Easton Real Estate

Step1. Descriptive statistics.

In this part, we did some basic summary of housing price and the effect from size, area, number of rooms and agency.

First of all, we analyzed the relationship between price and size of the house, excluding the effect from Area.

From the graph above, we can see that there is a significant positive linear relation between the price and the size of the house. Moreover, for houses in Dallas houses tend to be priced higher than the other two areas. But our two samples are priced reasonably according to the graph since there isn’t a significant deviation from the fit line.

House 125 Analysis (Fort Worth)

From the regression equation, we can write

Price (estimated) = a + b*Surface Area

Price of house P = a + b* Surface Area + ε where, a & b are the coefficients and ε is residual. ε ~ N (0, σ)

On detailed analysis, it can be found, that with C.I. of 95%, the house price in Fort Worth area should be in the range of [72647.78, 95979.94].

 House 228 Analysis (Elsewhere)

Price (estimated) = a + b*Surface Area

Price of house P = a + b* Surface Area + ε where, a & b are the coefficients and ε is residual. ε ~ N (0, σ)

On detailed analysis, it can be found, that with C.I. of 95%, the house price in Arlington area should be in the range of [80270.82, 102954.34]

Conclusion

As the price of both the houses, located in Fort Worth and Elsewhere zone lies within this price range, and we cannot say that it is underpriced with a C.I. of 95%.

Price per Square Feet vs Size:

We changed the variable “price” to “price per square feet (PSF)” to study further.

In this graph, the result is basically the same. The fit line change demonstrates that there is a slightly negative relation between size and price per square feet, which is also logical. Moreover, it seems that the two samples are still reasonably priced although they are slightly lower than the average level.

Conclusion:

Since both the...