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Research on Housing Prices and Their Relationships with Mortgage

Author: ShenXiaoFeng
Tutor: JiangZuo；TianPeng
School: Shanghai Jiaotong University
Course: Management Science and Engineering
Keywords: Housing prices Home mortgages Generalization regression neural network Life-cycle model Cointegration Analysis Error correction model
CLC: F832.4;F224
Type: PhD thesis
Year: 2007
Downloads: 795
Quote: 1
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Abstract

In recent years, China's housing prices continued to rise, 1 quarter of 2006, compared with the 1 quarter of 1999, the national average price rose 42.93 percent, which is 5.6 times the CPI increase over the same period. Rising prices at the same time, personal housing mortgage loans is also rising rapidly, the end of 2005, personal housing mortgage loan balance of 1.84 trillion, 43 times in early 1999. Housing as a necessity of human life and an important part of the wealth of the residents, caused by a surge in house prices in recent years the government attaches importance to the masses are concerned, and social concerns. Economists and policy-makers face the problem can not be avoided as follows: (1) China's housing price changes in laws? (2) housing prices and housing mortgage synchronous high, exactly how the relationship between the two, and whether there is a causal relationship, if there is a causal relationship, then what is due? whether the fruit? answer relationship residents purchase decisions on the above issues, the judgment of the relationship of the housing mortgage risk the formulation of monetary policy, the relationship between housing. To answer these questions correctly, must be established on the basis of the scientific understanding of the law of motion of housing prices and housing mortgage. Start from the analysis of the time series characteristics of housing prices, the law of motion study of housing prices, on this basis, the use of generalization regression neural network (GRNN) housing price forecasting model and the use of varying parameters cointegration, multivariate cointegration and error correction model, a thorough and systematic study of the relationship of housing prices and housing mortgage. The conclusion of this paper, the research and the formation include the following six areas: (a) analysis of the background and significance of the topic, at home and abroad about housing prices forecast, housing prices and their impact factors relationship research literature, summed the research and experience of existing literature, analysis of the problems in the relationship studies of housing prices in China and its influencing factors, and pointed out possible research direction. (B) the use of the JB test, the autocorrelation function and the normality of the ADF and PP test time series on China's housing prices autocorrelation and stability system, the results found: China's housing prices time series was right the distribution characteristics of partial and spikes, showing a high degree of self-correlation and mean reversion weak autocorrelation coefficient for a long duration, a negative correlation coefficient in the long lag period, the characteristics of long memory time series of housing prices. Housing price time series are non-stationary series, a sequence of first-order differential housing prices steady housing price time series is a sequence of order one. When certain long lag period (≥ 9 months), Beijing and Shanghai housing price changes Granger causality to each other, the two housing prices in the same influencing factors. China's housing prices time series of significant autocorrelation dependencies between the front and rear of the housing price time series data, you can take advantage of past and current housing prices make predictions on the future of housing prices in housing price forecasting Technical analysis is feasible. (C) review of domestic and foreign research literature on the effectiveness of the housing market, and learn from abroad, the housing market is weak-form efficiency runs test, and test the effectiveness of the housing market in Beijing and Shanghai, found that Shanghai housing market to reject the efficient market hypothesis, Beijing housing market acceptance weak form efficient market hypothesis. In the analysis of time series characteristics on housing prices, housing prices in Beijing and Shanghai housing prices as presented significant autocorrelation. In the capital markets, securities prices since the correlation can be explained with the capital market is not valid. However, in the housing market, the empirical results show that the efficient market theory alone can not be a reasonable explanation autocorrelation housing prices. Because the housing market is different from the securities market, the housing market can not completely copy the capital markets. On the housing market, even if it is a valid market, due to the particularity of the housing market, housing supply lagging adjustment, the non-rational expectations in the housing market, housing finance down payment constraints are likely to cause the housing price adjustment delays. (D) according to the housing price time sequence order one of the characteristics and autocorrelation, In Shanghai, for example, the establishment of a housing price forecasting ARIMA model, and use the model to predict the ARIMA model forecast Shanghai housing prices accuracy is not high, the explanatory power is not strong. In view of the