Časové řady nemovitostního trhu a jejich limitace v kontrastu strojového učení - Hrbek, Petr; Kaizr, Jaroslav; Pláničková, Markéta; Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví (2025)
This master’s thesis compares traditional time-series forecasting models with modern machine learning approaches for predicting house prices in King County, Washington (2013–2022). The study evaluates classical autoregressive models (ARIMA/SARIMA) alongside ensemble gradient boosting algorithms (XGBoost, LightGBM, CatBoost) using an expanding-window backtesting framework in skforecast library. The analysis focuses on the monthly median sale price series for multiple urban submarkets. Model variants are trained on univariate price-only time series as well as on enriched datasets that incorporate property-specific technical attributes and socio-demographic indicators as exogenous features. Forecast accuracy is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) metrics. The results indicate that machine learning models generally achieve higher predictive accuracy in volatile or irregular submarkets, whereas a seasonal ARIMA can match or even outperform ML models in submarkets with strong seasonal patterns and stable trends. Incorporating extra features (e.g. house attributes, income, population) improves predictions in some submarkets, particularly where past prices alone do not capture important local factors, but yields little benefit or even a slight performance degradation in others with well-structured trends and seasonality. Overall, no single model dominates across all conditions; the optimal modeling approach depends on each submarket’s characteristics.
Vedoucí práce: Jaroslav Kaizr
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2025
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2025
Způsob přístupu: Internet
Keywords: finance a oceňování podniku [obor dipl. práce]; diplomové práce; master's theses