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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 Gutachterausschusse 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.
The sample preparation for shadowing microscopy, to examinate biological soil properties, is time consuming, manual work. The outcome depends on subjective skills of the operator, furthermore the results are mostly not quantitative. The database on biological soil properties is mostly not sufficient for an integrated modelling on an multidisciplinary scale. This project combines three progressive approaches to develop a tool that is easy to use and gives in situ results that can be used for many purposes.
The primary consumers of plant exudates – in exact fungi and bacteria, are representative for the soil succession level from bare soil, which is bacterial dominant to old growth forest constitute by fungal dominance. In a specified level of soil succession, a special kind of plant family benefits on the Fungal to Bacteria Ratio. The ability to determine this ratio in situ without complex chemical applications is part of the project Electronical Laboratory for Intelligent Soil Examination (ELISE). Several mechanical and optical tests on soil samples are covered within this Project. To analyze the fungal to bacteria ratio, samples are prepared automatically – in a defined and reproductive procedure – to generate slides for shadowing microscopy. The samples are observed by a camera, which is attached to a transmitted light microscope. The automatic analysis, done with computer vision algorithms, aims to quantify bacterial and fungal biomass in the actual sample view. Moreover, the algorithm can classify organisms according to their color and shape.
To get a processable picture, several images from different focal levels must be taken through the sample thickness. Parts of each image, are in focus at the actual layer, are merged to a whole depth of field picture, by focus stacking.
This produced picture is used to classify, locate and quantify – in first step filamentous organisms e.g. fungal by image sematic segmentation. The result represents an image sized mask, which indicates the class of fungi with class equivalate values at the pixel positions – covered by the organism. This information is used to calculate the fungal mass per gram soil.
To quantify the bacterial biomass two approaches are implemented. For low density of bacterial existence, the individual bacteria is counted for a part of the field of view by an image detection algorithm to be extrapolate afterwards to the mass per gram soil. For high density of bacterial occurrence, specified regions of interest with only bacteria present are chosen. An image classification which has been pretrained by pictures of bacterial density patterns – previously determent by making the sample countable due to preforming sample dilutions, is done. The second option for high density bacterial count is, to automatically preform dilutions until the image detection is confidently countable.
To ensure a usable confidence score a statistical approach of many fields of view is taken.
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