I wrote an article about foreclosed houses a few days ago and found that more and more houses in China have been cut off.
User portrait and data analysis of China’s foreclosure houses in 2023
I spent 2 hours this morning writing a Monte Carlo algorithm simulation to predict the probability of mortgage loan failure.
Let me first introduce you to the common data on mortgage loans. Different repayment methods have different total repayments. 1 million loan, repayment in 30 years, calculated based on the central bank’s benchmark interest rate of 4.9% over 5 years. If the user chooses the repayment method of equal principal and interest: the monthly repayment amount is 5307.27, then the total interest is 910616.19, and the total principal and interest is 1910616.19; if The user chooses the equal principal repayment method: the first month's repayment amount is 6861.11 yuan, the repayment amount is gradually reduced, the total interest is 737041.67 yuan, and the total principal and interest is 1737041.67 yuan.
It is difficult to write a residential mortgage loan model because many people have different savings, marital status, work status, and education levels. So I wrote several grouping models to simulate. These models can be progressively optimized if more time is given.
Monte Carlo housing outage model 1_low turnover rate_high savings
Our model first considers the impact of turnover rate on housing mortgage loans. The number of civil servants in my country is about 7 million, accounting for 0.5% of the total population. Most of the jobs are absorbed by private enterprises. The average lifespan of private enterprises in China is only about three years. Coupled with the company's KPI performance appraisal, elimination system at the end of the period, and poor credit liquidity, it is difficult to maintain a job in one company for a long time.
I initialize some variables first. The number of housing mortgage loan borrowers is initialized to 10,000. The larger the number of people, the better the simulation effect of the program. However, when the number of people reaches a threshold, the difference in model prediction results is very small. 10,000 people are initialized here, which is enough to achieve the ideal simulation prediction state.
house_price=1000000 means that according to the contract, the house price is 1 million.
funds=500000 means that the mortgage borrower has 500,000 yuan in savings, that is, after paying the down payment, there is still 500,000 yuan at his disposal. This 500,000 includes all the money you can borrow from relatives and friends. This is just an initialized average.
initial_wager=5000 means that the monthly housing mortgage repayment is 5,000 yuan.
count=360 means that the loan term is 30 years, that is, the repayment time is 360 months.
num_broke represents the number of bankruptcies, which can also be understood as the number of people who have cut off their payments. If the family goes bankrupt, the mortgage payment will be cut off.
The default in the model is that the borrower's monthly income is fixed.
The model sets the monthly turnover rate to 10%.
house_price=1000000
#people表示贷款人数
people=10000
#funds表示家庭财富100万
funds=500000
#每月还款金额
initial_wager=5000
#count还款月数
count=360
#此模型当赌博次数达到10000时，破产率高达89%
#count=10000
num_broke=0When our family has sufficient funds at its disposal, the economy is stable, and the turnover rate is low, the loan interruption rate is 0. Banks make money, residents enjoy the joy of new homes in advance, and the government makes money by selling land, and everyone is happy.
As can be seen from the figure above, after 30 years, most mortgage holders still have a disposable income of 350,000, which will have little impact on their families.
Monte Carlo housing outage model 2_low turnover rate_low savings
Some families use almost all their savings to pay the down payment, and their disposable income is very low. I changed funds to 100,000 yuan, which means that the family savings are only 100,000 yuan. We then use a Monte Carlo model to observe how high the rate of discontinuation of household payments is for this group of households.
#作者Toby，邮箱[email protected]
#房价100万
house_price=1000000
#people表示贷款人数
people=10000
#funds表示家庭财富10万
funds=100000
#每月还款金额
initial_wager=5000
#还款月数
count=360When household cash flow accounts for 10% of the total house sale price, the model shows that the bankruptcy rate (off-payment rate) is 99.96%. This means that when households with low savings face a 30-year home mortgage, they will all be cut off.
The figure below visually shows that when the loan lasts for 20 years, most low-saving households will go bankrupt (cut off the loan) and owe the bank 100,000-200,000 yuan.
Monte Carlo Housing Discontinuation Model 3_Low Turnover Rate_Financial Crisis
Model 3 adds financial crisis parameters. With the rise of the Internet and mobile phones, financial crises are very close to us, such as p2p thunderstorms, telecommunications fraud, loan sharking, major medical expenses, excessive investment in education funds, rising prices, etc.
