Foreclosure discounts for German housing markets

Abstract

Purpose This paper aims to investigate the foreclosure discount for the German residential market in the years from 2008 to 2011. Design/methodology/approach The determinants of the foreclosure discount are estimated in a hedonic price model. The analysis is based on a unique data set compiled from three different data sources with 135,000 foreclosed properties. Findings The findings reveal that residential units in foreclosures are sold at a discount of 19 per cent compared to residential units with similar characteristics that are not in foreclosure. Second, a regional pattern can be observed, with discounts being negatively correlated to unemployment risk and liquidity. Third, the model with interaction terms shows that foreclosure discounts are linked to specific property characteristics. Fourth, these object-related risks are typically smaller than regional risks or locational risks. Research limitations/implications Given the highly fragmented system of Gutachterausschüsse in Germany, who are responsible for collecting transaction data, we were not able to directly analyze transaction data, but only a proxy for this price information. Practical implications The results can be important for financial institutions that are trying to assess the risk of lending for a specific object in a specific location. So far, banks primarily try to assess the default risk of private lenders by analyzing the debtor’s financial position and the quality of the property. The analysis provides insights into which characteristics of a property might imply additional risk, and in which region these risks are biggest. Originality/value To the best of the authors’ knowledge, this is the first attempt to analyze the foreclosure discount for the German housing market.

How Multiple Creditors, Market Liquidity and Homeowner Participation Affect Foreclosure Discounts

“…In Europe, studies by Donner (2020, andDonner, Song, andWilhelmsson (2016) őnd foreclosure discounts exceeding 20%. Just, Heinrich, Maurin, et al (2019) also conőrm foreclosure discounts of this magnitude in a study employing data on 135,000 German foreclosures. Furthermore, Donner (2017) claims that foreclosure status dominates property characteristics.…”

“…Still, these studies were later criticized by Carroll, T. Clauretie, and Neill (1997) as not being robust when introducing controls for location. Several later studies (Campbell, Giglio, and Pathak, 2011;Just, Heinrich, Maurin, et al, 2019;Donner, 2020) őnd sizeable foreclosure discounts and spillover effects from foreclosure on other real estate transactions (Immergluck and Smith, 2006). Furthermore, a study by LaCour-Little and Yang (2021) conőrms this discount by identifying excess returns in certain market conditions.…”

Different Models of Forbearance and Mortgage Enforcement Proceedings

“…These elements influence various aspects of the functioning in practice, such 183 as the length and net revenue of the procedure, and are therefore interesting to study. 97 Relevant here is also the role of the parties that are directly involved in the procedure (the mortgagor, mortgagee, and the enforcement agencies). Henderson divides the existing basic court enforcement agency models: court-controlled, public sector specialist, private or quasi-private specialist and multiple-institution controlled enforcement.98 An interesting paper by Gramckow shows that the type of enforcement agency influences the outcome of the procedure and this is therefore an interesting object of our comparative study.…”

Socio-Economic Background of Households and the implication for Housing Choices in Urban Ghana

“…Moreover, crowding has been found almost two and a half times more often in households reporting financial difficulties than in those where housing costs are not perceived as a burden (Ikenna & Sebnem, 2019;Just et al, 2019). It is imperative to recognize that housing crowding is highly associated with other measures of socio-economic deprivation such as low income, unemployment, low education level and fewer material resources (Al-Masum & Lee, 2019;Litman, 2019).…”