Mortgage Interest Rates Research Articles

Performance evaluation of housing price index prediction model using DNN and SVR

This study used the base interest rate, mortgage interest rate, deposit interest rate, consumer price index, exchange rate, and composite stock price index as variables to predict the housing price index. The variables were normalized using the min-max normalization method. Machine learning methods, Deep Neural Networks (DNN) and Support Vector Regression (SVR), were employed to construct a prediction model, and the fit of the model was evaluated using the Root Mean Square Error (RMSE). The analysis results of the DNN model indicated that when applying the softplus activation function, the RMSE was 0.1298, resulting in the smallest prediction error, and the correlation coefficient between the predicted and actual values was highest at 0.9342. The analysis results of the SVR model using the Gaussian RBF kernel showed that the RMSE was smallest at 0.2781 when the parameters were set to C=100 and Gamma=0.1. Therefore, the DNN model was found to be more fit for predicting the housing price index. Additionally, it was confirmed that the consumer price index had a non-linear effect with a positive relationship, and the base interest rate had a linear effect with a negative relationship.

Factors Affecting Urban Housing Development in Lusaka, Zambia

This study investigates the factors affecting urban housing development in Lusaka, Zambia, a city facing significant challenges due to rapid urbanization and a high prevalence of inadequate housing. Utilizing the Delphi method, data was collected through two iterative rounds from a panel of 10 experts to identify and evaluate 15 factors contributing to poor housing outcomes. The findings reveal that political influence, poor linkage between infrastructure investment and service delivery, lack of access to affordable housing finance, weak urban governance, and high mortgage interest rates are the key factors impeding urban housing development in Lusaka. The study recommends developing an integrated housing planning framework, tailored housing finance products for low-income earners, and promoting governance principles to enhance transparency and accountability in housing development. This research provides valuable insights for policymakers and stakeholders in Sub-Saharan Africa, highlighting socio-economic areas requiring intervention to achieve sustainable urban housing development.

A bank-level analysis of interest rate pass-through in South Africa

We study the pass-through of policy rate hikes and cuts to household and corporate lending and deposit interest rates in South Africa over the period January 2009 to December 2020. We show that rate hikes are typically passed through to mortgage interest rates completely while rate cuts are not. This asymmetry is more prevalent for household than corporate mortgages. Pass-through to household and corporate call deposit interest rates is typically complete, but cheque account interest rates are highly sticky and experience weak pass-through. Our results indicate that banks’ pass-through decisions often impose greater costs on households than firms, and may blunt the stimulatory effect of rate cuts by weakening their impact on debt servicing costs and the remuneration of deposit balances.

Re-investigating the linkage between buildings’ energy efficiency and mortgage rates

ABSTRACT The energy use in residential buildings is one of the deciding factors in the transition to a carbon-neutral economy. Using data on Dutch residential mortgages from 2013 to 2022, we re-investigate the linkage between mortgage interest rates and the energy efficiency of buildings. The results are novel in that they are the first to empirically provide evidence in favour of a small sustainability discount of about 3 bps on mortgage interest rates for residential homes. Sensitivity checks suggest that younger-aged borrowers and those in the middle-income range benefit the most from a sustainability discount. From a policy perspective, our results are relevant as they show how banks ‘walk the talk’ and incorporate energy efficiency in mortgage terms.

Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support

ABSTRACT Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.

DECISION-MAKING OF DEVELOPERS IN THE IMPLEMENTATION OF RESIDENTIAL DEVELOPMENT PROJECTS IN THE CZECH REPUBLIC ENVIRONMENT

The research was conducted with the aim of analyzing the aspects that enter into the decision-making process of developers in the field of residential construction in the Czech Republic. To ensure the representativeness of a diverse range of companies active in the real estate market, a suitable group of respondents was carefully selected. The survey provided a sufficiently representative sample, based on which the key factors influencing the decision-making in the implementation of development projects were identified. The research found that developers primarily focus on seeking profitable opportunities, and their decision-making process is predominantly aimed at achieving financial gain. In this decision-making, they also consider other factors, such as mortgage interest rates, the quality of newly built homes with an emphasis on energy efficiency, and the use of modern technologies.

Why Did House Prices Go Up During COVID-19 Pandemic? Policy-Driven or Market-Driven?

During the spread of the COVID-19 virus, governments in many countries took temporary measures to mitigate and/or remove the cost of its negative effects on both society and the economy. In Türkiye, as in many countries, the deposit banks cut mortgage interest rates. Thus, Türkiye has experienced large increases in house prices, as have many countries. Türkiye has also become the country with the highest average annual rate of house price increase among the Organisation for Economic Co-operation and Development (OECD) countries during the pandemic period. However, despite an increase in the number of studies on house prices or mortgage loans during the pandemic period, there is a research gap in terms of a comparison of the relationship between house prices and mortgage loans in the pre-pandemic and the pandemic periods compared with that on other assets or in other sectors,. This study aims to rectify this gap. To achieve this, both the Fourier Granger and the Fourier Toda-Yamamoto causality tests are used. The results indicate that there are differences in both periods of time with respect to the relationship between mortgage loans and house prices in Türkiye, and that there is a causal relationship between both factors in the prepandemic period, but not during the pandemic period.

