Service charges are an important, but not much researched, financial component of rental market. It comprises of a significant portion of warm rent in Germany and finding out its determinants is important to take measures to reduce the rent or find an apartment based on one's budget. This thesis establishes an economic framework for investigating the determinants and their interpretation for determining the service charges in rental market. It uses over 55,000 Munich’s rental housing market advertisement data posted between January 2020 until December 2022. Munich has been an epicentre of real estate investment because of its robust economy and stability in housing demand due to its strong job market, presence of renowned companies, universities and research institutions, which attracts young professionals, families, and students from all over the world. Over the years with growing industries and job opportunities, it has seen a steep rent increase and has become Germany’s most expensive city. The unusual study period of 2020–2022 includes the conclusion of COVID-19 and the beginning of Russia–Ukraine war, which brought about notable changes in market dynamics, especially for the energy sector. The average service cost rate and service charge-to-rent percentage in December 2021 increased from a long-term stable value of 3.1 €/m2 and 14.5% to a peak of 4 €/m2 and 16.5% by the end of 2022. An extensive data preparation is done in the original dataset to reduce the original 127 columns to the 22 most relevant columns for modelling. The study then employs statistical and machine learning techniques to pinpoint the key determinants influencing service prices. It then assesses the most important contributing determinants from the list of all significant determinants’ list. Empirical results align with theory, indicating, service charges are highly dependent on the type, location, size, age, and heating source of a dwelling. It also shows that properties close to city centres often have higher service costs. The addition of facilities like parking, lifts, and balcony terraces also raises service expenses. The study employs advanced visualization techniques, developed from scratch using Python, to create detailed maps of Munich. These maps incorporate GeoJSON data of Munich along with the original company data to represent zip codes with microscopic precision, allowing for the direct plotting of various statistics and simulation results. This approach facilitates better and easier-to-understand interpretation of the data. Later sections of the study simulate some cases including the projection of minimal net salary necessary to rent home in Munich and change of affordability with building age. The study concludes with policy recommendations for drafting income hike policies and ways to reduce the service charge and ultimately the total rent.     «

Service charges are an important, but not much researched, financial component of rental market. It comprises of a significant portion of warm rent in Germany and finding out its determinants is important to take measures to reduce the rent or find an apartment based on one's budget. This thesis establishes an economic framework for investigating the determinants and their interpretation for determining the service charges in rental market. It uses over 55,000 Munich’s rental housing market adve...     »