Dublin Core
Title
Comparative Analysis of Machine Learning Algorithms for Real Estate Price Prediction: Bosnia and Herzegovina vs. USA
Abstract
Real estate markets are impacted by a variety of variables, including changes in the population, urban development projects, and changes in economic policy. This thesis sets out to investigate the effectiveness of machine learning algorithms in predicting real estate prices, paying close attention to the particular circumstances of Bosnia and Herzegovina as well as the United States. While the US real estate market has a long history and is well-known for its capacity to bounce back from downturns in the economy, the tale of the BiH real estate industry is very different. In contrast to the United States, which has seen centuries of economic expansion, financial crises, and legislative changes, Bosnia and Herzegovina's market development is a result of a combination of past influences and present difficulties. Beyond simple quantitative comparisons, our research takes a holistic method to uncover the predictive capability of machine learning models.
We explore the complexities of random forests and decision trees, making use of their ability to reveal intricate patterns in real estate databases. This research also includes time series modeling to recognize and comprehend the evolving patterns that characterize real estate dynamics throughout time. The analysis of SARIMAX, ARIMA, and Holt-Winters time-series models shows ARIMA's consistent accuracy, while SARIMAX and Holt-Winters excel in stability and trend capture, respectively. In machine learning, Decision Trees offer interpretability, while Random Forests show reduced error rates and enhanced accuracy. In the US dataset, SARIMAX has a Mean Absolute Percentage Error (MAPE) of 3.35% and ARIMA achieves 1.66%, while Holt-Winters shows 3.54%. Decision Trees have a MAPE of 2.97%, and Random Forests achieve 2.10%. In the BiH dataset, SARIMAX has a MAPE of 5.08%, ARIMA achieves 1.22%, while Holt-Winters shows 2.17%. Decision Trees have a MAPE of 0.83%, and Random Forests achieve 0.82%.
We explore the complexities of random forests and decision trees, making use of their ability to reveal intricate patterns in real estate databases. This research also includes time series modeling to recognize and comprehend the evolving patterns that characterize real estate dynamics throughout time. The analysis of SARIMAX, ARIMA, and Holt-Winters time-series models shows ARIMA's consistent accuracy, while SARIMAX and Holt-Winters excel in stability and trend capture, respectively. In machine learning, Decision Trees offer interpretability, while Random Forests show reduced error rates and enhanced accuracy. In the US dataset, SARIMAX has a Mean Absolute Percentage Error (MAPE) of 3.35% and ARIMA achieves 1.66%, while Holt-Winters shows 3.54%. Decision Trees have a MAPE of 2.97%, and Random Forests achieve 2.10%. In the BiH dataset, SARIMAX has a MAPE of 5.08%, ARIMA achieves 1.22%, while Holt-Winters shows 2.17%. Decision Trees have a MAPE of 0.83%, and Random Forests achieve 0.82%.
Keywords
Real Estate, Prediction, Bosnia and Herzegovina, United States