As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.

The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice.

Lithuania is one of the EU Member States, where the rate of energy consumption is comparatively low but consumption of electricity has been gradually increasing over the last few years. Despite this trend, households in only three EU Member States consume less electricity than Lithuanian households. The purpose of this research is to analyse the impact of socio-economic factors on the domestic electricity consumption in Lithuania, i.e., to establish whether electricity consumption is determined by socio-economic conditions or population’s awareness to save energy. Cointegration analysis, causality test and error-correction model were used for the analysis. The results reveal that there is a long run equilibrium relationship between residential electricity consumption per capita and GDP at current prices as well as the ratio of the registered unemployed to the working-age population. In consequence, the results of the research propose that improvement of living standards for Lithuanian community calls for the necessity to pay particular attention to the promotion of sustainable electricity consumption by providing consumers with appropriate information and feedback in order to seek new energy-related consumption practices.

The COVID-19 pandemic caused a number of challenges worldwide regarding not only the human health perspective, but also the economic situation. Quarantine, imposed in many countries, forced a substantial part of businesses to close or narrow down their activities, thus leaving corporations and employees without any or with lower income. If national governments had not undertaken any actions to save national economies, the consequences could have been even more devastating. The real estate market is an important part of economy. Instability in the real estate market can cause financial problems, vulnerability of population’s welfare and other negative effects. This research aims to assess the impact of the economic stimulus measures on the real estate market under the conditions of the COVID-19 pandemic in Lithuania. The research methods include comparative analysis, correlation analysis, stationarity test, regression analysis and the ARDL models. The results indicate that the economic stimulus measures only partially contribute to stabilization of the real estate market in Lithuania. The drop in housing prices was 2.9 percent lower because of the economic stimulus in the second quarter of 2020. Maintenance of household cash and deposits as well as lending to business enterprises are the measures that allow to stabilize the real estate market in the shortest time under the conditions of the economic shock. The other governmental support measures are also important, especially if they are aimed at preserving jobs.