Abstract
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.
Highlights
The socio-economic development of a country, municipality or region can be defined by a set of indicators
The socio-economic situation is usually evaluated annually according to the indicator values in the respective year, the variation of indicators can be presented as multivariate time series
The multivariate time series clustering for socio-economic indicators of municipalities enables municipalities with similar development to be identified based on the relationships between the indicators and their trends
Summary
The socio-economic development of a country, municipality or region can be defined by a set of indicators. The multivariate time series clustering for socio-economic indicators of municipalities enables municipalities with similar development to be identified based on the relationships between the indicators and their trends. The proposed methodology can be applied for other reasons to analyze the real estate market. The reviewed methods focus mainly on spatial clustering approaches, and only briefly mention the importance of temporal analysis, the proposed methodological approach of our publication could provide a tool for market segmentation. Salvati and Carlucci (2014) proposed a sustainable development indicator for Italy; the research paper combined a wide range of variables and used a statistical tool to determine the composite indicator weights. One category of indicators is based on the experts’ decision as to what weights and indicators should be used in the composite indicator Another category employs statistical tools, such as principal component analysis, factor analysis, clustering analysis and others. The goal of these indicators is to quantify the vol 29, no. 3, 2021
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