In the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variables, by considering the features of the house. In this study, the price of the house was attempted to be predicted using machine learning methods by establishing a model with micro variables that reveal the features of the house. The study was conducted in Turkey’ Antalya province, where household housing demand of foreigners is also high. The house advertisements in locations belonging to the lower, middle- and upper-income groups were selected as the sample. In the results, it was observed that the artificial neural network (ANN) method made predictions with more meaningful results compared to support vector regression (SVR) and multiple linear regression (MLR). These results appear to be a viable model for institutions that supply housing, mediate housing sales, and provide housing financing and valuation. It is considered that this model, which can be used to predict fluctuating house prices, especially in developing countries, will regulate the housing market.

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It includes the systematic examination and mapping of the literature studies written for Society 5.0 using the CiteSpace application, with the scientometrics analysis method, the identification of the network of relations, and the discovery and intellectual analysis of their bibliographic contents. 151 bibliographic records from the Scopus database were analyzed between January 2017 and May 2021. Qualitative analysis of the obtained quantitative data based on the interpretative paradigm was made. Analysis of countries, intellectual analysis, keywords; burst point and cluster analyzes were performed. The case is gaining increasing importance in the multi-disciplinary field. Since the phenomenon of Society 5.0 is still current and concerns the future period, it is seen that it is a developing field in the literature. In the literature about this case; The fact that scientometrics studies and CiteSpace analysis have never been done has increased the originality of the study. It will be important for super-smart society goals to specialize in the fields studied and to increase the working tendencies in the fields that are lacking (sociology, psychology, gerentology, etc. social sciences).

It is considered that the use of renewable energy sources will replace fossil fuels due to global climate change and accompanying decisions taken by states. In this study, unlike the renewable energy production estimation studies in the literature, a model was created by taking the socioeconomic, environmental and energy time series data of the countries. In the study, Turkey, which did not promise a numerical reduction in greenhouse gas emissions unlike other developing countries but has an increasing energy production from renewable energy sources, was chosen. In the study, the data between 1990 and 2020 were used to receive more realistic results by considering the interval before and after the Kyoto protocol. Artificial neural networks and support vector regression among machine learning methods were used to predict the model. As a result of the study, support vector regression had a 92% and artificial neural networks had a successful predictive power of 89.9% according to the coefficient of determination (R 2 ). In the study, the root mean square error value was 0.071 for artificial neural networks and 0.045 for support vector regression; the mean squared error value was 0.005 for artificial neural networks and 0.002 for support vector regression, which was close to the ideal values. Both methods were statistically successful. It is predicted that the model designed because of these successful results obtained in the study would guide the creation of energy policies and contribute to scientific studies.