3-3 Assignment - Real Estate Analysis part 2

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Housing Price Prediction Model for D.M. Pan Real Estate Company Colleen Del Valle Southern New Hampshire University

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 2 Module Two Notes The listing price random sample mean is $213,636, the median is $231,127, and the standard deviation is $153,707. The mean for square feet is 3,078, with the median being 2,984, following up with the standard deviation of 1,127. The difference in mean price between the random sample and the National Summary Statistics is $128,729, the median difference is $86,873, and the standard deviation is $27,793. The difference of square footage between the Pacific Region and National mean is 967, the median comes in at 1,103, and lastly the standard deviation is 206. The regional sample does not seem to be reflective of the national model just because of the gap differences in listing prices alone. The square footage is a little closer but still has a noticeable difference. How I conducted the simple random sample was by copying and pasting the data specific to the Pacific region into a new workbook, from there I generated random numbers in the ‘G’ column with the ‘RAND’ function. After getting my random numbers, I highlighted all the columns with data, clicked the ‘Data’ tab at the top of the document, then clicked ‘Sort’ and had it sort the information randomly from smallest to largest. After it was all randomly sorted, I took the first 30 samples and copied them onto a separate worksheet. To get the mean, median, and standard deviation of the random listing price I used the ‘average’, ‘median’, and stdev.s’ functions on the spreadsheet. Below I have added a side by side of the Pacific Region and the National Summary Statistics. Listing Price (Dep. Var) sample national mean price $213,636 $342,365 median price $318,000

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 3 $231,127 std dev price $153,707 $125,914 Sq Ft (Indep. Var.) sample national mean sqft 3,078 2,111 median sqft 2,984 1,881 std dev sqft 1127 921 Regression Equation

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Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 4 The regression equation is y = -134.65x + 628035. Determine r R is the linear correlation coefficient and the stronger it is to ‘1’ or ‘-1’ the stronger the relationship is. Based off my scatterplot r = -0.98727, it is a strong negative due to the listing prices, as they get lower the square footage gets higher which causes the negative. The strong correlation is from the data being closest to ‘-1’, as you see on the scatterplot it is a very defined downward slope with all points near or on the trendline. Examine the Slope and Intercepts The slope is -134.65 and the intercept is 628035. To me the slope does not make sense as you would think with the increase in square footage the price would increase with it giving it a positive correlation instead of negative. To determine the value of land only, you have to find the value of y when x is 0. Based off the above information, the price for just land would be $628,035. R -squared Coefficient What r- squared means is coefficient of determination, which is the correlation between the listing price (y) and the price per square foot (x) that measures the information on your model to predict the outcomes. For the scatterplot above, r-squared = 0.974701, which means it is a strong correlation. Conclusions

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 5 When comparing the maximum square footage from the national data to the sample data the national sq ft is 1,406 more than the sample data, which is a sizeable difference to most families. Based off my sample data, the price would increase by $-13,465 for every 100 sq ft. You could use slope to help identify price changes, but it will all depend on what state and city you are attempting to sell/buy a house in. In the less developed cities, you can purchase at house with lots of sq ft for cheap, but as soon as you go to the more developed cities your listing price will go up compared to the sq ft. The range this graph would benefit from would be 1,800 – 3,000 sq ft.

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