## 3 Dataset  
The immo_data dataset was chosen for this research on the German real estate market due to its extensive and broader usage. This dataset was sourced from ImmoScout24, the most prominent and well-recognised real estate portal in Germany, and is publicly accessible at [[https://www.kaggle.com/datasets/corrieaar/apartment-rental-offers-in-germany](https://www.kaggle.com/datasets/corrieaar/apartment-rental-offers-in-germany)]([https://www.kaggle.com/datasets/corrieaar/apartment-rental-offers-in-germany](https://www.kaggle.com/datasets/corrieaar/apartment-rental-offers-in-germany)) (accessed on 11 October 2023). The initial dataset comprised 268,850 entries and 49 variables, providing detailed information on various characteristics of residential units within the German real estate market. Specifically for this study, data from the Munich housing market were extracted, resulting in 4383 records and 49 variables. After feature engineering, the dataset was refined to include 4369 records and 23 features, each representing distinct types of data. The housing attributes are described in Table 1:  
* The Munich property market includes 23 distinct parameters for each residential unit, including living space size, rent, and number of rooms. * The dataset contains a total of 4369 records, of which 20% are specifically designated for testing purposes. * The data comprises four types: objects, floats, integers, and Booleans. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline ServiceCharge & Auxiliary costs (electricity, water, etc.) \\ \hline NewlyConst & Is the building newly constructed? \\ \hline Balcony & Does it have a balcony? \\ \hline Picturecount & How many photos are on the listing? \\ \hline Pricetrend & The price trend as calculated by Immospot \\ \hline TelekomUploadSpeed & How fast is the internet upload speed? \\ \hline TotalRent & Total rent (sum of the base rent and other costs) \\ \hline NoParkSpaces & Number of parking spaces \\ \hline HasKitchen & Has a kitchen \\ \hline Cellar & Has a cellar \\ \hline YearConstructedRange & Binned construction year, 1 to 9 \\ \hline BaseRent & Base rent without electricity and heating \\ \hline LivingSpace & Living space in sqm \\ \hline Lift & Is an elevator available \\ \hline BaseRentRange & Binned base rent, 1 to 9 \\ \hline Geo plz & ZIP code \\ \hline NoRooms & The number of rooms \\ \hline ThermalChar & Energy efficiency class in kWh/(**m\textasci{2a}**) \\ \hline Floor & Which floor is the flat on \\ \hline NumberOfFloors & Number of floors in the building \\ \hline NoRoomsRange & Binned number of rooms, 1 to 5 \\ \hline Garden & Has a garden \\ \hline LivingSpaceRange & Binned living space, 1 to 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset dictionary. However, facilitating the rigorous evaluation and comparative analysis of various algorithms on this dataset is challenging due to its complexity. Improper feature engineering may lead to overfitting. Moreover, it is essential to note that there are several housing datasets available for study on housing prediction, including the Boston, Ames, and Chinese datasets. Currently, there is only one dataset pertaining to the German housing industry that has been made publicly accessible. This dataset stands out due to its distinctive formats, structures, and properties, which may require specific procedures and techniques for processing and analysis. Furthermore, its availability enhances its usefulness and replicability. Considering these factors, the immo_data dataset is well-suited for conducting research in the German market. The immo_data dataset was selected for this study because of its extensive and heterogeneous collection of data that encompass several facets of the German real estate market.