Mortgage Loans Research Articles

Impact of the war in Ukraine on the real estate market in Romania: Analysis in geopolitical context

The war in Ukraine has caused profound transformations in the dynamics of the Romanian real estate market, reflecting the impact of a complex combination of economic and financial, demographic and socio-cultural, as well as political and legal factors. In this context, the objective of the study is to analyze the impact of the war in Ukraine on the real estate market in Romania by assessing the influence of determinants to identify measures to support the stability and development of the real estate market within geopolitical crises. Subsequently, based on statistical analysis methods, the econometric model was developed considering as dependent variables the real estate market (measured by average price for housing) and independent variables – the number of Ukrainian migrants, interest rate on mortgage loans, GDP per capita, and inflation rate. The research results reveal that Ukrainian migration has a significant positive impact on the real estate market, leading to price increases for housing in urban areas and increased demand for rental housing. In addition, mortgage interest rates show a significant negative impact, suggesting that lower affordability of financing limits purchases of real estate. Moreover, GDP growth per capita is positively correlated with price dynamics in the real estate market, confirming that a prosperous economy stimulates investments in real estate. Findings provide valuable insights into understanding how geopolitical and economic factors interact to influence the real estate market.

The Impact of Consumption Downgrading on Housing Purchase Decision of Chinese Middle Class: An Analysis Based on the Life Cycle Theory and Marriage Market Theory

In the face of the trend of consumption downgrading, the purchasing decisions of the middle class regarding real estate have undergone new changes. Existing literature on the housing market and purchasing behavior of the middle class under the trend of consumption downgrading suggests that the purchasing power of the middle class has generally decreased, with many unable to bear the costs of mortgage loans and other housing-related financial burdens. As a result, the number of people purchasing real estate in the future may decrease. Based on existing research and practical needs, this paper summarizes the trends of previous studies through a literature review, pointing out their shortcomings. The paper further explores the decision-making process of the middle class in the context of consumption downgrading and analyzes the reasons for this situation using the Life Cycle Theory and Marriage Market Theory. The goal of this research is to provide policy recommendations for policymakers and reasonable suggestions for middle-class housing consumption decisions. The analysis shows that the middle class tends to rent housing or purchase small-sized properties, and that owning property is an important factor in the marriage market, especially from adulthood to the marriage stage.

Mass subsidized mortgage: No extension, no termination?

After the launch of mass preferential mortgage program in 2020, subsidizing interest rates on housing loans has become a key factor affecting the primary housing market in Russia. In 2024, the portfolio of preferential mortgage lending approached RUB 9 trillion, indicating the high importance of this factor for the financial sector and the entire economy. Nevertheless, the effectiveness of subsidized mortgage programs and their effect on the socio-economic situation remain a subject of debate among economists, politicians and the public. The paper analyzes the impact of these programs on the residential real estate market, house prices and housing affordability. The core part of the paper contains the estimation of the scale of the substitution effect — the share of subsidized mortgage loans that would have been granted under market conditions. Based on the results obtained, alternative methods of housing support are proposed.

CAUSES OF THE GLOBAL FINANCIAL CRISIS: POTENTIAL IMPACTS ON THE WORLD AND TURKISH ECONOMY

The global-scale crisis that quickly affected the entire world, originating in the United States, resulted in a dramatic increase in housing prices due to the widespread issuance of home (mortgage) loans to the public without much consideration for repayment capacity, fueled by the surge in demand. Subsequently, problems arose uncontrollably, especially for individuals with low incomes using variable interest rate mortgage loans, as the Federal Reserve (FED) also increased interest rates. The difficulties stemmed from the inability of these individuals to repay the loans, particularly those with variable interest rates, leading to what can be called "subprime mortgages." The securitization of these troubled loans and their grounding in derivative financial instruments, coupled with numerous banks transferring these contracts to each other, resulted in troubled stocks and derivative contracts experiencing significant global crisis due to excessive value losses. As the banking sector forms the cornerstone of financial markets and economies worldwide, the crisis that began in the banking sector rapidly and negatively impacted not only global banks but also developing and even developed country economies through a domino effect. This study aims to analyze the effects of the global crisis on the world countries and Turkey, along with the causes and consequences, based on the U.S. mortgage market.

