Tearing down the information barrier: the price impacts of energy efficiency ratings for buildings in the German rental market

Improving the energy efficiency levels of the housing stock is of particular concern in the private rentalmarket where capital costs and utility cost savings are not shared in equal measure by landlords and tenants. This problem is particularly pronounced in the German housing market with its predominance of rented accommodation over owner occupancy. The present study is the largest to date to investigate the effect of energy efficiency ratings on rental values. Using a semiparametric hedonic model and an empirical sample of nearly 760 thousand observations across 403 local markets in Germany with full hedonic characteristics, we find evidence that energy-efficient rental units are rented at a premium. However, this effect is not confirmed for the largest metropolitan housing markets. In a second step, a survival hazard model is estimated to study the impact of the energy ratings on time-on-market. It is found that energy inefficient dwelling have longer marketing periods and are hence less liquid than their more energy efficient counterparts.European Commission Horizon 2020 Grant H2020-EE-2014-201

Interpretable machine learning for real estate market analysis

While Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due to its complex non-parametric structure. This paper aims to elucidate the analytical behavior of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of model-agnostic interpretation methods to decompose the rental value drivers and plot their trajectories over time. Living area and building age are the strongest predictors of rent, followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the critical distances to these centers beyond which rents tend to decline more rapidly. Conversely, close proximity to hospitality facilities as well as public transport is associated with rental discounts. Overall, our results suggest that IML methods provide insights into algorithmic decision-making by illustrating the relative importance of hedonic variables and their relationship with rental prices in a dynamic perspective