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Determinants of a foreclosure discount

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Abstract

This study adds to previous research analyzing the impact of foreclosure status on real estate sales price by using a Swedish dataset were an appraiser has estimated the market value of apartments before they were sold at foreclosure auction. Appraisal data can address the issue of selection bias and a potential overestimation of foreclosure related discount. A mean discount of 7.9% with a corresponding median value of 9.5% is shown when comparing appraisal estimates with prices achieved at foreclosure auction. A hedonic model is also applied, and the resulting discount is estimated at 23.9%. Measures of local market conditions are related to the foreclosure discount, with hedonic price models and models using appraisal data producing consistent results. It is found that the discount is higher in lower priced neighborhoods, in neighborhoods that are heterogeneous in terms of price and in less liquid neighborhoods (significant in the hedonic model). It is also found that apartments with a higher value relative to the neighborhood price level sell at larger discounts. The results are consistent with studies on search and matching theory and contrast from earlier studies that attribute a foreclosure discount to seller motivational factors.

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Notes

  1. The dataset used in Renigier-Biłozor et al. (2018) is small, totaling 113 properties in both Italy and Poland, of which 40 are foreclosed.

  2. Eminenta Värdia. Official Website (Visited on April 2, 2020). http://www.eminenta.se/om-oss.

  3. As of April, 16 2020, one Swedish Crown corresponds to approximately 0.1 US Dollar.

  4. Defined as the standard deviation of the average square meter price divided by the average square meter price.

  5. In this case the foreclosure rate is defined as the number of foreclosures as a percentage of all sales.

  6. This is estimated as the standard deviation of the average square meter price divided by the average square meter price for the year of transaction.

  7. As a measure of location, Base Areas are used. There are 645 distinct such Base Areas in the dataset.

  8. This implies that all binary variables that shift from 0 to 1 have an impact on price given by g = 100[Exp (βi) − 1], with g being the percentage change.

  9. As previously mentioned, there are 645 Base Areas. As a robustness check, the hedonic models were also applied using parish as locational measure, these are larger and fewer, with the number of parishes at 70. Although producing a larger negative estimate of the price impact of a foreclosure, the results are otherwise consistent in the sense that all variables relating to local market conditions show the same signs and the estimated impact of a foreclosure decreases when adding interaction variables (from − .342 to − .233).

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  1. Global Projects Center, Stanford University, Stanford, USA

    Herman Donner

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Donner, H. Determinants of a foreclosure discount. J Hous and the Built Environ 35, 1079–1097 (2020). https://doi.org/10.1007/s10901-020-09757-1

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