Study on Mortgage Lending Patterns in China

Introduction

• Objective
• Why study this issue
• Organisation of the dissertation

With China's rapid economic development, urbanization advance, driven by domestic demand and improved living standards, the residents’ demand for housing improvement has been increasing. In the foreseeable future, China will have a thriving real estate development space and potential. Financial support is necessary for real estate development, especially housing mortgage loans. For Commercial Banks in China, this is a huge cake and cheese. The one with best risk-management abilities and tools will be able to gain more market share. This dissertation take China Merchants Bank as case study on the design, methods, processes, data, development and verification of housing mortgage loans scoring card, propose home mortgage loans scorecard model, and be used effective and widely.

Literature Review – Credit Scoring and Sub-prime Crisis

Mortgage contracts: types of mortgages

Credit Scoring technologies are used to control levels of default within american consumer credit. And such technologies have been involved with its own methodological, procedural and temporal risks. And periodic renewal of models and constant reappraisal of methods and procedures are required in accessing the risk. Through testing a diffusion model of techbology adoption for the financial services industry, we find that banking organizations that are more centralized in their organizational structure and those located in the New York Federal Reserve district adpted small business credit scoring before their peers. The former is due to the influence of organizational structure on technology adoption, and the latter is consistent with theories of geography-dependent innovation diffusion. Race is influential to the mortgage lending disicion in USA. However, it is not important in China. Profession and income level have greater effect on the probability of getting the loan. I will develop another critiria to understand the pattern in China based on the study of “Understanding Mortgage Lending Patterns”by Issac F. Megbolugbe.

Background of mortgage lending in China

Strong growth in China's housing markets. The housing mortgage in China is not very old in comparison to others.

Credit Scoring system in China Merchants Bank

Comparison with UK/US credit scoring system just for one bank

China Merchants Bank (hereinafter referred to as “CMB”?) is one of the first bank engaged in studying and adopting credit scoring model in China since 2004. In 2007, CMB developed its mortgage loan application scoring model, and the model was applied countrywide to all its 700 branches next year. In 2009, with the progress of Basel II Accord implementing in Chinese banking industry, CMB developed its application scoring model, behavior scoring model, collection scoring model of mortgage loan, auto loan, personal consumption loan in succession to measure and manage its risk in a more accurate way. As for now, scoring model systems are used in the bank to cover over 95 percent of loan business in retail banking sector through various process including loan application, marketing, pricing, after sale management, default loan collection, etc. CMB’s mortgage loan application scoring model uses the same methodology as that in those active European and American banks. By collecting debtor’s risk characteristics information, shaping its default definition and performance period, utilizing logistic regression based on 0/1 distribution of dependent variable, using KS statistical analysis to test the model’s predictive capability, the scoring model was finally established.

 Due to the difference in China’s political, economic and cultural environment with western countries, CMB’s credit scoring model also differs in its design details from European and American banks. With the relatively short history in studying and adopting credit scoring model in Chinese banking industry, some shortfalls in date collection was also in existence and will be reflected in the independent risk variables as the result of the credit scoring model. For example, CMB generated different scoring model for different regional segmentation in its model designing period. The reason relies on the unbalanced economical development and different demographical characteristics and risk characteristics through over the country. The risk characteristics like age, income can have totally different impact to the credit scoring model. In some cities, elder applicant represents high income level and stable economic status, thus the possibility of loan default is low. While in some other cities, younger applicant can get financial support from the whole family, thus means lower risk of this type of applicants. However, European banks usually apply only one consistent model in one country. For some small banks, one consistent model will be applied even in several countries due to the lack of adequate sample date and similar economic and cultural environment. Another example is that the demographical information in CMB sample data is not so credible as that for Western Banks’. Most of the demographical information in CMB data comes from the application form which is filled out by applicants themselves. 