What drives the real estate market? Could behavioral indicators be useful in house pricing models?

PurposeThe objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.Design/methodology/approachIn our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.FindingsOur findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.Practical implicationsThe results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.Originality/valueThis is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

COVID-19 mortality risk premium and the interest rate on mortgage loans

I investigate the impact of COVID-19 on the housing market, with a focus on mortgage interest rates. Rising COVID-19 fatalities result in higher mortgage interest rates. I use the Gaussian Copula approach to establish causality between COVID-19 fatalities and mortgage interest rates. Finally, I present a thorough examination of the mechanisms by which COVID-19 fatalities influence mortgage interest rates. The health concern channel is the most important. Banks raise the interest rate because they are concerned about the borrower's health or potential death. Lower interest rates for borrowers who alleviated this type of worry from banks by purchasing mortgage insurance.

아파트 매매가격, 전세가격, 월세가격 변동성 결정요인

In this study, the financial time series trend is predicted using Hamilton's Markov regime-switching model to observe the factors of apartment sales, jeonse, and monthly rent prices that change from time to time from a Markov perspective. Inflation since the coronavirus pandemic has pushed the United States interest rate higher, and South Korea is also raising its benchmark interest rate to stabilize the economy. Due to the impact of this increase in the base rate, the mortgage interest rate has also risen, which has a chain effect on the sale price of apartments, the price of jeonse, and the price of monthly rent. Changes in the trend over the long term can be interpreted as exogenous shocks, economic measures, or structural changes. As a result of the Markov model analysis, apartment sales prices, jeonse prices, and monthly rent prices all showed a higher probability of maintaining a recession period, and among them, the recession period for apartment sales price, and jeonse prices was relatively more likely to last longer. In the case of the expected duration period, the duration of the depression phase of apartment sales prices and jeonse prices was similar, while the duration of the depression phase of apartment monthly rent prices was short and highly volatile. As a cause of volatility, loan interest rates were found to be a determinant.

DETERMINAN PERMINTAAN KPR FLPP PADA PT. BANK BTN KANTOR CABANG SYARIAH

This research aims to determine the effect of income, population, murabahah margin, mortgage interest rates at competing banks, and age on the demand for FLPP (Housing Finance Liquidity Facility) mortgages at PT. Bank BTN Syariah Malang for the 2020-2022 period using secondary data. The reason for choosing Bank BTN Syariah Malang is because Bank BTN Syariah occupies the second position in the category of the best KPR Subsidy distribution in Indonesia, which is 14,052 units. This research uses quantitative methods with multiple linear regression analysis. The results of the study prove that income variables have a positive and significant effect on the demand for FLPP home ownership loans. The variability in the number of married people and mortgage interest rates at competing banks has a negative and significant effect on the demand for FLPP home ownership loans. Meanwhile, murabahah margin and age variables do not significantly affect the demand for FLPP home ownership loans. The variables of income, population, murabahah margin, mortgage interest rates at competing banks, and age simultaneously affect the demand for home ownership loans, housing finance liquidity facilities.

Testing Housing Price Drivers in Santiago de Chile: A Hedonic Price Approach

Hedonic modelling is an empirical analysis technique that is widely used to review the housing market and unpack the main determinants of price. A correct determination of housing price drivers allows a weighted prediction of the value of each dwelling for sale or rent. This paper reviews the fundamental determinants of housing prices that have been published in the literature to see which ones have a better predictive fit for the case of the housing market in the city of Santiago, Chile. From a record of 456,000 property transactions, a dataset composed of 11 explanatory variables is elaborated. Through a semi-logarithmic regression, 4 variables that contribute to explaining the formation of housing prices in Santiago, Chile, are identified. The results indicate that the socioeconomic characterisation of urban areas where housing is traded, the price of copper on the London Metal Exchange, the mortgage interest rate, the age of residential buildings are the main drivers for the main drivers of prices in Santiago. Given the crisis of access to housing in Chile, the article provides relevant information for decision-makers in housing policy.

