Warianty tytułu
Języki publikacji
Abstrakty
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 Kmeans 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. (original abstract)
Czasopismo
Rocznik
Numer
Strony
39--51
Opis fizyczny
Twórcy
autor
- Kaunas University of Technology, Lithuania
autor
- Kaunas University of Technology
autor
- Kaunas University of Technology
autor
- Kaunas University of Technology, Lithuania
Bibliografia
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- Athey, S. (2019). The Impact of Machine Learning on Economics. The Economics of Artificial Intelligence: An Agenda,. 548-551.
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- Blien, U., Hirschenauer, F., & Thi Hong Van, P. (2010). Classification of regional labour markets for purposes of labour market policy. Papers in Regional Science, 89(4), 859-880. https://doi.org/10.1111/j.1435-5957.2010.00331.x
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- Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. https://doi.org/10.1007/s11205-017-1832-9
- Gružauskas, V., Kriščiūnas, A., Čalnerytė, D., & Navickas, V. (2020). Analytical Method for Correction Coefficient Determination for Applying Comparative Method for Real Estate Valuation. Real Estate Management and Valuation, 28(2), 52-62. https://doi.org/10.1515/remav-2020-0015
- Kazak, J., van Hoof, J., Świąder, M., & Szewrański, S. (2017). Real estate for the ageing society-the perspective of a new market. Real Estate Management and Valuation, 25(4), 13-24. https://doi.org/10.1515/remav-2017-0026
- Kleinert, C., Vosseler, A., & Blien, U. (2018). Classifying vocational training markets. The Annals of Regional Science, 61(1), 31-48. https://doi.org/10.1007/s00168-017-0856-z
- Kokot, S. (2020). Socio-Economic Factors as a Criterion for the Classification of Housing Markets in Selected Cities in Poland. Real Estate Management and Valuation, 28(3), 77-90. https://doi.org/10.1515/remav-2020-0025
- Li, H. (2019). Multivariate time series clustering based on common principal component analysis. Neurocomputing, 349, 239-247. https://doi.org/10.1016/j.neucom.2019.03.060
- Majerova, I., & Nevima, J. (2017). The measurement of human development using the ward method of cluster analysis. Journal of International Students, 10(2), 239-257. https://doi.org/10.14254/2071- 8330.2017/10-2/17
- Manzhynski, S., Siniak, N., Źróbek-Różańska, A., & Źróbek, S. (2016). Sustainability performance in the Baltic Sea Region. Land Use Policy, 57, 489-498. https://doi.org/10.1016/j.landusepol.2016.06.003
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- Salvati, L., & Carlucci, M. (2014). A composite index of sustainable development at the local scale: Italy as a case study. Ecological Indicators, 43, 162-171. https://doi.org/10.1016/j.ecolind.2014.02.021
- Seidel, C., Heckelei, T., & Lakner, S. (2019). Conventionalization of Organic Farms in Germany: An Empirical Investigation Based on a Composite Indicator Approach. Sustainability (Basel), 11(10), 2934. https://doi.org/10.3390/su11102934
- de Senna, L. D., Maia, A. G., & de Medeiros, J. D. F. (2019). The use of principal component analysis for the construction of the Water Poverty Index. RBRH (Brazilian Journal of Water Resources), 24, e19, 1-14. https://doi.org/10.1590/2318-0331.241920180084
- Serra, P., Vera, A., & Tulla, A. F. (2014). Spatial and Socio-environmental Dynamics of Catalan Regional Planning from a Multivariate Statistical Analysis Using 1980s and 2000s Data. European Planning Studies, 22(6), 1280-1300. https://doi.org/10.1080/09654313.2013.782388
- Usman, H., Lizam, M., & Adekunle, M. U. (2020). Property price modelling, market segmentation and submarket classifications: A review. Real Estate Management and Valuation, 28(3), 24-35. https://doi.org/10.1515/remav-2020-0021
- Vilnius Institute of Policy Analysis. (2019). Municipality welfare index.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171628658