Theoretical and legal aspects of introducing new mechanisms for providing housing to military personnel and their families: special savings program, leasing, mortgage

One of the key elements of national security and defense of the state in the state is the military personnel. Social and legal protection, material support, and favorable conditions for the realization of their constitutional rights are priority tasks for the state. Providing housing for military personnel is one of the key aspects of social protection. The state of housing provision for military personnel not only reflects society’s attitude toward its defenders but also holds strategic importance for ensuring combat readiness and the moral-psychological climate within the Armed Forces of Ukraine, the National Guard of Ukraine, and other defense forces. At the legislative and organizational levels, the system of housing provision for military personnel in Ukraine remains ineffective and contains significant corruption risks. The sources of current legislation regulating the right of military personnel in Ukraine remains ineffective and contains significant corruption risks. The sources of corruption risks. The sources of current legislation regulating the right of military personnel to housing require radical changes. Soviet legislation forms the current housing laws, is outdated, and does not correspond to the present political or socio-economic conditions requiring radical changes. A comprehensive analysis of various periods in the development of domestic housing legislation provides an opportunity to identify the circumstances and reasons that will help eliminate the shortcomings in the legal regulation of housing relations and find ways to improve modern housing relations and find ways to improve modern housing legislation by introducing new mechanisms for housing legislation by introducing new mechanisms for housing provisions for housing provision for military personnel and their families. In this regard, it is important to conduct research on the circumstances and factors that influence the legal aspects of implementing new algorithms for housing provision for military personnel. Such an analysis will reveal the problems and shortcomings in the implementation of new housing provision mechanisms for a significant category of individuals and identify ways for their further development. The issue of implementing the «eOselya» affordable mortgage program for military personnel is considered. An analysis of mortgage housing loans has been conducted, focusing on the provision of long-term bank loans for the purchase or construction of residential real estate, where the collateral is the property itself. The requirements for candidates to obtain a preferential mortgage loan have been studied. Problematik issues arising in the process of mortgage implementation have been explored.

MORTGAGE LOAN MARKET IN LATVIA

Purpose and aim of the study: to examine the mortgage loan market in Latvia. Design / Methodology / Approach: The research employed the following methods: descriptive and logical construction were used for reviewing and analysing research papers and other information sources, as well as for scientific discussion. Statistical analysis was employed to process and analyse secondary data on trends in mortgage loans in Latvia. The graphic method was applied to better represent and compare the research results. Main Findings: It was concluded that the property, housing and mortgage markets were interlinked, with developments in one market affecting developments in the other. Tax policies, government support measures and macro-prudential supervision measures form part of the regulatory framework for the mortgage market, which, together with the creditworthiness and financial literacy of borrowers, can promote or restrict the availability and development of mortgage loans.Originality: The research examined the concept of mortgage loans and factors in the mortgage credit market, as well as mortgage loan market trends in Latvia.Implications: The research provides a basis for further research on the development of the mortgage market in Latvia.

The Deep Promotion Time Cure Model.

We propose a novel method for predicting time-to-event data in the presence of cure fractions based on flexible survival models integrated into a deep neural network (DNN) framework. Our approach allows for nonlinear relationships and high-dimensional interactions between covariates and survival and is suitable for large-scale applications. To ensure the identifiability of the overall predictor formed of an additive decomposition of interpretable linear and nonlinear effects and potential higher-dimensional interactions captured through a DNN, we employ an orthogonalization layer. We demonstrate the usefulness and computational efficiency of our method via simulations and apply it to a large portfolio of U.S. mortgage loans. Here, we find not only a better predictive performance of our framework but also a more realistic picture of covariate effects.