limitations on the predictive ability of the ARIMA model, this article will generalization regression neural network (GRNN) forecast for housing prices, the GRNN network of highly nonlinear mapping ability and fast computing power, more suitable for the simulation of complex systems. GRNN network forecast results and ARIMA model to predict the results of comparative analysis, the discovery the GRNN network method to predict the relative error of 0.37%, while the the ARIMA forecast relative error of 1.96%, after repeated training of a large number of samples GRNN network simulation Shanghai The behavior patterns of housing prices, GRNN model is more suitable than the ARIMA model in Shanghai housing prices to predict. Of course, the above conclusions from Shanghai housing price forecast comparison, GRNN network prediction method suitable for housing price forecasts for other regions of China also needs further verification. ARIMA model method and the GRNN model method itself is not better or worse, to choose what kind of model depends primarily on the characteristics of the system to study. Housing prices time series data generation system features the model to reflect the behavior patterns of housing prices, the predictive ability. On the contrary, does not meet the housing price time series data generation model of the system features, even advanced, more complex effect on prices forecast is not necessarily good. (E) In Shanghai, for example the relationship between housing housing prices and housing mortgage loans and its components. Step 1: The quantitative relationship between the assumed housing prices and housing mortgage does not change with time-varying empirical study, using statistics from August 1996 to August 2006, found that between housing prices and housing mortgage loans and its components there is no long-term cointegration relationship. Step two: use standard Granger causality test on the causal relationship between housing prices and housing mortgage research. The study showed that: the housing mortgage loans to the total amount of housing prices Grange reason, but the opposite direction of the causal relationship is not established; commercial individual housing loans Grange reason housing prices, but the opposite direction of causality is not established. Careful analysis of the conclusions found in the study period, the housing market is not a stable market. Due to stop in-kind housing distribution, households increased demand for housing. Reform of the central bank to support the housing system developed individual housing mortgage and cancel the direct control of the size of the commercial bank credit, coupled with commercial banks increasingly competitive business, residents get housing mortgage loans has become increasingly easy. Affected by the policy changes, the relationship between housing prices and housing mortgage is constantly changing, and has undergone a series of structural changes. Although the housing reform policy and housing finance policies specific time, but the process of digestion and absorption in the market is difficult to grasp, and the complexity of the various factors, and so can not be added to the linear equation dummy variables to analyze changes in factors, but to use a variable parameter model appropriate. Therefore, in the third step: get rid of the constant parameters of the basic model assumes that, with time-varying parameters instead of constant parameters, assume that housing prices and housing mortgage loans, the correlation coefficient for the AR (1) state transitions form using a Kalman filter algorithm to estimate the time-varying parameter model. The results show that: between housing prices and housing mortgage the cointegration relationships change over time - in 2000, the relationship between housing prices and housing mortgage regularity is not obvious; 2000, mortgage loans for housing the positive impact of the price gradually increase and an upward trend. Residual plots, the mean and standard deviation compared to the estimated effect of the time-varying parameter model is better than the constant parameter model, time-varying parameter model to better describe the relationship between housing prices and housing mortgages. In the same time, the empirical proof of the the variable structure characteristics of the housing market, further demonstrates why the ARIMA model is not suitable for Prediction of housing prices in Shanghai. More importantly, the empirical results can help us to recognize a real problem. Everybody is concerned about the threat to the financial stability of the housing bubble, in fact, overly loose monetary policy and excessive financial support is one of the important factors in the factors that contributed to the housing bubble, housing bubble needs to avoid a hedge against financial policy in the first place. Especially in the context of financial institutional change, uncertainty and information asymmetry, competition between financial institutions and short-sighted, leading to a large number of loans to invest in housing investment (speculative), a direct result of the volatility of housing prices. Address residential housing must have a supply of housing mortgage loans as support, but at the same time need to pay attention to the financial support over the issue, therefore, it is recommended to always adhere to the \buyers \In addition, according to our research, the Grange due to the rapid growth in house prices than housing mortgages, illustrates the housing mortgage loans