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This paper aims to investigate the foreclosure discount for the German residential market in the years from 2008 to 2011.

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.

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.

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.

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.

To the best of the authors’ knowledge, this is the first attempt to analyze the foreclosure discount for the German housing market.

Prior research indicates a discount for foreclosures sold through the multiple listing service (MLS). The purpose of this paper is to examine whether the effect of foreclosure on house value is consistent over submarkets based on property size in the US single family home market. The paper also tests whether the spillover effect of a nearby foreclosure on the specific property value varies across submarkets.

The full sample is split into four quartiles based on the square feet of all observations. The hedonic pricing models are estimated across full sample and three subsamples, in order to examine the effect of foreclosure on selling price. The number of neighborhood foreclosures within each combination of radii and timing intervals is used to investigate the spillover effect of a nearby foreclosure on the specific property value.

It is found that the quartile with smaller houses have the largest discount associated with a foreclosure of approximately 24 percent, while the medium and larger houses have a discount of approximately 19 percent. The results are robust after including a proxy for property quality. Second, the spillover effects of nearby foreclosures are lowest for small properties and highest for large properties. Adding additional controls for housing quality reduces the observed spillover effect.

The findings on foreclosure discount are consistent with Pennington‐Cross's argument, that the foreclosures in the smaller properties have lower appreciation than the larger ones. The paper's results about spillover effects also support the previous research, implying a greater stigma for foreclosed houses in neighborhoods with larger, more expensive houses.

The paper provides potential explanation for foreclosure discount and spillover effects of nearby foreclosure in the US single family residential markets.

Agent-based modelling and simulation (ABMS) has seen wide-spread success through its applications in the sciences and social sciences over the last 15 years. As ABMS is used to model more and more complex systems, there is going to be an increase in the number of input variables used within the simulation. Any uncertainty associated with these input variables can be investigated using sensitivity analysis, but when there is uncertainty surrounding several of these input variables, a single parameter sensitivity analysis is not adequate. Latin hypercube sampling (LHS) offers a way to sample variations in multiple parameters without having to consider all of the possible permutations. This paper introduces the application of LHS to ABMS via a case study that investigates the mortgage foreclosure contagion effect. This paper aims to discuss these issues.

Traditionally, uncertainty surrounding a single input variable is investigated using sensitivity analysis. That is, the variable is allowed to change to determine the impact of this variation on the simulation's output. When there is uncertainty about multiple input variables, then the number of simulation runs required to undertake this investigation greatly increases due to the permutations that need to be considered. LHS, which was first derived by McKay et al., offers a proven mechanism to reduce the number of simulation runs needed to complete a sensitivity analysis. This paper describes the LHS technique and its applications to an agent-based simulation (ABS) for investigating the foreclosure contagion effect.

The results from the foreclosure ABS runs have been characterized as “good”, “bad” or “ugly”, corresponding to whether or not a property market crash has occurred. As the only thing that can induce a property market crash within our model is the spread of foreclosing properties, these results indicate that the foreclosure contagion effect is dependent on how much impact a foreclosed property has on the price of the surrounding properties.

This paper describes the application of LHS to an agent-based foreclosure simulation. The foreclosure model and its results have been described in Gangel et al. Given a certain output “boundary” found within these results, it was highly appropriate to conduct an extensive sensitivity analysis on the simulation's input variables. The outcome of the LHS sensitivity analysis has given further insight into the foreclosure contagion effect thus demonstrating it was a beneficial exercise.

This paper aims to propose a model for democratization of Islamic home financing to tackle the issue of sustainability of homeownership affordability.

A conceptual framework and fractional equity model (FEM) are developed to incorporate big data analytics, artificial intelligence and blockchain technology in an ecosystem for affordability and sustainability of homeownership via the proposed financing model. In addition, the FEM adopts the simulation approach to show its validity in terms of liquidity when compared with traditional home financing. In this regard, this paper is focused on developing and demonstrating the feasibility of a new financing model, rather than testing specific hypotheses or relationships. This is to propose the democratization model for Islamic Home Financing that will not benefit the prospective home buyers without compromising the profitability of the financial institutions.

The findings indicate that the proposed end-to-end solution within the financing ecosystem can lead to more efficient matching market between the buyers and sellers of houses, reduced transaction costs, greater transparency and enhanced efficiency which in the end could lead to lower costs of owning homes and sustained financial resilience among house owners. The findings indicate that the FEM model is able to increase homeownership with more elements of liquidity, marketability and sustainability for homebuyers.

This research highlights the potential of big data and blockchain technology in democratizing Islamic home financing and evidence that the transfer of ownership is possible through tokenization. However, this will require a mature financing environment to adapt the technology for practical application.

The model proposes a solution to propagate shared prosperity among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies. The proposed FEM model provides alternative home financing that is more marketable, flexible and sustainable for households/buyers and financiers.

It is hoped that with the proposed financing ecosystem to promote affordability and sustainability of homeownership via big data analytics, artificial intelligence and blockchain technology can lead to greater financial resilience for homeowners which can then be translated to enhanced well-being, increased productivity and can further promote economic growth.

This research is a concept paper based on academic research and industry collaboration with a technology provider.

The problem in alleviating homeowner mortgage distress through refinance is how to achieve meaningful alleviation without prospectively harming the financier. The problem revolves around two parameters from real estate finance – the probability that the distress leads to foreclosure and resulting foreclosure loss severity for the financier if foreclosure does occur. Previous analysis focuses on reducing the probability that homeowner distress leads to foreclosure. By contrast, the purpose of this paper is to focus on reducing foreclosure loss severity.

The study develops a new intuitive formula for foreclosure loss severity to quantify its dependence on transaction costs. The study shows that foreclosure loss severity reduction is feasible by introducing a new refinancing instrument that lowers foreclosure transaction costs and applying property law to derive the structure of the refinancing instrument.