Impact of Digital Technology on Traditional Banking: A Case From the Credit Market in the European Union

ABSTRACTThe aim of this study is to investigate whether digital technologies affect the credit market in the European Union (EU). The impact of digital technology on three types of bank loans (residential mortgage loans, consumer loans and corporate loans) is analysed separately for two groups of EU countries: Central and Eastern Europe and Western Europe. A dynamic panel regression model is employed based on yearly data on the individual bank level. The sample panel data cover the period 2010–2019. The results show that digital technology affects the growth of loans for households, mortgages and consumer loans. We also find heterogeneity between the Central and Eastern European and Western European banking sectors. Furthermore, the impact of digital technology on bank loan development is significantly stronger in foreign banks. Finally, the findings confirm the dominant role of digital technologies in loans for households during the analysed period. This is the first comprehensive study on the determinants of different loan types in the context of digital technology use in the EU.

The coupling mechanism of farmland transfer and farmland mortgage: Taking the Jinshan District of Shanghai as an example

Taking the mortgage loan mechanism of farmland management rights in Jinshan District of Shanghai as an example, we analyzed the coupling mechanism between farmland transfer and farmland mortgage according to a case analysis and system evolution theory. We explored the comparative advantages and inherent limitations. The results showed that the linkage between farmland transfer and farmland mortgage has a Pareto nature, which can achieve a two-way mutual promotion and spiral development of the coupling system. The comparative advantages of the coupling mechanism of farmland transfer and farmland mortgage include source screening, process standardization, information symmetry, and comprehensive subsidies. The inherent limitations include the instability of the management right of flow transformation; the difficulty of land value evaluation; and the difficulty of mortgage disposal, unsustainability, and social risks. The benign coupling between farmland transfer and farmland mortgage likely triggered the market driving force and formed an operating mechanism that is “market driven and government promoted”; however, the capital supply of rural finance has not been consistent with the scale of agricultural production and management. With the deepening of this coupling, the systemic risk of the rural land financial market has spread from a farmland financial market to a farmland transfer market, and the degree of risk has become greater and the degree of harm has become deeper. Therefore, the government should strengthen the top-level design and improve the following key links: (1) encouraging and guiding the signing of medium- and long-term farmland transfer contracts; (2) establishing professional farmland value evaluation institutions; (3) building an effective exit mechanism for financial institutions; and (4) establishing a standardized farmland management right redemption mechanism.

UNDER THE LOAN: AN ANALYSIS OF ARMENIAN BANKING SECTOR AND CREDIT TRENDS

This study examines recent trends in Armenia's banking sector and export patterns, revealing underlying economic vulnerabilities despite apparent growth. Using data from the Central Bank of Armenia and official trade statistics, this paper analyzes shifts in lending patterns across various economic sectors and changes in export composition. Our findings indicate a concerning decline in industrial sector lending, coupled with a significant increase in mortgage and consumer loans. Additionally, we observe a heavy reliance on precious metal re-exports for economic growth, raising questions about the sustainability of Armenia's current economic trajectory. This research contributes to the understanding of potential risks in Armenia's financial system and highlights the need for diversification in its export base.

Locked In: Rate Hikes, Housing Markets, and Mobility

Rising interest rates in 2022 introduced large moving costs for homeowners with low, fixed-rate mortgages. Using a novel dataset linking mortgage loans, consumer credit profiles, and property sales, we examine the effects of rate hikes on household mobility and the broader economic impacts of the resulting mortgage rate lock-in. As market rates rise relative to those on borrowers’ existing loans, likelihood of moving falls with the highest elasticity among borrowers just “in the money.” Our results suggest about 44% of the decline in moves among mortgage holders between 2021 and 2022 may be attributable to the widening gap between borrower’s existing and market rates. We find limited scope for labor misallocation due to lock-in, as moves across labor market areas are rather unaffected. Instead, lock-in primarily reduces within-metro churn and moves up the housing ladder, leading to fewer real estate listings and greater house price growth. We explain lock-in-driven price increases through a housing search model: in a seller’s market, reduced churn raises market tightness, driving up prices. Consistentwith such a model, we show measures of market tightness increase in response to lock-in, with the most significant effects in markets that were already tight.