P2P thunderstorm
In June 2018, the central government and the China Banking and Insurance Regulatory Commission began to release signals to strengthen supervision on the P2P market, and the development of the P2P market came to an abrupt end. Within 42 days from June 1st to July 12th, 108 P2P platforms across the country went bankrupt, involving more than 7 trillion in funds. The explosion of P2P platforms has caused the bankruptcy of many companies or individuals, which is an important reason for the increase in foreclosed property data. The vast majority of people in China buy houses with loans, and many residents are tempted by high interest rates and invest their money in p2p. With the explosion of P2P, the assets of residents or companies have been wiped out, and they will inevitably be unable to continue to repay bank mortgages. Of course, the number of foreclosed houses has increased sharply.
Telecommunications fraud
According to the latest data released by the Ministry of Public Security, a total of 464,000 telecommunications network fraud cases were uncovered nationwide in 2022, a year-on-year increase of 5%. Our country loses nearly 2 trillion yuan every year due to online telecommunications fraud. What a concept, our country’s defense budget in 2023 is less than 1.6 trillion! It can be seen that online fraud has caused huge harm to the country and the people. Internet fraud mainly targets people aged 20-40. About 170 million people are defrauded every year, of which 70.3% are men and 29.7% are women. The post-90s generation accounts for 36.4% of the total number of defrauded people. According to telecom fraud news in northern Myanmar, the redemption fee per capita is 200,000-500,000, which is enough to wipe out the wealth of ordinary families or turn it into a negative number.
Why has the central government recently vigorously cracked down on telecommunications fraud? You may understand when you see this. The stricter the crackdown on fraud, the better the protection of residents' savings will be, and the lower the rate of discontinuation of service. If all residents' money is defrauded by telecommunications fraud, who will have money to repay the bank's housing mortgage loan?
Therefore, there are important economic factors behind these political and military activities.
online loan
Information from People's Daily Online shows that the number of active lenders and active borrowers in the online lending industry in August was 1.8514 million and 2.1544 million respectively. From this, it is estimated that the annual number of online loan borrowers is about 20 million. Many online loans are usurious and routine loans, and there are only a few online loan platforms with formal licenses. When encountering usurious loans, there is a high probability that the routine loan will go bankrupt, which means the mortgage loan will be cut off.
Stocks fell
In 2018, the Shanghai Composite Index and the Shenzhen Component Index fell 24.6% and 34.4% respectively throughout the year, both recording the largest declines in 10 years. The GEM Index and the Small and Medium Enterprises Index fell 28.7% and 37.8% respectively. The stock market is also an important investment channel for residents. When residents lose money in the stock market, their disposable income will naturally decrease, which will also affect their mortgage repayments.
From the analysis of the monthly K data of the Shanghai Composite Index, this is a wave-shaped distribution. Most investors were trapped in the position at a high level and could not endure the long-term suffering. Then they cut their flesh at a low level and suffered heavy losses.
The stock market has repeatedly cut off investors, which will also reduce household disposable income.
Compare this to the S&P 500 Index in the U.S. stock market, which shows a long-term rising trend, with household income increasing as the stock market rises.
Educational capitalization and involution
The involution and capitalization of education is no longer news. Capitalists spread false information through public opinion and the media every day, constantly increasing parents' anxiety.
In order to ensure that children do not lose at the starting line, many tuition fees have been priced according to w. Educational expenses have become a heavy burden on family income, and there will be less and less money to repay bank loans, and the probability of discontinuation will increase.
At present, many kindergartens have tutoring classes. School does not end at 5 o'clock and continues to participate in various training classes. Of course, many kindergarten make-up classes are initiated by most parents. Very few elementary schools finish school on time, and make-up classes usually last until 5:30 to 6:30 pm. Evening self-study in junior high schools and high schools lasts until 8:30 pm, and in some schools it is even later. Many students go home at 10 pm. Then we discovered that there are more and more children with depression and mental illness.
Back to the topic, we ran the Monte Carlo housing cut-off model 3_low turnover rate_major financial crisis model, which showed that the cut-off rate for this group of people was 100%.