Challenges Hampering Adoption of Different Housing Models to Access Housing by Households in Nairobi County

Purpose: This study aimed to assess the challenges hindering the adoption of diverse housing models for household access to housing in Nairobi County, Kenya. Utilizing the Public Interest Economic Regulation Theory (PIERT), the Q Theory of Housing Investment, and the Theory of Distributive Justice, the research explores the interplay between government regulations, market forces, and social equity in the context of affordable housing.
 Methodology: Employing a descriptive research design, the study investigates the impact of housing regulations on housing access through mortgage and cooperative models. The research sample comprises 385 households, CEOs of housing financing organizations, and government officers. By analyzing the usage prevalence of housing models, the study delves into challenges such as high-interest rates, limited mortgage financing access, and insufficient awareness about housing options. The research employs a nuanced approach, considering the competitive dynamics within the housing sector and the need for equitable resource distribution.
 Findings: The study reveals that the mortgage model is most prevalent (41.4%), followed by the incremental model (28.0%) and cooperative model (23.7%). High-interest rates, restricted mortgage financing access, and inadequate awareness about housing options emerge as significant challenges hampering housing model adoption. Additionally, housing regulations, vital for safety, impact affordability, with 39.2% of respondents acknowledging this influence. 
 Unique Contribution to Theory, Practice and Policy: This study contributes valuable insights to the existing body of knowledge by synthesizing the PIER, Q Theory of Housing Investment, and Theory of Distributive Justice. The study therefore validates these theories by offering a comprehensive understanding of the intricate interplay between regulations, market dynamics, and social equity in the adoption of affordable housing models. The recommendations, including lowering mortgage interest rates, enhancing financing access, promoting awareness about housing models, and fostering collaborative efforts among stakeholders, provide actionable strategies for policymakers, practitioners, and researchers to improve housing access in Nairobi County.

Prepayment and Default Risks of Mortgage-Backed Security Collateral Pools

Mortgage-backed securities (MBS) are structured financial products that are produced via securitization of mortgage loans. Due to the nature of securitization, all risks of mortgage loans are transferred from originators to MBS investors. Prepayment and default risks of mortgages lead to uncertainty in MBS cash flows and create a complex problem for valuation of these instruments. Therefore, estimating these mortgage termination risks has become the focus of valuation of MBS collateral pools. This study explores two questions by using a publicly open dataset provided by Fannie Mae. First, two machine learning algorithms (Random Forest and Multinomial Logit Regression) are used for classification to predict whether a mortgage loan is likely to be prepaid, defaulted or current. Afterwards, Competing Risks Cox Regression Analysis is performed to see determinants of when mortgage termination risks are likely to happen. It is found that not all mortgage borrowers behave optimally in their prepayment and default decisions. Therefore, in addition to refinancing incentive and negative equity which depend on variations in prevailing mortgage interest rates and housing prices, heterogeneity in mortgage borrowers’ behaviors and loan characteristics, and also local economic factors are significantly important in estimating mortgage termination risks. It is worth noting that prominence role of mortgage payment delinquencies in particularly predicting defaults emphasizes the essential need of monitoring payments by servicers to keep safety of MBS investors and financial markets.

A long short-term memory model for forecasting housing prices in Taiwan in the post-epidemic era through big data analytics

This study aims to analyse housing prices in Taiwan in the post-epidemic era, identify the crucial factors influencing them, and develop a suitable method for analysing and forecasting them. This study collects relevant data such as Taiwan's housing price index data from 2002 to 2020 to identify the crucial factors affecting Taiwan's housing prices; this is achieved by constructing a regression model, forecasting Taiwan's housing prices through a constructed long short-term memory (LSTM) model that employs big data analytics, and verifying the efficiency of the proposed models through R-square and root mean square error values. The results indicate that the top 10 factors affecting Taiwan's housing prices are mostly related to mortgage interest rates, suggesting that in Taiwan, the effect on housing prices in the post-epidemic era may be non-significant. This study collects data on Taiwan's housing price for the period from the first quarter of 2002 to the fourth quarter of 2020 to construct an LSTM for forecasting Taiwan's housing prices. The results indicate that the proposed LSTM exhibits good fitness, indicating that the model is suitable for analysing and forecasting housing prices. Given that analysing and forecasting quantity is also crucial in housing market analyses and that this study focuses only on predicting housing prices, future research should explore the simultaneous prediction and analysis of both price and quantity.

Heterogeneity of Mortgage Refinancing Channel in Russian Regions

During the period of monetary policy easing in Russia, mortgage refinancing became popular, which involves the issuance of a new mortgage housing loan to repay an earlier loan on more favorable terms. There is a regionally heterogeneous refinancing of mortgages at lower mortgage rates. The purpose of this paper is to quantify the impact of monetary easing on mortgage refinancing and household consumption for groups of Russian regions. Econometric methods (vector autoregression models on panel data, estimated by Bayesian methods) and multivariate clustering (hierarchical analysis, k-means method) were used in the paper. Based on the impulse response functions, it is shown that during the period of monetary policy easing, the greatest effect of lowering mortgage interest rates on refinancing is typical for groups of regions with high- and middle-income levels, a liquid housing market, and a developed mortgage market. It is proven that refinancing is involved in the transfer of the effects of monetary policy to consumer spending and consumer lending, and this transfer is heterogeneously. Refinancing in low-income regions stimulates consumption and consumer borrowing. In regions with an average and high level of per capita cash income, after refinancing, consumer spending and consumer loans are temporarily reduced. The obtained results can be explained by the different types of financial behavior of borrowers in the use of top-up mortgage refinancing, depending on the level of their cash income. Based on the results of the study, the hypothesis about the heterogeneity of the refinancing channel in the Russian regions was confirmed. The scientific novelty of the study consists in the development of a methodological approach to the study of the refinancing channel and its approbation on Russian regional data.

