## 2 Materials and Methods
### Dataset and Its Statistical Characteristics
\begin{table}
\begin{tabular}{l r r r r r r r r r} \hline
**Value/Criteria** & **Room** & **Price** & **Pricepersquare** & **Square** & **Hometype** & **Floor** & **Year** & **Ceiling** & **Security** \\ \hline Average & 2.2397 & 25,785,761 & 422,053.7 & 61.294 & 3.0492 & 4.5114 & 1999 & 2.74 & 4.75 \\ \hline Standard Error & 0.0024 & 47,434.54 & 467.127 & 0.067 & 0.0023 & 0.0081 & 0.053 & 0.000759 & 0.0057 \\ \hline Median & 2 & 23,000,000 & 393,890.7 & 58 & 3 & 4 & 2005 & 2.7 & 5 \\ \hline Mode & 2 & 30,000,000 & 500,000 & 60 & 4 & 5 & 2022 & 2.7 & 5 \\ \hline Standard Deviation & 0.9356 & 18,074,239 & 177,991.9 & 25.7 & 0.9025 & 3.0961 & 20.35 & 0.28 & 2.19 \\ \hline Sample Variance & 0.8754 & \(3.27\times 10^{14}\) & \(3.17\times 10^{10}\) & 660.5 & 0.8145 & 9.5862 & 414.36 & 0.08 & 4.8 \\ \hline Excess & 11.1524 & 175.68 & 838.6201 & 32.472 & \(-1.3909\) & 4.3745 & \(-1.26\) & 62.39 & 9.49 \\ \hline Skew ness & 0.8478 & 7.98 & 10.1154 & 3.157 & \(-0.2507\) & 1.5787 & \(-0.34\) & 5.47 & 2.67 \\ \hline Interval & 29 & \(8.9\times 10^{8}\) & 18,936,170 & 814.5 & 3 & 39 & 130 & 5 & 15 \\ \hline Min & 1 & 1,500,000 & 45,000 & 4.5 & 1 & 1 & 1900 & 1 & 1 \\ \hline Max & 30 & \(8.9\times 10^{8}\) & 18,936,170 & 819 & 4 & 40 & 2030 & 6 & 16 \\ \hline Sum & 325,187 & \(3.74\times 10^{12}\) & \(6.13\times 10^{10}\) & 8,899,208 & 442,719 & 655,014 & \(2.9\times 10^{8}\) & 398,203.7 & 689,765 \\ \hline Count & 145,188 & 145,188 & 145,188 & 145,188 & 145,188 & 145,188 & 145,188 & 145,188 & 145,188 \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of numeric basic housing characteristics. A strong correlation exists between the price of an apartment and its room size. Regional variations also significantly influence prices, with marked differences across different areas of the country. Additionally, the total area of an apartment (square footage) positively impacts its price. The data further reveal that newer buildings tend to house more expensive apartments. There is also a positive correlation between the number of bathrooms in an apartment, the level of security provided, and the corresponding price of the property. For investment strategies, the analysis suggests that purchasing apartments in specific regions with larger square footage at lower prices could be advantageous. For example, apartments requiring renovations generally obtain lower prices. Investors might  
Figure 1: Correlation of numerical indicators of real estate criteria. The heatmap shows correlations between variables, with **green** for strong positive correlations, **red** for strong negative correlations, and **yellow/orange** for weak or no correlation. The diagonal represents a perfect correlation of 1.  
consider acquiring properties with inexpensive flooring materials such as cork, wood, or linoleum, and upgrading to higher-quality materials like parquet or laminate to enhance value. Additionally, increasing the number of bathrooms and enhancing security measures could potentially increase the resale value of these properties. Among other notable correlations, the study observes that older buildings frequently contain apartments with smaller rooms, whereas newer constructions typically offer larger room sizes. There is also a trend towards constructing high-rise buildings, with these structures often featuring higher ceilings and enhanced security measures. Conversely, older buildings are more likely to contain apartments in poorer conditions. Figure 2, generated using the plotmatrix function, displays a matrix of scatter plots, with each plot comparing pairs of real estate criteria. The analysis of these plots reveals distinct regional variations in apartment prices, affirming a strong geographic influence on property values. Notably, apartments constructed with brick command the highest prices, followed by those in monolithic, panel, and other types of buildings, in descending order of price.