Impact Of Government Subsidy And Credit Score On Real Estate Prices: A Study Of The UK Housing Market

Determinants of Real Estate Prices

The impact of government subsidy and credit score of an individual on real estate prices are two major impact factors. In particular, the link between housing costs and the credit structure of house buying people is important. The cost of homes in the United Kingdom has expanded impressively since the mid-1990s, and the vast majority of these costs have expanded in the cost of existing homes as opposed to in new homes. Over the most recent 30 years, it has likewise turned out to be less demanding for mortgage holders to acquire against the security estimation of their home. The housing fund framework in the United Kingdom was all the while recuperating from the credit emergency and the 2007-2008 money related emergency, due to volatility in US markets. A few highlights of the framework have made it especially defenseless against this emergency, including the degree to which loan specialists have depended on currency markets and securitization to loan them and the liberality of credit conditions, particularly after 2005. The highlights helped the framework conquer the emergency, including the moderately low exchange rate and the predominance of variable rate and following home loans, which implies that a considerable lot of the present borrowers have seen their advantage installments fizzle (Dell’Ariccia, Igan & Laeven, 2008. ).

In this article, basic in the gross and disposable income of the buyers have been highlighted, the impact of the government scheme and mortgage rates on house prices were scrutinized. The central area shows a framework of the determinants of the income and financing of the financial agencies. The second area talks about rights and facts about the assistance provided by the government of the country (Elbourne, 2008).  

The housing market is generally considered as a business opportunity for the construction of a house when in reality there are two special activities. The owners who live in the apartment have administration services with residence. Second, by owning the house, people are looking for speculation. The ownership of a property owned by a company has important economic benefits to avoid paying the rent. Two cost schemes represent these commercial sectors. Rental costs are governed by the free market operation of the administration, while the costs of housing regulate free market activity as housing company (Gallin, 2008). These commercial sectors are distinguished from each other. It is quite conceivable that it is in the market for one and not for one. Leasing allows one to live in an apartment without a house or to renovate the property, but people do not live there (Mayer, Pence & Sherlund, 2009).

Housing is a multidimensional element, which can be considered as a durable consumer good that offers a series of services as a refuge and as an asset for the investments through which leased income or capital gains are obtained. Therefore, the demand for housing can also be classified in demand and investment demand. Income can only explain part of housing prices and there is a crisis related to housing when income growth does not coincide with housing prices. The literature on housing has suggested that housing prices and income should have a long-term equilibrium (Mayer, Pence & Sherlund, 2009).

Credit Structure and Housing Costs

Income, demography, user cost, house price, and availability of substitutes determine demand for housing, in accordance to the neo-classical approach. A straightforward linear regression model for time series data of house costs has been depicted where demand level rate was endogenous (Tsolacos, 2006). A buyer has inclinations over housing administrations where administrations are relative to the housing stock. The change rate between the wage level of the individual part and the accessible piece of the wage was exogenous and decides the relative costs of the structures (Hilber & Vermeulen, 2016). In this unique circumstance, the relative cost of real estate relies upon government contract rates and monetarily invaluable plans. The scholar researched the model’s capacity to clarify value development, from 2007 to 2017

Estimation of the forecasting parameters were done using the OLS regression model Pt = b0 + b1INC_grosst + b2mrt + b3htbt + µt, (t = 1, 2, 3 …44), which assessed the linear relationship of the dependent variable of the study (Pt = average house price) and the independent micro and macroeconomic determinants (INC_grosst = gross household income, mrt = mortgage rate, htbt = government scheme) of the study (Chen and Patel, 2002). It was hypothesized that the independent factors have insignificant impact on house prices in UK. The model was utilized to estimate the coefficients of forecasting of the house prices. The sensibility of this econometric representation for the three bedroom model was in line with hedonic model of economics (Selim, 2009).

