Assignment Real Estate Analysis Part I

Introduction [Include in this section a brief overview, including the purpose of the report.]This report was created to illustrate the listing prices and square footage for homes sold in a randomized selection of counties in the New England area. A random selection of 30 counties from the new England area were selected from national real estate county data. This random selection will be utilized to display the median listing price and the median square footage for the houses. In this report the response variable, the y axis, in the scatter plot chart represents the median listing price, while the predictor variable, the x axis, represents the median square footage of the listing. Representative Data Sample Table 1 Mean Median Standard Deviation New England Region Listing Price $409,537 $370,029 $157,309 Square Feet 1914 1922 272 Source: Realtor.com [Present your simple random sample of 30, including the region you selected for your sample. Then identify the mean, median, and standard deviation of the median listing price and the median square foot variables.]As stated in the introduction the area chosen for this analysis is the New England area. The data set consists on a randomized selection of 30 counties in the New England area. The analysis showed that in the New England area the mean listing price is $409,537 and the mean square footage is 1,914 square feet. The median listing price is $370,029 and the median square footage is 1,922 square feet. The standard deviation on the listing price is $157,309 and the square footage is 272 square feet. Data Analysis The regional sample that I created compares the median and mean listing prices and the median and mean square footage of houses that were listed for sale in various counties in New England area. The median is the middle value of the listing prices or square footage of 30 houses that were randomly selected for the survey. To find the median, I listed the numbers in numerical order from smallest to largest and then find the middle number(s). The sample of listings chosen for this survey as an even number “30” because of this, the middle two values were added together and then divided by two to find the median values. While the mean is the average value of the listing price or square footage of those same 30 houses. To find the mean value, I add all the numbers and then divide by the number of numbers. The sample was randomly obtained by adding a new column within the National Real Estate County Data spreadsheet and naming it “random number”, then in the first cell underneath the heading row I typed “= RAND()”, press “Enter,” and a random number will appear in the cell. I then copy/pasted the first cell into the other cells in the “random number” column. Once each of the rows contain a random number, sort the values by the “random number” column. I then chose the first 30 lines in the regional data to use in my survey. Table 1 Mean Median Standard Deviation New England Region Listing Price Square Feet $409,537 1914 $370,029 1922 $157,309 272 Source: Realtor.com Table 2 National Listing Price Square Feet Mean Median Standard Deviation $288,407 $256,936 $163,986 1944 1901 367 Source: Realtor.com As shown in the tables one and two, both the median and the mean listing prices were higher in the New England region than in the national market. The houses in the New England tended to have less square footage than the one’s listed for a lower listing price in the national market. The standard deviation for the listing prices is within $6,500 for both New England and the National markets. [Discuss how the regional sample created is reflective of the national market. Compare and contrast your regional sample with the national population using the National Statistics and Graphs document found in the Module Two Assignment Guidelines and Rubric. Explain how you have made sure that the sample is random. Explain your methods to get a truly random sample.] ScatterplotScatterplot Table 3 New England $900,000 $800,000 Median Listing price $700,000 $600,000 $500,000 f(x) = − 10492.16 x + 516550.96 R² = 0.33 $400,000 $300,000 $200,000 $100,000 $0 0 5 10 15 20 25 30 35 40 45 50 Square Footage Source: Realtor.com [Insert a scatterplot graph of the sample using the x and y variables noted earlier. Include a trend line and the regression equation.] [Insert a scatterplot graph of the sample using the x and y variables noted earlier. Include a trend line.] The Pattern The scatterplot (Table 3) of New England housing prices shows that there is a weak positive correlation between the response variable, the y axis, in the scatter plot chart representing the median listing price and the predictor variable, the x axis, representing the median square footage of the listing. There is a weak, positive linear association between the two variables with one outlier. A regression curve is a curve added to a scatter plot that shows the relationship between two variables. A linear regression curve is also called a regression line or a trend line.(Berrier et al, 2020) The trendline line is displayed in a rising pattern from the left to right which is an indication of a positive association between median housing square footage and its median listing price. A positive correlation between the two variables will be indicated by the variables increasing in relation to each other. The plots in the scatterplot are widely spaced from the trendline which is an indicator that the relationship between housing price and square footage is weak. The trendline in the scatterplot above demonstrates that the median listing price will increase as the square footage increases. There is one outlier in the scatterplot, it lists a 1,263 square foot house for $791,997, this listing does not follow the trendline in the scatterplot. This outlier may be explained by other factors which were not sampled in the survey, such as the house sitting in a more desirable piece of land and is not indicative of a failed survey. Outliers can affect the mean value in the descriptive statistics. This outlier can be removed from the scatterplot but an explanation as to the reason for its removal would have to be provided when presenting the survey. In this scatterplot the house price is the response variable, while the house size is the predictor variable. These two variables are correlated. Correlation indicates that a variable performs with a predictable pattern with respect to fluctuations in the other variable. After analyzing the scatterplot, I would predict that a larger house would sell for more money than a small house. I calculated the price of a 1,200 square foot house by utilizing the regression equation that was generated by the New England region scatterplot. The equation is y=189.33x+47,162. In the equation I substituted the x variable with 1,200, the equation became y=189.33(1200)+47,162=274,358. If a 1,200 square foot house was listed for sale in New England, I would predict that its price would be $274,358.