Analyzing the Causal Relationships Between Financial Crises and the Residential Mortgage Market

Subject. We consider the economic relations, arising in the process of the analysis of the financial and economic state of the housing (mortgage) loan market during crises. Objectives. The aim is to conduct a study within the practical concept of financial crises and the residential mortgage market, to identify cause-effect relationships. Methods. We apply analytical and expert methods, based on the analysis of residential mortgage market data and the activities of the Agency for Housing Mortgage Lending. Results. The study determines the segmented role of digitalization of the banking sector in the system of State programs intended to support the residential mortgage market. We reveal causal relationships between financial crises and the residential mortgage market on the basis of a factor analysis. Conclusions. During 2008 and 2014, the government regulation of the banking crisis was only through changing the level of the key rate. It resulted in an increase in interest rates and a decrease in demand for mortgage loans, as affordable mortgage interest rates still remain the main driver of mortgage lending for citizens. This scenario gives rise to a stagnation of the residential mortgage lending market and, consequently, a very long recovery period. In contrast to the scenario of 2020, where we see an active growth in mortgage lending against the background of the financial crisis, the reason was the implemented set of measures that triggered the growth and formed a safety cushion for the banking sector in the form of secured lending.

27. Protection of the Borrower

All books in this flagship series contain carefully selected substantial extracts from key cases, legislation, and academic debate, providing able students with a stand-alone resource. This chapter reviews the loan contract and the controls that the law has imposed to protect the borrower. The level of protection differs according to the nature of the borrower and the type of security transaction. Market regulation of the residential mortgage market has increased protection for domestic borrowers. Vitiating factors, particularly undue influence, have impacted upon the creation of collateral mortgages of the family home to secure commercial borrowing. Equitable protection has been provided by controls against penalties and oppressive and unconscionable terms, as well as by protection of the borrower’s equity of redemption. Statutory consumer protection now offers more effective protection to domestic borrowers. The common law, equitable, and statutory control mechanisms are then described and applied to demonstrate the protection they afford against particular mortgage terms, for instance to control rates of interest and other costs associated with borrowing.

Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey

Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender 2016; Demiroglu and James Management Science, 64(4), 1747–1760 2018). The extreme events on the tails are usually known as “black swans” (Taleb 2010) in finance and their existence complicates financial risk management, assessment, and regulation. We show via theory, Monte Carlo experiments, and an empirical example that a direct relation exists between non-normality of the pricing errors and goodness-of-fit of the house pricing models. Specifically, we provide an empirical example using US housing prices where we demonstrate an almost perfect linear relation between the estimated degrees-of-freedom for a Student’s t distribution and the goodness-of-fit of sophisticated evaluation models with spatial and spatialtemporal dependence.

Differential Replication for Credit Scoring in Regulated Environments

Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solutions.