Some demographical information, like income, marital status, could not be verified through any official credit agency. In some occasions, applicants may exaggerate their income level or distort their marital status to meet the credit approval requirement of the bank. So the authenticity of the date could not guaranteed, and the bank must use those information cautiously. The relationship manager of the bank always need to assess their customers’ risk level by using the tools bank provided or with their own experience, but the assessment result are usually not quantitative output and can not be used in building the credit scoring models. While for European and American banks, the authoritative credit agencies provided a transparent and credible information environment. Banks can cheaply and conveniently access to the credit agencies for demographical information of applicants, including age, dwelling address, telephone number, period in current employment, household income, other outstanding loan, default record and tax information, and those electric information can be downloaded and integrated into the banks’ database system for the purpose of building the credit scoring model. To compare the credit scoring model development process in CMB and CITI Bank of USA, (hereinafter referred to as “CITI”), we found the same basic methodology are adopted, both banks using logistic regression to create their mortgage application scoring models, the default definition and performance period are quite close, and KS testing, out-of-sample testing and cross-time testing are all applied to validate the model’s effectiveness. 

The main discrepancy rest with the date sample date sector and can be sorted as follows: Firstly, the sample date is easily accessible from credit agencies for Citi bank, and the bank use extensively those date in its model building process. There are more than 1000 credit agencies in USA and mostly of them are subjected to three most authoritative credit agencies: Experian, TransUnion and Equifax. Citi bank receives credit information both directly from Experian and from their outsourcing credit enquiry companies in Georgia and South Dakota. But for Chinese banks, including CMB, the one and only credit information channel is the PBOC(People’s Bank of China) Credit Center established few years ago. This official credit agency started to provide its credit query service to commercial banks only in 2008. Due to its limited history, more than half of the nation’s population are still excluded from outside the system. For many applicants, Credit Center could neither verify their demographical information nor find credit record for them. Furthermore, the credit query service do not provide mass electronic date but only printable date, thus made the integration of query results with banks database an impossible mission. Secondly, for Citi bank, some demographical information, like race, gender, can’t be used in credit scoring model development due to the restriction of laws and regulations. Otherwise, the discrimination between loan applicants with different races or genders may cause serious consequences such as lawsuit against the bank. But there is not such limitation in both China’s banking regulations and traditional morality, the bank could use such information to discriminate their customers in the credit scoring model if only they have enough predictive ability and business sense.

 Thirdly, in Citi bank’s credit scoring model, the information of refusal debtors’ are also deemed as sample date, thus provide a new dimension to analysis the customers’ risk characteristics. The bank may summarize rules from the risk characteristics of those rejected applicants and adopted into the credit approval guidelines for further practice operations. CMB did not reserve any information about rejected applicant in past so that its credit scoring system did not involve any reject inference activity. Recently, CMB has started to collect information about rejected applicant, but the information can’t be used in adjust the model only when they accumulated to a certain amount after about five years. Generally speaking, CMB has already established a series of credit scoring model to cover its retail banking business including mortgage loan by using universal methodology. However, the date collection is one of its main shortages for the bank to improve the quality of its modelling practice.

Empirical Analysis of Homeowners’ Data Using Cross-sectional Analysis modelling and Probit/Logit modelling

Banking regulation in China

China’s housing market reform started in year 1988, but it was not until 1995 that China’s central bank PBOC issued its first regulation in individual household loan area. This Provisional Code of Housing Loan on Commercial Banks’ Account set the minimum mortgage down payment ratio at 30%, the maximum term of mortgage loan at 10 years, the mortgage interest rate is a preferential rate to the benchmark of fixed assets loan. In 1999, PBOC issued A Number of Views on Encouraging the Development of Consumption Loans, in which the loan to value rate loosened to 80%, the maximum term of mortgage loan to 30 years, and the lowest preferential interest rate can be 10% discount on benchmark. In 2003, PBOC issued Notice on Further Enhance Real Estate Loan Management, in which reaffirmed the 20% minimum down payment, raise the interest rate of mortgage loan to benchmark. The Notice brings forward the new concept of “second set of house”, to which the bank should raise the down payment and interest rate level to the debtor. In recent years, the skyrocketing real estate prices in China bring worries on assets bubbles in public and accumulated risks to mortgage loans and real estate loan. 

