# Commercial Real Estate Harvard Case Solution & Analysis

## Commercial Real Estate Case Study Solution

### Null Hypothesis 3:

Null hypothesis states that there is not a statistically significant difference on land area in number of square feet by number of bathrooms in a house on average below than 10 and more than 10.

### Alternate Hypothesis 3:

Alternate hypothesis states that there is a statistically significant difference on land area in number of square feet by number of bathrooms in a house on average below than 10 and more than 10.

### Interpretation:

Appendix 8 shows that, at the 0.05 level of significance, it has been found that the P- value is 0.02 which is lower than the 5 percent level of significance which provides the significantly strong evidences to reject null hypothesis ultimately do not reject alternate hypothesis. It means that there is a statistically significant difference on land area in number of square feet by number of bathrooms in a house on average below than 10 and more than 10.

At – 2.5162 (22) = P - Value (0.02 < 0.05), provides a conclusion that there is a statistically significant difference on land area in number of square feet by number of bathrooms in a house on average below than 10 and more than 10.

### Null Hypothesis 4:

Null hypothesis states that there is not a statistically significant difference on dollar selling price of a home by different school quality rating.

### Alternate Hypothesis 4:

Alternate hypothesis states that there is a statistically significant difference on dollar selling price of a home by different school quality rating.

### Interpretation:

Appendix 9 shows that, at the 0.05 level of significance, it has been found that the P- value is less than 0.00001 which is also lower than the 5 percent level of significance which provides the significantly strong evidences to reject null hypothesis ultimately do not reject alternate hypothesis. It means that there is a statistically significant difference on dollar selling price of a home by different school quality rating.

At 38.88 (20,910) = P - Value < 0.00001, provides a conclusion that there is a statistically significant difference on dollar selling price of a home by different school quality rating.

### Null Hypothesis 5:

Null hypothesis states that the land area in number of square feet predict the dollar selling price of a home.

### Alternate Hypothesis 5:

Alternate hypothesis states that the land area in number of square feet predict the dollar selling price of a home.

### Interpretation:

Appendix 10 shows that, at the 0.05 level of significance, it has been found that the P- value is less than 0.01 which is also lower than the 5 percent level of significance which provides the significantly strong evidences to reject null hypothesis ultimately do not reject alternate hypothesis. It means that the land area in number of square feet predict the dollar selling price of a home.

At 65.331 (20,910) = P - Value < 0.01, provides a conclusion that the land area in number of square feet predict the dollar selling price of a home.

### Null Hypothesis 6:

Null hypothesis states that the selling price of a particular house with  complete given data set.

### Interpretation:

Appendix 12 shows that, Arima model with values (p, d, and q) which is (1, 0, and 1) refers to AR model, degree of difference, and MA order. We may change these values for finding of most optimized and efficient result on given data. The given model have outcome of fitted coefficient and standard error for each of coefficient. It shows the AIC and BIC values for this model is related to the selling price of house in this data set is 338195.1 and 338190.8 respectively. This model also represent the error rate of this model in term of mean error, residual’s mean square error and mean average error.

Appendix 13 shows that, the acf and pacf plots of given model with respect to the selling price of house in real-estate data. This represents the similar impact of the lagged value in both plots.

### Appendix 13

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