The analysis was reassessed with log linear regression model lnPt = b0 + b1lnINC_grosst + b2lnmrt + b3htbt + µt, (t = 1, 2, 3 …44), where the coefficients of the independent factors represented the elasticity of the variance of the independent factors(Drake, 1993). The percentage alteration of the house prices was explained from this model (Zietz, Zietz & Sirmans, 2008).

The average house prices between 2007 and 2017 was calculated as 175168.18 (SD = 35424.59), mean gross household income was 37813.64 (SD = 3842.03), standard mortgage interest rates as 4.31% (SD = 0.10%). The correlation between the house price and the gross household income revealed highly significant correlation (r = 0.92, p < 0.05), whereas mortgage interest rate had statistically insignificant negative correlation (r = -0.1, p =0.27) with house prices of UK. Hence, the coefficient estimates on INC_gross and mr was expected to be positive and negative correspondingly, in the regression equation.

The dummy variable htb was used for sub grouping the results for status of the government schemes. The impact of the scheme on the model was positive in nature (Table 2). Hence, before 2011 the linear model was able to explain 58.5% variance of house prices and interestingly, the coefficient of mortgage interest rate had positive effect on the house prices. Post 2011, the government scheme assisted people in buying homes (Table 3). The model explained mere 41.4% variation of the house prices, and the impact of gross income increased along with usual negative impact of mortgage interest rates.

The constant term or the coefficient of the regression term signified the hypothetical average price of houses in UK in absence of individual income and mortgage rate. But in reality, for non zero values of the independent variables, there was no intrinsic meaning for b0. The error term (“µ” term) represents the residuals, which signifies the difference between the actual and predicted values of house prices. This additional term removed the possibility of error in the calculated house prices.

Government Scheme and Mortgage Rates

Table 1: OLS Regression Model for Mean House Prices

R2 = 0.892, Adj-R2 = 0.884, SE = 12058.091, n = 44, F = 1110.375 (0.000)

The corresponding regression model explained in table 1 was significant in nature (F = 1110.375, p < 0.05) and it explained 89.2% variance of the average house prices. Keeping other factors constant, one unit change in house hold income affected the house price by 4.76 times. For one percent change in mortgage rate the average house price was appeared to be negatively affected.

Table 2: Regression Model for Mean House Prices (Scheme Not Present)

R2 = 0.585, Adj-R2 = 0.522, SE = 8096.26, n = 44, F = 9.177 (0.000)

Table 3: Regression Model for Mean House Prices (Scheme Active)

R2 = 0.414, Adj-R2 = 0.367, SE = 13330.997, n = 44, F = 8.829 (0.001) 

The normality assumption of the regression model was satisfied; the residual P-P plot validated the postulate of the model. The standardized residuals values hovered around the mean line without any significant outlier values. Hence, no anomalous outcome was perceived in the OLS model.  

The Durbin-Watson value was 0.84 (p < 0.05), which suggested that the time series data had positive autocorrelation. As the value of the statistics was less than 1, it was a matter of concern for the OLS regression model (Field, 2009). The probable solution was to assess the dependent variable as  in the regression equation, where was the measure of autocorrelation (Hill et al., 2008).

The OLS model also has multicollinearity problem, as the VIF value for the average gross household income (VIF = 4.64) with tolerance of 0.216. The value of VIF was greater than 3, and hence the high correlation between gross household income and government scheme was considered as the primary reason for multicollinearity.

The heteroscedasticity of the model was evident from the scatter plot of the standardized residuals. The pattern of the plot revealed that the points in the left side in the graph were closely plotted, whereas the points plotted in the right were spaced vertically. Hence, the plot indicated heteroscedasticity, which was a violation of the assumption of the model and the output of the house prices was not predicted properly. 

The OLS regression model was transformed using natural logarithms as, lnPt = b0 + b1lnINC_grosst + b2lnmrt + b3htbt + µt. this transformation was done to assess the percentage change effect of the house prices. The probable non linear relation between the factors or parameters was removed by this method, and required linearity in factors was established. 