Consumer Choice in Mortgage Loans Cross-Selling

Consumer Choice in Mortgage Loans Cross-Selling

Mortgage Loan Data Exploration with Non-parametric Statistical and Machine Learning Perspectives

Mortgage Loan Data Exploration with Non-parametric Statistical and Machine Learning Perspectives

Social proximity to capital and mortgage lending

This study investigates the impact of social proximity to capital on the U.S. mortgage markets. The results show that mortgage loans in counties with stronger social connectedness to capital are associated with significantly larger loan amounts and lower interest rates. The endogeneity concern is mitigated by employing a propensity score matched sample. Channel analysis reveals that the potential mechanisms operate through borrowers’ higher income and improved credit conditions. The cross-sectional analyses indicate that first-time homebuyers in counties with closer social proximity to capital secure larger loans and lower rates, whereas minorities in these areas face adverse outcomes.

Machine learning Bureau score for Home Lending in an American finance company

Our client is a leading provider of mortgage financing, originating loans and lines of credit to consumers in the US. Currently, they receive applications where applicants provide personal information and a soft pull of their FICO score is requested. That score is used to evaluate the applicant’s credit worthiness and determine conditional approval and the type of product available for the customer, including conventional, FHA or other mortgage loans. After conditional approval, a formal application is initiated, and underwriters review the information to determine the final application decision. When evaluating applications below regulatory and business thresholds, the company has the intention to approve more applications and increase loan volume, and there is an expectation that through the enhanced credit assessment, our client will improve the percentage of Low to Moderate Income (LMI) population able to obtain mortgage loans. Both aspects have a direct impact on the reputation and economic profits of the firm, so they are of pressing importance to the company. This project aims to build an applicant-level bureau-only score based on upgraded bureau internal attributes. This score will eventually serve as the basis for evaluating a customer’s credit risk before any loan structure or collateral information is considered. It will be used as a standalone score that can be used in the initial customer evaluation to identify better leads (mortgage inquiries for preapproval) and as input to a future application-level model.

Evaluating the impact of demographic characteristics on residential mortgage default risk: Evidence from Lebanon

Purpose: This study aims to examine the relationship between borrower's demographic characteristics and default risk in mortgage loans to help financial institutions develop more effective lending policies. Design/Methodology/Approach: Cross-sectional data were elicited from randomly selected 6743 individual accounts from Lebanese housing banks. This study applied the binary logistic and stepwise regression models to analyze the dataset using the Stata statistical software. Model diagnosis is performed using the Hosmer-Lemeshow goodness of fit test, likelihood ratio test, model accuracy classification table and statistically significant test-ROC curve. Findings: The findings revealed that there is a significant relationship between residential loan default risk and borrower’s marital status, nature of job occupation, job economic sector, job location and loan purpose. The performance of the binary logistic regression analysis demonstrates the overall percentage who is correctly classified is 91.61%. Conclusion: The log odds of default risk for widowed borrowers are about 90 percent higher than those of divorced borrowers and that of self-employed borrowers is about 54 percent higher than that of employed borrowers. Borrowers working in the banking and real estate sectors have lower default rates than borrowers working in other economic sectors. In addition, loans granted for renovation purposes have the lowest default rates compared to loans provided for purchase, under-construction and construction purposes. Practical Implications: The empirical results help financial institutions to have early warning signals in detecting financial distress and to differentiate between a high- and low-risk group of borrowers, helping in the development of tailored risk mitigation strategies and adjusting the lending criteria.