Foreclosure loss severity reduction can subsidize concessions on scheduled payments for homeowners with arbitrarily poor credit without prospective harm to the financier.

Quantification of mortgage distress relief is limited to distressed mortgages described by representative parameter values from various government studies.

For most distressed homeowners, payment and principal reductions could exceed those available from the recent government programs.

Implementation should significantly enlarge the pool of homeowners eligible for mortgage distress relief.

The mortgage refinance is qualitatively different from that available under existing government refinance programs because it is based on an arms-length exchange of property rights that makes market sense regardless of whether the refinancing results in subsequent homeowner default.

The purpose of this paper is to determine if lender experience in disposing of repossessed single‐family houses in the local market is significantly related to the probability a property will sell. In addition, other factors that are significantly related to the market duration of repossessed houses are identified.

The Cox proportional hazard model is used to analyze transaction data for 2,099 single‐family houses in Dayton, Ohio. Title to each of these properties was obtained by lenders through foreclosure. The study period approximates the first three years of the subprime mortgage crisis in the USA: 2007‐2009.

The marketing efforts of lenders with more local property disposition experience are found to be superior to the efforts of less experienced lenders. The results also indicate that the selling rate function increased over the study period, and there is seasonality in the data which is consistent with lenders attempting to limit holding costs.

The study is limited to the experience of lenders in a single local market over a three year study period. Additional research to determine if similar results apply in other markets would be a valuable addition to the literature.

While foreclosure is not a desirable outcome for any of the parties involved in a mortgage loan, the paper's results offer a bit of good news for lenders. The results are consistent with organizational learning theory which posits that experience should enhance performance. Given predictions that the mortgage crisis has not yet run its full course, lenders' performance in disposing of repossessed houses is likely to continue to improve.

This is the first study to apply the proportional hazard model to the study of foreclosed houses. This technique offers an advantage over previously applied methodologies because it allows the researcher to include properties that lenders did not sell during the study period into the analysis. All previous efforts were limited to sold properties and this restriction may have biased the previous results.

Mortgage defaults within a neighborhood may tip the scales whereby a vicious cycle of disinvestment and deterioration in the surrounding neighborhoods begins. This paper aims to examine the impact that mortgage default has on properties in the same ZIP code and neighboring ZIP codes.

Hypothesizing that neighborhoods' susceptibility to cascade failure can be measured by the rate of acceleration of mortgage failures within the neighborhood, the paper introduces a model to investigate whether or not this vicious cycle is such that mortgage failures multiply, and there is a tipping point at which the downward cycle accelerate.

The paper applies the model to data for the Los Angeles metropolitan area for the period 2006-2007 and finds evidence of a tipping point.

The paper is limited by the availability of data with respect to both time and space.

A failure tipping point will provide a signal that mortgage crisis is pending. Reacting to this signal could allow financial markets to avert such crises in the future.

Some neighborhoods may resist being labelled as one with significant mortgage failure activity. This resistance may cause a negative reaction to these results and implementation for the findings.

To-date, no evidence of a mortgage failure tipping point has been discovered in the literature.

The purpose of this study is to demonstrate that the conventional mortgage system is not appropriate for household finance because it encourages equity extraction and excessive leverage during housing boom and leads to negative equity during a housing bust, a situation that translates into mortgage defaults and foreclosures. Home financing could alternatively be structured as a diminishing partnership preventing the homeowner from ever having negative equity.

Using Johansen’s cointegration test, the authors provide evidence of a long-run relationship between the delinquency rates, volume of refinancing and the change in house price index (HPI) during the 1994–2019 period. To unravel the short run dynamics between these variables, the authors used a Granger causality test that concludes that the volume of refinancing and the change in the HPI Granger cause default rates.

The authors provide evidence that under the current conventional mortgage system, excessive refinancing opportunities and equity extraction that are the main factors determining delinquency rates leading to a non-sustainable homeownership.

If mortgages were such that they do not incentivize defaults and foreclosures during a housing downturn, the recovery of the housing market always leads to capital gains. Therefore, disincentivizing refinancing and equity extraction would lead to a more sustainable homeownership.

Households would be encouraged to pursue sustainable homeownership through a partnership-based model with long-term wealth accumulation for themselves and their heirs rather than short-term home ownership through the conventional mortgage system, leading to negative equity and defaults when the housing market slumps.

Policymakers ought to rethink the mortgage design by promoting partnership-based finance to protect the equity a household accumulates over a lifetime and thereby enhancing stable and sustainable homeownership.

The purpose of this paper is to examine the determinants of property sale probability and sale price at auction in Kuala Lumpur, Malaysia.

A two-stage Heckman sample selection model is used for this research. The first stage involves the estimation of a probit model on a successful sale. The second stage introduces an additional selection variable, the inverse Mills ratio, as an explanatory variable to the sale price estimation equation.

The authors find that Chinese-owned auctioned properties have higher sale probability and are sold at higher prices as compared to Indian and Malay-auctioned properties. Properties auctioned by the largest auction house outperformed other smaller auctioneers. Auction characteristics such as proximity to the city center, number of previous auction attempts and number of online viewers are positively related to sale price and sale probability.

The findings on the substantially lower sale price obtained by Malay and Indian borrowers compared to their Chinese counterparts imply that it is much harder for these borrowers to be relieved from financial distress. The two plausible explanations offered in this paper for this price differential, i.e. racial residential segregation and ownership restriction, warrant further study.

First, the authors consider the explanatory power of seller ethnicity, number of online viewers and auctioning route which are new to the literature. Second, they use a Heckman model that addresses possible selection bias of sold properties. This methodology is unexplored in the auction literature.