PurposeWe investigate causes for the cost overrun and delay of the railway project Stuttgart 21. Besides, we try to forecast the actual costs and completion date at an early stage.Design/methodology/approachThe results of exploratory research show the causes for the cost overrun and delay of Stuttgart 21; we compare our findings with other railway projects. To estimate the costs at an early stage, the reference class forecasting (RCF) model is applied; to estimate the time, we apply an OLS regression.FindingsWe find that the following causes are relevant for the cost overrun and delay of Stuttgart 21: scope changes, geological conditions, high risk-taking propensity, extended implementation, price overshoot, conflict of interests and lack of citizens' participation. The current estimated costs are within our 95% confidence interval based on RCF; our time forecast underestimates or substantially overestimates the duration actually required.Research limitations/implicationsA limitation of our approach is the low number of comparable projects which are available.Practical implicationsThe use of hyperbolic function or stepwise exponential discount function can help to give a clearer picture of the costs and benefits. The straightforward use of the RFC for costs and OLS for time should motivate more decision-makers to estimate the actual costs and time which are necessary in the light of the rising demand for democratic participation amongst citizens.Social implicationsMore realistic estimates can help to reduce the significant distortion at the beginning of infrastructure projects.Originality/valueWe are among the first who use the RCF to estimate the costs in Germany. Furthermore, the hyperbolic discounting function is added as a further theoretical explanation for cost underestimation.

We analyze whether lower rents for energy-inefficient apartments reflect tenants' willingness to pay due to a higher green awareness, purchasing power, or energy consumption costs. Based on a German rental apartment dataset from Q1 2007 to Q1 2019, we use interaction terms for socioeconomic characteristics in a hedonic regression model. We find that rents are lower for apartments with higher energy consumption, even in neighborhoods with lower levels of green awareness. This relationship is stronger in neighborhoods with higher purchasing power, such that communities with low levels of green awareness and high purchasing power show the steepest negative slope for increasing energy consumption (−8.6% from the highest to lowest rating). Thus, the rent-decreasing effect of purchasing power is higher than that of green awareness. Splitting the entire period into smaller windows, we find that the interaction effect of green awareness has emerged in the most recent years (2017-2019). This may be driven by changes in regulation, which have made it easier for tenants to assess the energy consumption before they rent, or by a general increase in green awareness over this period.

We analyze how direct and indirect effects (spatial spillovers) matter when estimating price effects for a property located in a flood zone. Using a spatial Durbin error model, we show the importance of indirect effects which amount to-6.5% for houses and-4.8% for condominiums in the flood-prone city of Dresden (Germany). Direct effects diminish when controlling for spatial spillovers. Our results are generally robust across different model specifications, urban areas, and riskadjusted prices that include insurance costs. Thus, ignoring indirect flood effects can lead to flood management that is inefficient and cost-ineffective, as the economic consequences are underestimated.

JEL Classification R30 • C45 • C80 1 The economic relevance of the real estate sector und its recent dynamics In most countries, the real estate sector plays a dominant role-as measured by volume, share of the economy, and workforce; but its turnover ratio and therefore its ability to generate profit for traditional banks or transaction and consultancy companies is lower than it is for the stock or bond market. For example, in the US, the total value of the real estate market amounted to about $46.

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