10.2478/remav-2021-0020
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    References listed on IDEAS

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    1. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
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    More about this item

    Keywords

    regional policy recommendations; machine learning; multivariate time series cluster analysis;
    All these keywords.

    JEL classification:

    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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