Risk Analysis of Mortgage Loan Default for Bank Customers and AI Machine Learning

Abstract Risk Analysis of Mortgage Loan Default for Banks is a crucial issue. On one hand, it concerns the quality of the bank's credit decisions, and on the other hand, it affects the rights of homebuyers to obtain financial support. In 2008, the U.S. subprime mortgage crisis sparked a global financial meltdown. What began with the collapse of the housing market quickly spread throughout the global financial system, resulting in the failure of numerous banks and a widespread economic recession. Recently, the banking sector has increasingly leveraged Artificial Intelligence (AI) and Machine Learning (ML) to enhance decision-making processes, particularly in assessing the risk of mortgage loan defaults. This paper aims to explore the application of ML techniques to predict and analyze the risk of default among bank customers, thereby enabling financial institutions to make more accurate and informed lending decisions. Keywords: Mortgage Loan Default Risk Analysis AI ML K-MANS LTV.

Loan-to-value ratio and uncertainty

ABSTRACT We examine how uncertainty affects the loan-to-value (LTV) ratio, with a focus on identifying which types of uncertainty – such as those related to macroeconomy, financial market, housing market, and economic policy – are most impactful, using a VAR model. Our finding reveals that the LTV ratio declines in response to increased uncertainty, suggesting that uncertainty plays a significant role in shaping the LTV ratio. Notably, uncertainties in the financial and housing markets substantially decrease the LTV ratio for newly originated mortgage loans, challenging the exogenous collateral constraint framework. Additionally, it shows delayed and hump-shaped responses. These results differ from theoretical predictions.

MENGANALISIS FAKTOR YANG MEMPENGARUHI PERMINTAAN KREDIT PEMILIKAN RUMAH

The increasing need for housing encourages people take other alternatives, such as using Home Ownership Loans (KPR) as an alternative in obtaining housing. There are some factors that influence the community as consumers in taking / using mortgages, some factors include customer income, house prices, interest rates, down payments, loan terms, and the simultaneous influence of all factors. This study aims to provide an overview of the factors that influence the demand for mortgage loans in Watubangga urban village housing in Kendari city. The method used in this research uses descriptive quantitative, where this approach explains the phenomenon of the situation, measures the relationship and draws conclusions based on the use of numbers. The findings of this study are partial factors that affect mortgage credit such as customer income, price of a house and time period are positively significant, which means that the increase in these partial factors increases the community's intention to take out a mortgage. However, it is inversely proportional to partial factors such as interest rates and down payments. Even so, the effect of these factors is simultaneously positively significant.

Review of The Financial Health of Bank During Increasing Foreclosed Assets and The Application of Analytic Hierarchy Process (AHP) in Selecting Strategies to Reduce Them

PT Bank Laju International Tbk (the Bank) has struggled with its increased foreclosed assets, particularly those originating from mortgage loans, which could affect its financial health. The research assessed Bank’s financial health using Risk Based Bank Rating (RBBR) framework by analysing key financial ratios and investigating the root causes of rising foreclosed assets through Fishbone and Pareto analysis. Key factors include customer income instability, slow progress in selling foreclosed assets, and lack of insurance protection during economic downturns. To select effective strategies for managing foreclosed assets, the research utilized Analytic Hierarchy Process (AHP), a Multi-Criteria Decision-Making method, to evaluate multiple alternatives based on various criteria, which were derived from Focus Group Discussion (FGD) with Bank’s officers, who directly involved in mortgage loans and foreclosed assets activities, also reference with Bank’s SWOT analysis. The research evaluated three alternatives strategy: Protection Insurance for Customer’s Mortgage Loans. Actively Partnering with property agents to accelerate sale of foreclosed assets as-is. Collaborating with property agents to renovate foreclosed assets. The result showed that despite the increase in foreclosed assets, the Bank remains financially sound, with key financial ratios within acceptable regulatory limits. AHP analysis revealed that alternative 1 stands in first place to be prioritized, followed by alternative 2, however between both alternatives indicates a very small difference in prioritization weight, which means that both strategies are important to be executed and prioritized. Proposal to implement both alternatives in parallel would be a very effective strategy, where alternative 1 conduct as Preventive Strategy in preventing customer’s mortgage loans defaults, while alternative 2 as Corrective Strategy in lowering foreclosed assets volume. Alternative 3 acts as a complement strategy, where renovating foreclosed assets could facilitate quicker sales, however it’ll take longer time for renovation itself.