Modeling and Forecasting Rental Prices in Berlin Using AI with Nonlinear Covariates for Housing Policy and Social Equity

This study employs machine learning techniques (AI), specifically multiple linear regression with nonlinear covariates, to model rental prices per square meter in Berlin, Germany. The research investigates major quantitative and qualitative variables influencing rent dynamics by leveraging a robust dataset comprising over 2.6 million apartments with 59 variables from 2007 to 2020, sourced from FDZ Ruhr and ImmobilienScout24. Drawing from over 99,000 rental records (2015 and 2019 datasets) and 31 variables (9 quantitative and 22 qualitative), the analysis evaluates the influence of factors such as furnishing quality, modernization year, apartment size, and energy efficiency on rent pricing. Polynomial and log transformations were applied to improve model robustness. The use of log-transformed rent as the response variable, combined with nonlinear covariates, yielded the best model performance, with the highest adjusted Rsquared values of 0.3645 and 0.492 in the 2015 and 2019 models, respectively, among the tested models. The results suggest that both quantitative and qualitative variables significantly influence rent sqm, with influential predictors varying in linearity and significance across the two years. In 2015, rent was influenced by nonlinear trends in living space and construction years, while in 2019, heat cost and modernization showed linear increases in rent. Apartments with upscale furnishings, high energy efficiency, elevators, and guest toilets consistently commanded higher rents, whereas a pet allowance was associated with lower rents. Residual analysis, variance inflation factors (VIF), and the Akaike Information Criterion (AIC) confirmed the model’s statistical validity. Results indicate significant shifts in rent patterns and offer predictive insights relevant to policymakers, urban planners, and educational leaders addressing housing affordability and equity. This research builds prior literature in AI-supported urban analytics and contributes a replicable data-driven framework for strategic housing decisions in high-demand university cities.