Estimation of Property Price and Budget: Analysis

Answer

Estimation of Property Price and Budget: Analysis

After a comprehensive assessment of the real estate data for the 45 residential real estate properties, it is obvious that the key problem revolves around the pricing, which is seen to have a negative effect on the market attractiveness (Castelli, et al., 2020). This comment is based on the linear regression equation which indicates that the houses stay in the market for at least 2.34 years before they are sold. This duration presents a major inconvenience considering the fact that a year increase in the age of property results in $2,345.33 price decline. When placed into perspective, this problem is caused by the huge variations in the number of bedrooms and the lot sizes. It suffices to highlight that some houses have up to 6 bedrooms, yet this variable has an incremental impact on the house prices. The same case applies to lit sizes, which vary from 0.1 to 40 acres with a Mean 2.31 (SD = 6.78). Given the nature of this issue, a relevant solution would involve reducing the variations in the highlighted variables.

            On that note, the proposed alternative would be a reduction of the lot sizes by a third, and a reduction of the variation in number of bedrooms to a constant figure (3). These moves will result in a relative decline in the property prices as well as their ages prior to purchase (Armstrong, 2019; Breuer & Steininger, 2020). This alternative is affirmed by the fact that the variable size is moderately skewed to the right with a Mean of 1900.71 (SD = 683.04): noteworthy is the significant difference between this Mean and the Center of the distribution, which is a Median of 1858. This challenge will be resolved by a reduction of the number of bedrooms to a minimum constant of 3. The effects on the confidence levels cannot be clearly stated since the correlation for the same is skewed (Bakar, Hassan, Zakaria, & Halim, 2019). Not to mention that variations are at large with respect to the confidence levels of each variable.

            There are potential drawbacks that are likely to emerge when the proposed alternative is implemented. Lot reduction might trigger the emergence of a new variable, unutilized land, which is bound to affect the confidence levels as well as the prices (Aziz, Anwar, & Dawood, 2020). The new variable will have to be acknowledged in another analysis to confirm its impact on the rest of the variables (Barlindhaug & Nordahl, 2017). Also, this move is likely to affect the general attractiveness of the properties in the market. Meanwhile, the reduction of the number of bedrooms might also reduce the confidence levels since some purchases are, arguably, made due to this variable. The fact that little is known of the number of purchases are made with respect to the number of bedrooms makes it impossible to determine the likelihood of such a risk, yet it ought to be acknowledged.

            Apparently, number of bedrooms and lot allocation are the main variables causing pricing problems in this real estate setting. The huge variations in both variables have a direct impact on price increase, which in turn reflects in the amount of time buildings spend on site before they are purchased. Such a situation demands a reduction of each variable to median figures to ensure that each property enhances the confidence levels of the buyers. Of course, such a move demands caution since the transformations might as well affect the properties’ attractiveness.

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