1. The present document analyzes the data of a real estate company that has gathered information from 100 homes located in the research area of California. Assessing the gathered data pricing model for the real estate is proposed which can be followed by the real estate officials to make decisions regarding purchasing or selling the property.
On the basis of gathered data, the dependent variable is price, and the independent variable is listing price. The size of the property affects its prices due to which it can be stated as an independent variable. Since the price change with the size, therefore, it shows that it is the dependent variable.
Looking at the model summary, it can be identified that the relationship between the variables will be stronger because the R squared value of 0.802 that is closer to 1, which indicates a strong relationship between the variables (Brooks & Tsolacos, 2011). The price range of the homes would greatly influence overall size in California.
Y(Listing Price) = -7.123 + 0.896 (Size/Square feet)
Looking at the trend line, it can be noted that the all the variables are closer to the linear line. It shows that the model of price and size of the property for the real estate in California are well fitted in the regression line (Brooks & Tsolacos, 2011). The figure 1 shows that the variables are closely linked which shows that the relationship stronger relation between the variables. There is lower variance among the variables regarding price and size that can be observed from the trend lines in the scattered plot. However, there is an upward trend regarding price and size of the homes. The greater size of the home is there would be a relative increase in the price of the home. This indicates that the relationship between the variables is stronger. However, it can be noted that the demand for 1,000 to 4,000 square foot at the price between $100,000 to $1,000,000 are more preferred. These price ranges are more preferred by the clients and as a result the price range would be different depending on the other associated factors such as many bedrooms, locations, and facilities. Following these price range, the real estate can make decisions in the investing of the property based on the value gained from the model. The other main factor that can influence the choice of the customers and demand for property is the number of bedrooms that needs to be determined. The price ranges of the homes that are sold in the price range $0 to $199,999 have greater demand than the homes that were sold between $200,000-$399,999.
On the basis of gained findings, it can be noted that the real estate individuals should keep in view the price and size of the homes. All of the homes show that the increase in the size leads to the increase in the price of the homes in California. From the results obtained the proposed model explains that the real estate that there would be an increase of one square foot of the homes increase the price of the home by $89.6.
Reference
Brooks, C., & Tsolacos, S. (2011). Real Estate Modelling and Forecasting. Cambridge: Cambridge University Press.
