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Housing is a vital aspect of any economy. Any factors undermining the housing sector are often treated badly and are often dealt with as soon as they occur (Smith & Suchanek, 1988). However, sometimes it is difficult to deal with the housing problem because it could be caused by factors that are way beyond the control of the government involved. The United States economic difficulties that included the economic bubble and economic recession are some of the hard scenarios that affect housing and have proven to be hard to amend (Hommes & Sonnemans, 2005). The economic challenges have adversely affected the housing sector and after the housing prices peaked in 2006, it has progressively declined since 2007 to 2012. Since the bubble is comprised of many factors, inflation is one of the direct factors that have affected the housing market during this spell of time (Homme & Sonnemans, 2005).

Inflation is often referred to as the scenario where there is excess currency in an economy, making the price of goods to rise (Garber, 2001). Housing is considered a very good option for investment when an economy is undergoing inflation because it becomes a leverage of asset. Many people have had this argument regarding housing market and they even tend to think that there is minor effect on housing as a result of inflation. However, some studies have shown that there is a correlation and that the rate of inflation somehow affects the housing sector. This has been attributed to the fact that the material used for the homes building is imported and inflation adversely affects the cost of importing goods (Garber, 2001). Therefore, it is important to investigate closely whether there is a relationship between the two variables. In this paper, we shall have an objective function that will determine the extent to which the independent variable inflation (x) affects the dependent variable housing market prices (y). The objective function will look into the inflation figures between the year 2007 and 2012, then relate them to the trends of housing inflation for the same period. A scatter plot will then be drawn to determine the regression trends between the two variables. An analysis for this will then be made to determine the extent of effect between the variables and specifically, the extent to which inflation has affected the housing market.

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y = k + fx

Where y = housing market prices

x = inflation

k = constant

0
0
DAYS
:
0
0
HOURS
:
0
0
MINUTES
:
0
0
SECONDS
Discount Code

Methods

For this paper, data that were sourced from the Internet are used as secondary sources of information. The used information was selected for the period between the year 2007 and 2012. The used websites were well examined and corresponded with several other sources to ensure that the correct data were used for the study. The main parameters that were used ere inflation and the market prices as indicated in the objective function. The data were importantly done to ensure that the period used for inflation is the same period used for housing market. This is a key factor that has to be used since data from corresponding years has to be used in order to ensure that they co-relate. If this was not followed, the regression obtained would not be a true reflection of the status on the ground. From the data, a regression chart will be developed and a true reflection of the matter will be obtained. After this, recommendations for the manner in which people should deal with the problems of housing with regard to inflation will be made and any false perceptions on the issue eliminated from the people’s thinking.

Results

The data obtained from the internet sources were as follows:

The house prices for the period between 2007 and 2012. The inflation has been considered in the data.

Year

2007

2008

2009

2010

2011

2012

Price in USD

287,500

250,000

200,000

193,750

181,250

175,000

During the same period, the inflation rate was as follows:

Year

2007

2008

2009

2010

2011

2012

Inflation rate (%)

2.85

3.85

-0.34

1.64

3.16

2.07

If to include the data in one table, the following will be obtained:

Year

2007

2008

2009

2010

2011

2012

Inflation rate (%)

2.85

3.85

-0.34

1.64

3.16

2.07

Price in USD

287,500

250,000

200,000

193,750

181,250

175,000

 

Multiple R

0.389705334

R Square

0.151870248

Adjusted R Square

-0.060162191

Standard Error

1.516655698

Observations

6

 

R Square

0.151870248

 

 

Coefficients

Standard Error

t Stat

P-value

Intercept

-0.563802632

3.329650611

-0.169327866

0.873757022

X Variable 1

1.29032E-05

1.52462E-05

0.846321155

0.445034322

           

The regression model is Y=-0.563802632+0.0000129x

Discussion

However, the price of houses has continually declined since 2007, as shown by the data above. The inflation rate went to as low as -0.34% at a period when the cost of the houses was 200,000 USD. The prices continued to fall in spite of the increasing in the inflation rate from -0.34% to 1.64% and then to 3.16% between 2009 and 2011. The respective price for the same period was USD 200,000, 193,000 and 181,250. This shows that there is minor correlation between inflation and housing market and that there are other factors that affect the housing market more than the inflation rates. It can, therefore, be concluded that the inflation affects the housing market in a minor way. This means that the people concerned with the department should look for the other factors that lead to the changing prices of housing because, evidently, inflation has  minor, almost insignificant influence.

Statistical discussion through the regression analysis is made below:

R square is used to measure goodness of fit. It describes how well the given data fit a regression model. For this particular case the data fit the model by 15%. This shows that 15% of housing prices are explained by inflation rates over the years.

The p value is used to test the significance of the slope of the fitted model. In this particular case the p value is 0.445034322 which is greater than 0.05 tested at 95% level of significance.  This implies that the slope of the model is equal to zero. It is therefore statistically proven through the regression analysis that there is no relationship between inflation and the changes in the housing market prices.

The perception that housing is a good investment field in the economies, but it hits by inflation, is correct, because still there is minor effect on the housing.

Recommendations

The people who are involved in the housing department should carry out further research to determine the actual factors that lead to changes in the housing sector. Throughout the economic bubble, the price of houses has continually fallen, meaning that there are other factors that affect the sector. These factors should be looked into and methods to mitigate them should be devised.

The perception that inflation negatively affects all the sectors is misleading and does not reflect the actual state of affairs. Players in the housing sector should determine the factors that directly affect them and not get concerned with the inflation rates, because they have a minimal effect on their sector. 

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