Selling Price and Area Analysis for a Selling Price and Area Analysis

Selling Price and Area Analysis for D.M. Pan National Real

Selling Price and Area Analysis

Introduction

In this report the analyst who recently joined D.M. Pan Real Estate Company will present his findings for the sales team that examined the association between property listing prices and property sizes when considered in terms of square feet.  The researcher relied on a real estate datasheet that reflects properties from the Mid-Atlantic region, randomly selecting 30 out of the 47 listed properties that have come to market in recent years in. The purpose of the report is therefore to provide an analytical approach that the business can use to gain a competitive advantage when it comes to pricing properties that it is selling around the country. Hence the main aim is to formulate a linear regression equation that will relate the two variables of size and price such that it will be within national trends.   Once the sales department gets this report it will be in a position to make informed decisions about the market in focus and how to price properties for the best return on investments.

Representative Data Sample

The data was collected from all the Mid-Atlantic region with only 30 samples randomly selected for the purpose. All these properties were in the state of Virginia with the counties of Albemarle,  Alexandria City,  Arlington,  Augusta,  Bedford,  Campbell,  Charlottesville City,  Chesapeake City,  Chesterfield,  Danville City,  Fairfax,  Fauquier,  Franklin,  Frederick,  Hampton City,  Hanover,  Henrico,  Henry,  James City,  Loudoun,  Lynchburg City,  Montgomery,  Newport News City,  Norfolk City,  Pittsylvania,  Portsmouth City,  Prince William,  Richmond City,  Roanoke And Roanoke City all represented in the sample.

The mean listing price was $310,300 and the mean square feet $1,618.

The median listing price was $300,000 and the median square feet $1,651.

The standard deviation for the listing price was $54,467 and the standard deviation for square feet was $267.

Data Analysis

Simple random sampling is the process of collecting elements of a population for a study in an unsystematic manner such that each element has an equal chance of being part of the study sample (Alim & Shukla, 2020; Lone & Tailor, 2017). Analysts often do this due to the simplicity of the study they are conducting which may not require more complex forms of sampling that reflect different aspects of the population, hence the term ‘simple random sampling’ (West, 2016).

The table above provides the summary descriptive statistics of the national population while the table below provides it for the Mid-Atlantic region

	square feet	listing price
Mean	1,618	310,300
Median	1,651	300,000
StDev	267	54,467

From the findings it is evident that mid-Atlantic region has generally lower listing prices for properties and lower sizes for its houses compared to the national scenario in which all the descriptive metrics are higher. This is attributable to other factors which may not fall within the purview of this study such as location, population, economic conditions, etc.

Below is the comparative national listing which displays a different shape, which shows that the regional sample is not exactly representative of the national population.

The graph below provides the regional sample of listing price

The comparative table below represents the national statistics as required in the instructions

Hence it is possible to conclude that the regional study was not a representative sample which mimicking national statistics.

In the following section the report will demonstrate how square foot property size and listing price are related and how this can help realtors determine the price of properties that they want to sell or buyers determine the price of houses they want to buy.

Scatterplot

The Pattern

The variables applied in this graph include square feet and listing price. Square feet are the size of the listed property and listing price is the amount a buyer will pay to acquire the property.  The square feet or size of the listed property is the best variable in making predictions about price

From the scatterplot it is evident that the bigger the property the higher the listing price. However, a few outliers exist in extreme ends of the trend-line. A property with a space of 1476 square foot has a listing price of $235,700 which seems too low for the region considering the trends. A second property of 1893 square feet is listed as worth $431,000 which is too high for the size.

The regression equation is y = 189.84x, where y is listing price and x is square foot. A 1800 square foot house would therefore be listed at (189.84x 1800) = $341,712.

Hence for properties in the Mid-Atlantic or Virginia state to be exact, the company is likely to be more competitive if it used this regression equation to price properties that it is selling. However, this equation does not necessarily reflect the national situation or the situation in all the regions because all of these face different market dynamics that influence their listing prices such as population, location, economic conditions, construction costs, etc. Some or all of these factors come into play when dealing with each unique market, which ultimately determines the kind of regression equation applicable for that market.

References

Alim, A., & Shukla, D. (2020). Sampling-based estimation method for parameter estimation in big data business era. Journal of Advances in Management Research.

Lone, H. A., & Tailor, R. (2017). Estimation of population variance in simple random sampling. Journal of Statistics and Management Systems, 20(1), 17-38.

West, P. W. (2016). Simple random sampling of individual items in the absence of a sampling frame that lists the individuals. New Zealand Journal of Forestry Science, 46(1), 1-7.