The central bank and CBRC (China Banking Regulatory Commission) constituted and enacted several regulations to guide the mortgage business with most of the measures hooked on down payment ratio and interest rate. Generally speaking, for purchase of the first set of house, the down payment requirement is at least 20% and the interest rate are relatively flexible; for purchase of the second set of house, the down payment requirement is at least 30% and the interest rate at benchmark. The relatively loosen policy was deems as one of the important drives to boost the house price to an irrational high level now in many Chinese big cities. To curb housing market speculation, the State Council announced in April, 2010 that: 1. The minimum down payment ratio is lifted to 30% for the first set of house purchase if the area is more than 90 square meters; 2. Second set of house buyers must pay at least 50% of the value for mortgage down payment, and the interest rate are at least 1.1 times of benchmark; 3. Higher down payment ratio and interest rates are required for “three of more set”of house purchase. Analyst believe the mortgage loan demand should be negatively impacted but within a limited range, and the lifted down payment ratio would provide better buffer for housing pricing decline, which would benefit loan quality. As for the banking regulations specific in the credit scoring system, it was not until 1997 CBRC released several regulatory guidelines successively along with the Basel II Accord implementation progress in Chinese banking industry. 

One of the most important is the Internal Rating System Regulatory Guideline, in which principle guidelines for building internal rating system are provided for commercial banks. Being a vital part of internal rating system, credit scoring system is also regulated by these guidelines, and the main principles on credit scoring system in the guidelines are as follows: The burden is on the bank to satisfy its supervisor that a model or procedure has good predictive power and that regulatory capital requirements will not be distorted as a result of its use. The variables that are input to the model must form a reasonable set of predictors. The model must be accurate on average across the range of borrowers or facilities to which the bank is exposed and there must be no known material biases. The bank must have in place a process for vetting data inputs into a statistical prediction model which includes an assessment of the accuracy, completeness and appropriateness of the data specific to the assignment of an approved rating. The bank must demonstrate that the data used to build the model are representative of the population of the bank’s actual borrowers or facilities. 

When combining model results with human judgement, the judgement must take into account all relevant and material information not considered by the model. The bank must have written guidance describing how human judgement and model results are to be combined. The bank must have procedures for human review of model-based rating assignments. Such procedures should focus on finding and limiting errors associated with known model weaknesses and must also include credible ongoing efforts to improve the model’s performance. The bank must have a regular cycle of model validation that includes monitoring of model performance and stability; review of model relationships; and testing of model outputs against outcomes. The bank should clearly know the basic assumptions of the model and assess the consistency between the assumptions and current economic or market conditions. The bank must demonstrate that economic or market conditions that underlie the data are relevant to current and foreseeable conditions. If current conditions change, the bank should ensure that the model can adapt the changed conditions. If model can not meet the request, the bank must adjust model conservatively. The above are all regulation items about credit scoring system. We can see those regulation items are mostly principle-based. The process and method of building credit scoring system are in compliance with the guideline on the whole. 

Although the banking regulations on credit scoring system are roughly and fundamentally, CBRC has always kept a close watch on the credit scoring model construction in the country’s biggest six commercial banks including CMB. Stringent and strict on-site assessment has been carried out by CBRC throughout the whole process of date collection, model design to parameter results to ensure the appropriate adoption of the models. In June 2010, CBRC urgently request various state-owned commercial banks, joint-stock commercial banks as well as the city commercial banks with asset size over 500 billion yuan to start self-examination for real estate loans, and conduct stress testings especially in mortgage loan business. The result shows that even under highest stress scenario that the house price drop 30% from present level, the real estate related loans will maintain good quality, and the NPL (non-performing loan) ratio for mortgage loans will increase less than 1% for the whole banking industry. It is believed that the relatively high down payment ratio and the traditional credit culture in China contributed to such a released consolable result.

Comparison of China/US/UK banking regulation and default risk

Mortgage lending in China is safer and more stable than that in US.

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