<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution 4.0 International License.

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....

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

Publié en ligne: 13 août 2021

Pages: 39 - 51

DOI: https://doi.org/10.2478/remav-2021-0020

Mots clés
regional policy recommendations, machine learning, multivariate time series cluster analysis

© 2021 Valentas Gružauskas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Publié en ligne: 13 août 2021

Pages: 39 - 51

DOI: https://doi.org/10.2478/remav-2021-0020

Mots clésregional policy recommendations, machine learning, multivariate time series cluster analysis

© 2021 Valentas Gružauskas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
regional policy recommendations, machine learning, multivariate time series cluster analysis