R2 = 0.910, Adj-R2 = 0.903, SE = 0.066, n = 44, F = 134.461 (0.000)

The corresponding regression model explained in table 4 was significant in nature (F = 134.461, p < 0.05) and it explained 91.0% variance of the average house prices. The adjusted R-square was 0.90 and was almost equal to the coefficient of determination.

The coefficients of the log-log regression represent elasticity of variables of the model. This indicated that, for 1% change in average gross income, the price of the house will increase by 1.08% (Keeping other factors constant). Similarly, for 1% increase in mortgage rate, the average house price will come down by 0.65%.

The Housing Market as a Business Opportunity

The bivariate correlation between “natural log of disposable household income” and the “natural log of gross household income” was insignificant in nature (r = -0.03, p = 0.416). The uncorrelated nature implied that the variables were not linearly related, and hence independent in nature. Due to the uncorrelated nature, there was no share of variance with each other and hence combined effect on house prices was not possible. Therefore, regression model with any one of the variables was as good as the combined model.

The initial hypothesis of uncorrelated house price and independent factors was rejected based on the evidences from the regression model.  The linear model was less than a viable predictor because of multicollinearity and observed heteroscedasticity. Auto correlation in the OLS model was on the predicted line, as the model was built on a time series data.  Non linear relation between the predictors was removed by the log-log regression model.

Conclusion

In this article, the importance of several economic aspects has been reviewed, that affect the cost components of real estate projects. In addition, the difference in house prices for last ten years, starting from 2007 has been characterized with the strength of the evidences to the support of the real estate markets. The general level of proclamation of this discussion does not allow characterizing the summative approach for house buying guide.

The critical exercise was related to the strong and continuous association between the costs of inflation and apparent financing from one point of view, and the help from government schemes. The feedback from real estate spending to credit enhancement was more rooted in countries with a higher normality of home claims with variable interest rates and more methods of valuation of market-oriented assets for a solid accounting. In the country, the promotion of the financial schemes that generally strengthen the country’s real estate department has been mentioned here, which showed that prudential experts must verify the updates of the real estate estimates. 

References 

Chen, M.C. and Patel, K., 2002. An empirical analysis of determination of housing prices in the Taipei area. pp.563-595.

Dell’Ariccia, G., Igan, D. and Laeven, L., 2008. Credit booms and lending standards: Evidence from the subprime mortgage market (No. 2008-2106). International Monetary Fund.

Drake, L., 1993. Modelling UK house prices using cointegration: an application of the Johansen technique. Applied Economics, 25(9), pp.1225-1228.

Elbourne, A., 2008. The UK housing market and the monetary policy transmission mechanism: An SVAR approach. Journal of Housing Economics, 17(1), pp.65-87.

Field, A., 2009. Discovering statistics using SPSS. Sage publications.

Gallin, J., 2008. The long?run relationship between house prices and rents. Real Estate Economics, 36(4), pp.635-658.

Hilber, C.A. and Vermeulen, W., 2016. The impact of supply constraints on house prices in England. The Economic Journal, 126(591), pp.358-405.

Hill, R.C., Griffiths, W.E., Lim, G.C. and Lim, M.A., 2008. Principles of econometrics (Vol. 5). Hoboken, NJ: Wiley.

Mayer, C., Pence, K. and Sherlund, S.M., 2009. The rise in mortgage defaults. Journal of Economic perspectives, 23(1), pp.27-50.

Selim, H., 2009. Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), pp.2843-2852.

Tsolacos, S., 2006. An assessment of property performance forecasts: consensus versus econometric. Journal of Property Investment & Finance, 24(5), pp.386-399.

Zietz, J., Zietz, E.N. and Sirmans, G.S., 2008. Determinants of house prices: a quantile regression approach. The Journal of Real Estate Finance and Economics, 37(4), pp.317-333.

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