| Abstract [eng] |
The dynamics of housing prices in Lithuania are highly important from both economic and social perspectives. Constantly rising prices not only erode household purchasing power but also result in housing expenses taking up an increasingly larger share of household budgets. Such market conditions compel individuals to carefully evaluate their housing choices, as these decisions are often tied to their most significant financial commitments. Failure to assess all alternatives and their potential can lead to serious consequences, including financial instability or even negative equity. Therefore, accurate, data-driven housing valuation is becoming increasingly important. While this study includes an overview of housing price indices and the macroeconomic factors influencing their trends, the primary focus is placed on modelling the value of individual real estate properties. The research involved collecting data from the real estate portal aruodas.lt, conducting exploratory data analysis, performing feature engineering, searching for optimal structural parameters, and developing regression models with tuned hyperparameters. Model performance was evaluated using RMSE and MAPE metrics, and the best results across all datasets were achieved using ensemble tree-based algorithms. The best-performing models were then used to conduct feature importance analysis. Using apartment sales data, it was determined that the most important explanatory variables for apartment square meter price were the building’s age and the Euclidean distance to the city center. Towards the end of the study, apartment price modelling was performed using segments derived from the most important explanatory variables. This allowed for the evaluation of model accuracy across different market niches and the development of practical recommendations for individuals planning to purchase an apartment. Additionally, rental prices were also modelled, enabling the calculation of approximate price-to-rent ratios for listed properties. These results provided insights into how long an investment in a particular apartment segment would take to pay off solely through rental income, thereby offering buyers an additional evaluation perspective that integrates the investment aspect of homeownership. |
