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.

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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.

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.

Purpose This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania. Design/methodology/approach The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations. Findings The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle. Practical implications Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary. Originality/value This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.

Labour productivity is one of the key drivers for higher earnings and welfare standards in every economy. The problem of how to ensure the growth of labour productivity is especially relevant to less developed economies and forces justification of the factors affecting sustainable productivity growth. The purpose of this research is to test if the investment in tangible assets improves labour productivity in the European manufacturing industry and to reveal the countries with inefficient investment. The results show that with consideration of all European countries, a 1% increase in gross investment in tangible goods (G.I.T.G.) per person employed (P.E.) has a 0.0373% long-run effect on apparent labour productivity (A.L.P.). Considering various types of investments in tangibles, only an increase in gross investment in existing buildings and structures (G.I.E.B.S.) per P.E. and gross investment in machinery and equipment (G.I.M.E.) per P.E. caused growth of A.L.P. However, the impact of investment in assets on A.L.P. significantly differs among the countries and it is revealed that many European countries, which are characterised by low productivity, use investment inefficiently.

The problem of Lithuanian GDP prediction is relevant. There are several institutions, such as Statistics Lithuania, state's central bank, other banks that constantly announce their predictions of GDP. Frequently the forecasts of different institutions vary because they use different methods. The main purpose of the paper is to investigate whether regression models made of the monthly published economic indicators or time series models are better for Lithuanian GDP prediction. The changes of Lithuanian GDP as well as many other economic indicators that have impact on GDP are published quarterly. Prediction of quarterly economic indicators as it was done by the most researchers can be related to greater errors comparing with the prediction models that are made according to the monthly data. Monthly data can ensure that the newest information is used for prediction of GDP and show how the state's economy is changing in the current quarter, that's why it can reduce the error of prediction. The research is based on the economic data that is measured and published monthly by Statistics Lithuania (154 ratios at all). Various linear and non-linear regression models are made in order to find the best model for Lithuanian GDP prediction. The results of regression models are also compared with the results got by ARIMA (time series) models. The analysis showed that the regression models made of monthly published economic indicators may be better than time series models for prediction or Lithuanian GDP.

This paper analyses the link between the tangible investment rate and apparent labour productivity in the European manufacturing industry. The research results show a negative and opposite relation between apparent labour productivity and investment rate, that is, changes in apparent labour productivity cause changes in investment in tangible assets but not vice versa. The findings do not show any significant differences among European countries when the relation between apparent labour productivity and investment rate is analysed. However, when analysing the gross investment in tangible goods, as well as in machinery and equipment, period effects are observed. A crisis and economic slowdown reduce investment in tangible capital. Meanwhile, the growth of the economy spurs more investment. The negative correlation between apparent labour productivity and investment rate indicates that investment in tangible assets is ineffective. An analysis on individual countries is required in order to reach more nuanced conclusions.