Alois Weigand

As symbolized by vacant office buildings, empty shopping malls and abandoned flats in metropolitan areas, the new coronavirus disease 2019 has severely impacted real estate markets. This paper provides a comprehensive literature review of the latest academic insights into how this pandemic has affected the housing, commercial real estate and the mortgage market. Moreover, these findings are linked to comprehensive statistics of each real estate sector’s performance during the crisis. Finally, the paper includes an outlook and discusses possible future developments in each real estate segment.

The value of land is determined by the locations’ attractiveness and the degree of regulation. When land regulations are binding, e.g. when a restriction on the maximum floor area ratio exists, the best use land price can be directly expressed as a function of the maximum floor area ratio and local amenities. We show theoretically and empirically how this approach can be used to determine land values from residential rents. From our empirical results, we derive two main sources for a monocentric structure of land prices. First, the location attractiveness of centrally located dwellings makes land prices more expensive. Second, as the maximum floor area ratio is high in central areas, the regulation works as a multiplier for land prices and inflates prices accordingly. Our model gives insights into the determinants of urban land prices and provides a useful approach for land appraisal in urban regions where land transactions are scarce.

The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.

Based on 39,791 rental contracts from 2,438 residential properties in the Swiss real estate market, we study how a property’s CO2 emissions affect net rental values. We use a novel measure of operational carbon emissions which relies on various parameters related to the sustainability and energy efficiency of a building as well as climate conditions of its location. After controlling for a building’s state, its macro and micro location, local rental market conditions and various property or apartment characteristics in an extensive hedonic framework, our results suggest that apartments in low-carbon buildings have higher net rents. This effect is mainly driven by lower ancillary costs of sustainable apartments in contrast to tenants’ higher preferences for environmentally-friendly living. We underline this result by sample splits across urban and rural areas, warm and cold locations, as well as regions with a high and low share of people supporting a Federal Act for the Reduction of Greenhouse Gas Emissions in Switzerland. Based on 432 residential building transactions, we also show that low-carbon buildings have lower capitalization rates which translate into higher market values due to lower risk premiums.

This paper investigates whether and how strongly the share of homeowners in a community affects residential property taxation by local governments. Different from renters, homeowners bear the full property tax burden irrespective of local market conditions, and the tax is more salient to them. “Homeowner communities" may hence oppose high property taxes in order to protect their housing wealth. Using granular spatial data from a complete housing inventory in the 2011 German Census and historical war damages as a source of exogenous variation in local homeownership, we provide empirical evidence that otherwise identical jurisdictions charge significantly lower property taxes when the share of homeowners in their population is higher. This result is invariant to local market conditions, which suggests tax salience as the key mechanism behind this effect. We find positive spatial dependence in tax multipliers, indicative of property tax mimicking by local governments.