## 2 Materials and Methods  
### Dataset and Its Statistical Characteristics  
The dataset utilized in this study of apartment sales was extracted on 22 July 2024, from the Kazakhstan real estate sales websites. Data collection was facilitated through a custom scraper developed using Python and the BeautifulSoup library [36]. The examination of this comprehensive dataset of apartment sales in Kazakhstan serves multiple critical objectives. Primarily, it facilitates a deeper understanding of the dynamics within the real estate market in Kazakhstan, encompassing price trends, demand patterns, and the influence of diverse apartment features on pricing. This granular analysis aids stakeholders, including investors, developers, and policymakers, in making well-informed decisions. Furthermore, real estate prices and sale volumes are indicative of economic vitality; thus, analyzing such data provides insights into the region's economic status and growth prospects. Detailed insights into buyer preferences--such as the importance of location, building materials, and features like balconies and parking facilities--inform marketing strategies and development plans. The development of predictive models for apartment pricing, which incorporate multiple variables, proves beneficial for buyers, sellers, and real estate agents in understanding fair market values and anticipating future price trends. This study also provides empirical insights to local governments and urban planners, supporting the enhancement of urban planning, housing policies, and regulations based on the current housing conditions, offered amenities, and building states. The employment of a custom scraper and sophisticated data analysis techniques underscores the role of technology in the collection and processing of real estate data, potentially setting a benchmark for analogous studies in different locales or sectors. By gathering and scrutinizing a vast array of characteristics across a significant number of observations, this research aims to provide a detailed portrait of Kazakhstan's housing market, thus delivering invaluable knowledge to various stakeholders engaged in the real estate domain. The dataset utilized for studying apartment sales in Kazakhstan is notably expansive and detailed, covering a broad geographical scope and a wide range of property characteristics. The dataset comprises a significant total of 145,188 observations. It includes data from various locations across Kazakhstan, providing a comprehensive view of the real estate market dynamics within the country. The collected data encompassed various apartment characteristics, including price per square meter, number of rooms, total price, location, living area in square meters, primary building material, floor number, total number of floors in the building, name of the residential complex, year of commissioning, apartment condition, mortgage status, balcony presence, balcony type (glazed or not), door type, flooring type, ceiling height, whether the property was a former hosted, exchange possibility, internet access type, parking facilities, landline telephone connection, toilet type, furniture inclusion, and safety features. Descriptive statistics and a comprehensive dataset description are presented in Table 1. It is imperative to acknowledge that the statistical outcomes and descriptions are specific to the dataset and analytical methodologies employed. A detailed examination of each variable is recommended to fully understand their implications within the dataset. For instance, the 'room' variable indicates the number of rooms in the property, 'price' reflects the overall cost, 'pricepersquare' denotes the price per square meter,'square' represents the total living area, 'hometype' categorizes the construction material of the walls, 'floor' identifies the apartment's level within the building, 'year' signifies the construction date, 'ceiling' details the ceiling height, and'security' describes the level of security in the residential complex.