RELATIONSHIP OF VALUE OF HOME PRICE OVER A PERIOD OF TIME COURSEWORK:
When one may be considering to, buy or purchase a home, he or, she may be concerned or interested in knowing whether home prices may be poised to fall or rise. We may see that no one may want to purchase or buy a home which later might plummet in value. This may lead one to insist on buying a house or a home before prices may go upward. It may not be easy to predict what might happen with real estate prices in a city, country or state over a period of time. Therefore, from this one may come to the conclusion that the vitality of an area and economic health may drive the prices and demand for homes in that area. Also, when there may be an increase in jobs, which may pay well, this may in the end increase the demand for housing thus; an increase in price.
When one may be deciding on whether to buy a home or not, he or she may have to consider some of the following how long they might be in that particular area for instance a period of five to ten years or more. Another factor to consider may be whether the current home prices in one’s local area may offer good value. Also, other factors may include the state of the job market, the number of home listings that may be for sale in the area and the level of real estate prices as compared to rent. This may help one to know whether the current home prices may be relatively high compared to the rental cost and he or, she may be able to make a choice on whether to rent or purchase a home. In addition to this, buying a home may be a long-term financial commitment whereby, one may be required to take a 15-30 years mortgage so as they may finance the purchase depending on their income. Normal value may be defined as the price that may be changed by a firm in its home market value. Trade may not be considered ordinary over an extended period of time (Hoekman, Mattoo, and English, 200-203).
Data collection may be relatively straight forward and may rely on secondary information. Data collection may be a critical step in problem solving whereby, without good data one may be in the end guessing solutions to problems. The gathering and collecting of data may help in some of the following ways data may help one to clarify a problem or to know what might be happening in a situation, it may help one to separate what they may think to what might actually happen, it may help one to understand how, what they might measure might relate to the problem they might want to solve or, come up with solutions and it may provide a baseline on how to measure improvement from a previous perspective to a new one. Data collection or gathering may be done in some of the following ways these may be questionnaires, through observation, interviews and reading from other primary sources. One may collect data after identifying a problem and may want to understand more about it and find or come up with solutions to it (Joiner associates, 4-7).
To help in better understanding of the relationship of home price value over a period of time, the following data may have been collected over a ten year period from 2000 to 2010. In the year 2000 the price of purchasing or buying a home may have ranged between 30000 and 40000.Over the years, depending on some of the following factors economic influences, personal influences, neighborhood influences and government set policies, the prices may have risen, stayed stagnant or dropped.
Scatter plots may be similar to line plots but, in a scatter plot one may not connect or join the points. One may be able to display the points without any interpolation or they may add a regression line that might show the relationship between two columns. One may produce a scatter plot with a regression line by may be using regression analysis. A scatter plot may be said to be a graph whereby, each of the plotted point may represent an observed pair of values for the independent and dependent variables. The value of the independent variable x, which in this case may be the years, may be plotted with respect to the horizontal axis. The value of the dependent variable y, which in this case may be the amount, may be plotted with respect to the vertical axis. The form of relationship that may be represented by the scatter plot may be curvilinear rather than linear. Based on the data that may be collected above, one may come up with a scatter plot as shown in the figure 1.1 below.
The purpose or use of the least squares analysis that may be how the regression equation may be used may influence or have an impact on the manner in which the model may be constructed. The potential uses of the regression equation may include some of the following providing a good description of the behavior of the response variable, prediction of future responses and estimation, estimation of mean responses, extrapolation or, prediction of responses that may be outside the range of the data, estimation of parameters, control of the process by varying levels of input and developing realistic models of the process.
Regression equations with fewer variables may have the appeal of simplicity. In addition to this, it may also have an economic advantage in terms of may be obtaining the necessary information to the use of the equations. Also, there may be a theoretical advantage of eliminating irrelevant variables and, even variables that may contain predictive information about the response variable. Least square regression results may reflect the correlation structure of the data that may be analyzed. The term linear may indicate that the regression equation may be a linear equation. A linear equation may describe how independent variables may combine to define the single dependent variable. When the regression equation may have two independent variables, it may define a plane. Linear regression may be a technique that may use data to produce an equation for a straight line (Gravetter and Wallnau, 570-573).
Using the data that may be collected above, one may come up with the regression equation that y= a+bx, whereby y may be the dependant variable, b the slope of the gradient and x the independent variable.
The analysis of variance methodology may be concerned with the investigation of factors that may contribute significant effects through suitable choice of experiments. It may be a technique through which variations associated with different factors or defined sources may be estimated or isolated. The procedure may involve the division of the total observed variation in the data into individual components attributable to various factors and, those because of random or, chance fluctuation and performing tests of significance to determine which factors may influence the experiment (Hardeo and Ageel, 1-3).
The best way that may be used to truly represent ones set of data by using the regression equation, may be through the determination of the correlation coefficient as compared to other methods. The correlation coefficient may be represented by the letter r. It may measure the direction and strength of a linear relationship between two variables. The formulae of the correlation coefficient may be given as r= n∑xy-(∑x) (∑y) divide by √x (∑x2)-(∑x) 2√y (∑y2)-(∑y) 2. From this, after computing one may either get a positive 1 or a negative 1. The positive value may indicate a relationship between the y and x variables which may show that as the values of x increase so do the values of y. In the case whereby, one may get a negative value, it may indicate a relationship between x and y in the sense that when x may increase, y may decrease. In addition to this a value near to zero may mean that there might be a random, non-linear relationship between the two variables. A perfect correlation of a ± 1 may occur only when the data points may all lie exactly on a straight line. A correlation that may be greater than 0.8 may be said to be strong correlation whereas, a correlation that might be less than 0.5 may be said to be a weak correlation.
In conclusion I believe that when one might collect or gather data from different sources, he or, she might be able to get ways or solution of dealing with problems that may be at hand. The regression equation, the analysis of variance and the correlation coefficient may help one to determine the relationship of two variables.
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