As can be seen from the visualization in the figure below, most of these people will go bankrupt and cut off their repayments after 12.5 years of repayment to the bank.
Monte Carlo Housing Discontinuation Model 4_Turnover Rate_Divorce
Model 4 adds the factor of divorce to observe the impact of divorce on the discontinuation rate. The divorce rate in my country increased very slowly from 1978 to 2002, but from 2002 to 2018, the divorce rate increased significantly. The divorce rate in 2018 was 3.36 per 1,000 people.
If the mortgage loan is 5,000 yuan per month, and the couple exchanges one person's income for the housing mortgage, and one person's income is used to live and raise children, they can barely survive. But after the divorce, the debt is borne by one person, and the family's disposable funds are almost halved.
According to big data, divorce peaks between 6 and 8 years after marriage. This is the saying behind the 7-year itch. Teacher Toby added into the program that when the repayment time is 7 years, the family gets divorced, and then observes that the repayment rate is 0.988. It seems that divorce is a very important factor in the loss of housing affordability.
As can be seen from the visualization in the figure below, when a family has sufficient savings and gets divorced in the 7th year after marriage, this group of people will go bankrupt (cut off their mortgage payments) in 27 years. If the family reserves are stronger, they can get through it.
Monte Carlo Housing Discontinuation Model 5_Second Turnover Rate_High Cash Flow_Extra Long Repayment Period
Model 5 mainly observes the impact of the repayment cycle on household bankruptcy and cut-off.
The results of the model operation show that the longer the loan period, the higher the probability of household bankruptcy and discontinuation of payment. This is the law of large numbers, and the professional term for casinos is that if you gamble for a long time, you will lose.
Yamamoto Isoroku likes to decide the outcome with one hand. If there are many unfavorable factors in the future, the more times he plays, the greater the chance of capsizing.
Residents are not fools and can do simple math. China Youth Daily and other media showed that there is currently a wave of early repayments. Lenders seem to understand something. Paying off debts early can avoid family bankruptcy.
Monte Carlo Housing Discontinuation Model 6_High Turnover Rate_High Savings
Finally, I used Model 6 to look at the impact of high unemployment on supply cuts based on high savings. I adjusted the monthly unemployment probability to 30%, and the household disposable funds are still 500,000 yuan. The model shows that the supply cut-off rate is 83.3%. This shows that high unemployment has a great impact on the suspension of housing mortgage loans.
Summary
To sum up, through Monte Carlo model simulation, I found that high unemployment, divorce, and major financial crises are important factors in cutting off supply, which are also consistent with business logic.
High household savings, high disposable funds, and high income are positive factors that can reduce the probability of interruption of supply.
Teacher Toby believes that whether you should take out a loan to buy a house needs to be discussed in different categories. If you are a bank executive, financial executive, or Wall Street veteran, buying a house will bring you half to three years of income, so there is no pressure.
If you are a civil servant in a big city, have a stable and harmonious family, and a stable income, the probability of paying for a house in the long run is extremely low.
If you are self-employed or a company owner and your income is stable or rising year by year, buying a house is not a problem.
If your family savings are very low, you work in a private enterprise, in a non-core technical position, and you have a long-term loan to buy a house, you will easily fall into the supply cut-off trap.
Judging from the above models, long-term housing mortgages have caused residents' savings to drop significantly or even go bankrupt. The debt economy cannot be maintained for a long time. To put it simply, the debt economy is killing the goose and retrieving the eggs in slow motion.
In fact, banks don’t want residents to cut off their mortgage payments. What they want is your money, not your house. The discount rate of foreclosed houses is 70-90%, and banks also suffer heavy losses when faced with foreclosed houses. The failure rate of Lanzhou’s foreclosure houses is over 90%, and the Lanzhou Bank may have fainted in the toilet from crying.
"The Mathematical Trap Behind Long-Cycle Mortgage Loans - Monte Carlo Algorithm Reveals Why More and More Mortgage Cutoffs Are Increasingly" will be introduced here. The debt economy cannot be maintained for a long time, and banks, residents and local economies will fall into a long debt cycle in a few years.
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Author Toby, source: python risk control model, "The mathematical trap behind long-term mortgage loans - Monte Carlo algorithm Monte Carlo reveals why there are more and more supply interruptions"< /span>