Forecasting Residential Real Estate Prices Using Listing Data : Descriptive research on Finnish Market
Eloranta, Eero Matias (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024053119156
https://urn.fi/URN:NBN:fi:amk-2024053119156
Tiivistelmä
This thesis investigates the use of machine learning models to accurately identify mispriced residential real estate properties by analyzing listing data. Unlike prior studies that incorporate various data types, this research focuses specifically on numeric and spatial data from Finnish real estate listings and actual transaction prices. The objective is to develop a methodology that can accurately identify properties listed at prices significantly different from their market value.
The study focuses on the Finnish real estate market to develop and evaluate machine learning models specifically aimed at detecting mispriced properties. By processing and analyzing this data, the research employs several machine learning models to predict property prices. Through feature engineering, model selection, and rigorous testing, the study aims to determine the most effective algorithms for detecting mispriced properties. The evaluation of model performance focuses on the accuracy of price predictions and the capability to spot discrepancies between listing and actual sale prices.
The findings reveal significant insights into the dynamics of the Finnish real estate market and the effectiveness of machine learning models in identifying mispriced properties. The models demonstrate a high degree of accuracy in forecasting property prices, which can help investors and market analysts recognize undervalued or overvalued listings. This underscores the practical implications of advanced data analytics in real estate, offering new strategies for investment and portfolio management.
In conclusion, this thesis not only enhances the academic understanding of machine learning applications in real estate price prediction but also provides practical tools for market participants to identify mispriced properties. The results advocate for a data-driven approach to real estate investment, highlighting the importance of continuous data enhancement and the integration of macroeconomic factors in future research. As the real estate market evolves, the use of advanced analytics and machine learning will be crucial in shaping investment strategies and market analysis.
The study focuses on the Finnish real estate market to develop and evaluate machine learning models specifically aimed at detecting mispriced properties. By processing and analyzing this data, the research employs several machine learning models to predict property prices. Through feature engineering, model selection, and rigorous testing, the study aims to determine the most effective algorithms for detecting mispriced properties. The evaluation of model performance focuses on the accuracy of price predictions and the capability to spot discrepancies between listing and actual sale prices.
The findings reveal significant insights into the dynamics of the Finnish real estate market and the effectiveness of machine learning models in identifying mispriced properties. The models demonstrate a high degree of accuracy in forecasting property prices, which can help investors and market analysts recognize undervalued or overvalued listings. This underscores the practical implications of advanced data analytics in real estate, offering new strategies for investment and portfolio management.
In conclusion, this thesis not only enhances the academic understanding of machine learning applications in real estate price prediction but also provides practical tools for market participants to identify mispriced properties. The results advocate for a data-driven approach to real estate investment, highlighting the importance of continuous data enhancement and the integration of macroeconomic factors in future research. As the real estate market evolves, the use of advanced analytics and machine learning will be crucial in shaping investment strategies and market analysis.