Their study, which analyzed over 188,000 transactions from 2003 to 2010, found that a 10% increase in credit availability leads to an average 1.5% rise in real estate values. Another noteworthy study by [PERSON] et al. [13] assessed the influence of energy price fluctuations on the housing cycle, drawing on quarterly data from 20 European and other countries. The results indicate that while real estate markets in both oil-importing and -exporting countries are impacted by energy price changes, exporters are less susceptible to prolonged housing market downturns. Additionally, French researchers [PERSON] and [PERSON] [14] employed mathematical methods to explore the correlation between the real housing price index, migration balance, and Gross Domestic Product (GDP) levels. They concluded that migration within the 20-49 year age group notably affects housing prices, as this demographic frequently engages in housing investments. Further, researchers from Hong Kong University, [PERSON] and [PERSON] [15], developed linear and semi-logarithmic models to analyze how attributes such as ceiling height, window orientation, road noise level, views of green spaces, and proximity to forests and water bodies influence transaction prices in the real estate market. Their study was based on data provided by developers of four major private residential properties. In a detailed analysis of the Singapore real estate market, a study employing a two-stage least squares regression method was conducted over a 12-year period (1988-2000) [16]. The research focused on the economic and market factors influencing the demand, supply, and pricing of condomiums. The equilibrium model used was based on the theoretical premise where the demand for condomium housing matches its supply. The pricing equation considered various macroeconomic indicators such as GDP, the consumer price index, housing loan interest rates, and the volume of completed condomiums. Anotherstudy by [PERSON] and [PERSON] [17] leveraged data from SouFun, one of China's largest real estate websites, to analyze the Chinese residential property market from 2012 to 2015. The initial dataset included over 64,000 entries across 3742 residential complexes, which, after data cleansing to remove duplicates and outliers, resulted in a sample of 8511 observations. Their findings indicated that the number of bedrooms and living space size were predominant factors affecting property prices. In 2005, Israeli researchers developed a model to depict the real estate dynamics in four residential zones in Haifa, incorporating more than 20 explanatory variables such as the construction year, availability of parking and elevators, number of apartments per floor, total apartment area, and proximity to parks and highways [18]. This model also considered environmental factors, including cleanliness and views. Furthermore, French scholars conducted a regional analysis of the real estate market across 22 areas in France, utilizing models that correlated changes in property prices with GDP per capita, migration, unemployment rates, and the per capita availability of social housing [19]. Their analysis revealed a significant negative impact of immigration on property prices, although the property prices did not show a substantial responsiveness to varying levels of immigration. Machine learning has emerged as a pivotal technique for predicting property prices, particularly due to its capacity to analyze attributes accurately, independent of historical data trends. This attribute is increasingly valuable in the context of the growing prevalence of Big Data. Studies by [PERSON] et al. [20] and [PERSON] [21] have demonstrated the effectiveness of various machine learning strategies, yielding positive results. Recent advancements have expanded the application of machine learning beyond mere analysis to include elements of the selection process in property markets.