Malaysian residential mortgage loan default: a micro-level analysis

Adzis, Azira Abdul; Lim, Hock Eam; Yeok, Siew Goh; Saha, Asish

doi:10.1108/rbf-03-2020-0047

Cited by 6 publications

(5 citation statements)

References 78 publications

(148 reference statements)

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“…Notice that the positive sign of the logistic coefficient indicates that the higher the x i value, the higher the probability of default and vice versa. Therefore, the male is more likely to default as compared to the female, which is similar to the finding of Lin et al (2017), Chamboko and Bravo (2019), Adzis et al (2020), and Saha et al (2021). Adzis et al (2020) recommended that the Malaysian lenders may create a new mortgage loan product specifically for female borrowers since women are more responsible in their loan repayment rather than male.…”

Section: Resultssupporting

confidence: 70%

“…Therefore, the male is more likely to default as compared to the female, which is similar to the finding of Lin et al (2017), Chamboko and Bravo (2019), Adzis et al (2020), and Saha et al (2021). Adzis et al (2020) recommended that the Malaysian lenders may create a new mortgage loan product specifically for female borrowers since women are more responsible in their loan repayment rather than male. Besides, we can conclude that the credit score of the tenant decreases when the tenant’s number of months late payment, the expense-to-income ratio, or age increases.…”

Section: Resultssupporting

confidence: 70%

“…Chamboko and Bravo (2019) found gender, age, income earned, debt-to-income ratio, loan terms, and the number of past missed payments in Zimbabwe. On the other hand, Adzis et al (2020) and Saha et al (2021) concluded house equity, age, gender, ethnicity, location, the types of occupation, guarantor availability, and loan characteristics like payment-to-income (PTI) ratio, loan original balance, loan tenure, loan interest rate, and loan-to-value (LTV) ratio are the significant factors that influence loans default in Malaysia. The chance of individuals who lack credit histories getting a loan will be increased if financial institutions use non-financial data to develop the credit scoring model.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Tenant’s Credit Scoring Using Logistic Regression

Ling,

Jamaian,

Mansur

et al. 2023

Sage Open

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This study implements the multivariable logistic regression to develop a credit scoring model based on tenants’ characteristics. The credit history of tenant is not considered. Rental information of tenants was collected from a landlord company in Malaysia. Parameters of the multivariable logistic regression were estimated by using the penalized maximum likelihood estimation with ridge regression since separation in training data was detected. The initial factors considered that affect tenants’ credit score were their gender, age, marital status, monthly income, household income, expense-to-income ratio, number of dependents, previous monthly rent, and number of months late payment. However, the marital status factor was then excluded from the logistic regression model due to its low significance to the model. Meanwhile, a tenant’s credit scoring model was generated by calculating the tenant’s probability of defaulting. The main factors of the tenant’s credit score are the number of months late payment, the expense-to-income ratio, gender, previous monthly rent, and age. There is no underfitting or overfitting in the proposed credit scoring model which means the model’s bias and variance are low.

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Section: Resultssupporting

confidence: 70%

Section: Resultssupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

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Modeling Tenant’s Credit Scoring Using Logistic Regression

Ling,

Jamaian,

Mansur

et al. 2023

Sage Open

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“…-------------- ------------------------ -----------------Total Equity Ratio 9 Earnings Growth Variance between income at t period with income at t-1 period (EBITt -EBITt-1) EG = - -------------------------- Analysis technique applied was binary logistic regression because independent variable in this study was a dichotomous variable consisting of two nonmetric (dummy) categories (Gregova et al, 2020). A logistic model is more effective than a probability model based on linear regression since it can accommodate both qualitative and quantitative inputs (Adzis et al, 2020). The logistic regression equation models in this study were:…”

Section: Methodsmentioning

confidence: 99%

Corporate Governance and Financial Ratios of Manufacturing Companies on Financial Distress: The Main Board and Development Board of IDX

Adielyani

Pangestuti

2023

mem

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Financial distress is a condition where a company is unable to meet its obligations when they directed to bankruptcy. The purpose of this study was to analyze influence of corporate governance, profitability, liquidity, leverage and earning growth on financial distress. Research sample data used in this study were manufacturing companies listed on the main board and development board on the Indonesia Stock Exchange in 2016-2020. Logistic regression analysis had been applied to analyze data of study. The results show that the factors that influence Financial Distress in manufacturing companies on the main board and development board were different. For manufacturing companies on main board, influencing factors of financial distress were independent audit committee, liquidity, leverage, and earning growth. Meanwhile, board size, profitability, liquidity, leverage and earning growth were influencing factors of financial distress for manufacturing companies on development board.

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“…Yusof et al (2018) provided evidence of the effect of macroeconomic shocks on the short-and long-run effects of home financing offered by Islamic and conventional banks. Moreover, Adzis et al (2021) demonstrated interest rates as a significant determinant of mortgage loan default. Lo et al (2022) discussed the importance of monetary supply, stock market, and foreign exchange markets for housing price to rent ratios in the UK.…”

Section: Literature Reviewmentioning

confidence: 99%

The asymmetric impact of macroeconomic variables on Islamic bank home financing in Indonesia

Fakhrunnas,

Chowdhury,

Anto

2023

J. Eko. Keu. Isl.

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Purpose – The study investigates the performance of non-performing financing (NPF) in the housing sector concerning the asymmetric influence of macroeconomic conditions. Methodology – The method of the study adopts nonlinear autoregressive distributed lag (NARDL), while the data of the study ranges from 2014m6 to 2022m2.Finding – The findings reveal an asymmetric relationship between macroeconomic variables and NPF is present both in the short and long run. In addition, the COVID-19 has worsened the NPF of Islamic banks' home financing in some sectors in the short run, while only such financing in the flat and apartment market has suffered during the pandemic in the long run.Implication – The implication of the study suggests that the Islamic banking industry and financial authority need robust risk management and financial policies, respectively, to achieve and maintain the stability of Islamic banks in home financing activities. Originality – The study is original in measuring the impact of macroeconomic influences on Islamic banks' home financing from a nonlinear viewpoint.

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Malaysian residential mortgage loan default: a micro-level analysis

Cited by 6 publications

References 78 publications

Modeling Tenant’s Credit Scoring Using Logistic Regression

Modeling Tenant’s Credit Scoring Using Logistic Regression

Corporate Governance and Financial Ratios of Manufacturing Companies on Financial Distress: The Main Board and Development Board of IDX

The asymmetric impact of macroeconomic variables on Islamic bank home financing in Indonesia

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