A New Look on the Profitability of Fixed and Indexed Mortgage Products

This study presents a novel approach to analyzing the present value of total profit for fixed and indexed mortgage products in order to determine the optimal mortgage interest rate that would maximize the bank’s expected total profit based on applying the approach used in operations research to the field of finance. The study considers the impact of lending rate, demand, prepayment, and defaults on bank profits and emphasizes the trade-offs between potential gains and losses when setting the lending rate. As such, we not only used a fixed-rate mortgage model or an index mortgage model with the interest rate as the decision variable, but also employed mathematical analysis methods to find out the loan rate that maximizes the present value of the bank’s expected total profit. The findings revealed that an increase in interest rate, loan amount, and demand positively impacted the bank profits, while prepayment had an adverse effect. The study highlights the importance of carefully evaluating various factors that influence revenue in order to arrive at the most appropriate lending rate that will optimize profits. The results provide valuable insights into the optimal mortgage interest rate and the factors that determine the revenue and profits of a bank, with implications for cost–benefit analysis, fixed-rate mortgage, indexed mortgage, lending rate, defaults, and maximum profit. This study contributes to the existing literature on mortgage products. It provides practical implications for banks in managing their mortgage products efficiently in order to enhance their financial performance and recommends optimizing mortgage interest rates for maximum bank profits by taking the lending rate, demand, and prepayment effects into account.

The empirical modelling of house prices and debt revisited: a policy-oriented perspective

The recent boom in house prices in many countries during the COVID-19 pandemic and the possibility of household financial distress are of concern among some central banks. We revisit the empirical modelling of house prices and household debt with a policy-oriented perspective using Norwegian data over the last four decades within the cointegrated VAR model. Our findings suggest, in line with previous work, a long-run mutually reinforcing relationship between these financial magnitudes, and thus the potential for the build-up of financial instabilities and spillover effects to the real economy. Applying a policy control analysis, we find that both house prices and debt are controllable magnitudes to some pre-specified target levels through the mortgage interest rate, which enables the central bank to reduce large fluctuations and bubble tendencies in the housing market. The present control analysis thus provides some useful policy implications from empirically relevant representations of two important financial factors entering the decision process of the policy maker.

A Real Estate Focuses on Covid-19 Accounting Considerations – The Case of Abuja, Federal Capital Territory in Nigeria

The COVID-19 pandemic quarantine has affected every sector and area, including families, communities, and economies worldwide. Global businesses have suffered income losses, higher operational expenses, and delayed credit collection. To address these challenges, the government should implement fiscal stimuli for the real estate industry through central banks and mortgage financing regulators. This will preserve investments, save low-paying jobs, and maintain housing affordability for low-income families. Lowering mortgage interest rates and expanding access to preferential home finance loans are crucial measures. This research focuses on accountants' roles in Abuja's real estate business and examines the impact of COVID-19 on the market. Understanding the effects of mortgage financing difficulties is essential for assessing market competitiveness. The pandemic has affected every aspect of the real estate sector, making it important to consider the perspectives of stakeholders. The regulatory framework implemented due to COVID-19 has reduced rental prices and operating income for real estate businesses. The pandemic's global quarantine measures have made financial instruments less attractive, leading to income losses, increased operating costs, and delayed credit collection for organizations worldwide. In Nigeria, property owners are allowed to postpone annual reports by up to three months. This research employs a cross-sectional design and investigates the impact of COVID-19 on accounting practices in Abuja's real estate market. Quantitative data is used for statistical analysis, adopting a deductive approach. The study examines accounting problems faced by real estate businesses in Abuja following the COVID-19 outbreak. It reveals significant challenges such as decreased demand, disrupted supply chains, and increased expenses. Consequently, accounting practices, especially in areas like cash management, cost control, and financial reporting, have been greatly affected. Transparent and effective communication of financial information is crucial during crises to maintain stakeholder and investor confidence.

Analyzing the Relationship Between Mortgage Interest Rates and Delinquency Probability: A Causal Investigation

Analyzing the Relationship Between Mortgage Interest Rates and Delinquency Probability: A Causal Investigation