TITLE:
Statistical Modeling of Rent Per Square Meter in Munich City, Germany
AUTHORS:
Ugochukwu Onumadu
KEYWORDS:
Statistical Modeling, Munich Housing Market, Rent Price Modeling, Multiple Linear Regression
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.13 No.9,
September
23,
2025
ABSTRACT: This study explores a comprehensive statistical model for analyzing rental apartment prices per square meter in Munich, Germany. The research investigates key quantitative and qualitative variables influencing rent dynamics by leveraging a robust dataset comprising over 2.6 million apartments with 59 variables, sourced from FDZ Ruhr and ImmobilienScout24, for the years 2015 and 2019. Thirty-one key variables (9 quantitative and 22 qualitative) were analyzed, and the study identified significant predictors, such as apartment size, furnishing quality, energy efficiency, and amenity availability, through exploratory data analysis and multiple linear regression with nonlinear covariates. Applying log transformations and polynomial terms improved model performance, with the 2019 model achieving an adjusted R-squared of over 0.54 in the Analysis Of Variance (ANOVA) ratio tests. Model diagnostics, including the Akaike Information Criterion (AIC), residual plots, and Variance Inflation Factor (VIF), were employed to assess model fit and multicollinearity, ensuring the robustness and validity of the regression model. The results indicate a consistent trend where larger apartments and permitting pets command lower rent per square meter, while upscale furnishings, kitchens, and the number of bedrooms are associated with higher prices. This study provides meaningful predictive analytics insights into urban housing and Munich’s evolving rental market. The findings provide valuable insights for real estate planning, sustainable housing policies, urban development strategies, and educators, particularly for university administrators and planners who can advocate for informed housing policies. This research contributes to academic literature on rent modeling and provides a data-driven foundation for evidence-based decision-making in high-demand urban housing markets.