soaring in recent years, mainly due to apply for mortgage loans to purchase the amount of business growth, rather than from housing prices as collateral growth, mortgage balance is not high prices pushed up the home mortgages of the study period dependent on the value of the collateral is not an insignificant risk. (F) the life cycle model of housing prices in Shanghai, for example, to build the urbanization indicators, housing prices and per capita disposable income income expected (unemployment rate), housing, mortgage interest rates and urbanization, and other variables multifactorial model EG two-step cointegration analysis and error correction model, housing prices and per capita disposable income, income expectations, mortgage interest rates and urbanization variables between the long-term equilibrium relationship and short-term affect the relationship. The results show that: the long-run equilibrium relationship between these variables. The long-term income expected (the unemployment rate), mortgage rates, urbanization, and the mortgage balance is to determine the main factors to affect the level of housing prices. From the impact, 1% of the mortgage balance growth, will result in a 0.21% growth in housing prices; 1% of the rural population reduction will result in a 0.17% growth in housing prices; decrease of 1 percent income expected (unemployment rate), housing The price will increase to 0.74%; mortgage rates increase 1 percent, housing prices fell 0.08%. Income expected (the unemployment rate) and the mortgage balance on housing prices, and these two variables is the short term impact of housing price changes. The equalization error correction term coefficient of 14%, the Shanghai housing market prices adjust faster adjustment of each month 14% deviation from the long-run equilibrium. Lag of two housing price changes affect the elasticity of the price changes for the current period was 0.42, three housing price changes lag the impact of the change on the current price elasticity of 0.34, four housing price changes lag the impact of the change on the current price elasticity of 0.20, revealing a housing The price adjustment process is a sticky price adjustment process lag housing price changes prompted the main reason of short-term fluctuations of housing prices, the housing market does not comply with the efficient market hypothesis, irrational expectations of buyers in the housing market is expected to. Buyers predict future prices, housing prices and their change in prices surged stage, prone to speculative bubbles, prices may significantly deviate from its long-run equilibrium value, coupled with the housing market at this stage is the lack of long-term purchasing power. support, the price adjustment speed is faster, the Shanghai housing market prosperity - recession cycle relatively fragile housing market the government should give more attention to ensure a smooth development of the housing market. The innovation of this study is reflected in the following four aspects: (a) weak-form efficiency empirical results show that the effectiveness of the capital market theory can not be used to explain the self-correlation of housing prices. This article from the inherent properties of the housing market, a systematic analysis of housing prices since the correlation of the reasons, that the housing supply lagging adjust the non-rational expectations in the housing market, housing down payment financing constraints are likely to cause housing price adjustment delays. (B) generalization regression neural network (GRNN) for housing price forecasting, analysis and the ARIMA forecasting results contrast obtained GRNN model is better than the ARIMA model forecast Shanghai housing prices to prove the non-housing market linear features. (C) the use of time-varying parameters cointegration relationship estimation and testing methods, housing prices and housing mortgages relationship to time-varying parameter model to arrive at housing prices and housing mortgage change over time, the long-term equilibrium relationship between and time-varying parameter model superior to the conclusion of the constant parameter model to describe the relationship between the two. (D) Construction of the indicators of urbanization and urban unemployment rate as a proxy variable for income expectations, the establishment of the Shanghai housing prices and urban per capita disposable income, income expectations, mortgage interest rates, mortgage the total balance of the loan, as well as variables such as urbanization between the multi-factor model, the use of EG two-step cointegration analysis and error correction analysis obtained housing price and income expectations, mortgage rates, and the existence of a long-run equilibrium relationship between the variables of the total mortgage balance and urbanization. Per capita disposable income of urban residents and housing prices have not significantly correlated. The factors affecting the price of housing, urbanization and the mortgage balance is to determine the main factors that affect the level of housing prices. Overall, this dissertation research in the understanding of the law of motion of housing prices and housing prices and mortgage relationship research gives some new conclusions for the development of housing and financial policies to promote China's housing market healthy development, to achieve social harmony and stability has considerable theoretical value and practical significance.

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