A multiple linear regression analysis was carried out using data for 60

residential housing sales in a given area in the east coast of the USA in 2019.

Y = Sales Price (in thousands of dollars).

X₁ = Living Area (in square feet)

X₂ = 1 if the house is on the waterfront

= 0 if the house is not on the waterfront

X₃ = 1 if the house has a garage

= 0if the house does not have a garage

The results of a regression analysis used to predict housing sales are as follows:

1. Prediction equation: Y = -28 + 0.088X₁ + 280X₂ + 35X₃

2. The F test is statistically significant.

3. The three T tests are statistically significant.

4. The value of SSE = 35,000.

5. The value of SSR = 315,000.

6. The residual plots exhibited a random pattern with no outliers present.

A waterfront property (not one of the 60 data points in the sample) without a garage and with 3500 square feet of living area sold for $636,000. A buyer would overpay if the residual for the sale is an outlier. Based on the results of this regression analysis, what is the residual and did the buyer overpay for this property?