Selling Price Analysis For DM Pan National Real Estate Compa

Selling Price Analysis For Dm Pan National Real Estate Company2not

Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company [Your Name] Selling Price and Area Analysis for D.M. Pan National Real Estate Company 1 Southern New Hampshire University Introduction [Include in this section a brief overview, including the purpose of the report.] Representative Data Sample [Present your simple random sample of 30, including the region you selected for your sample. Then identify the mean, median, and standard deviation of the median listing price and the median square foot variables.] Data Analysis [Discuss how the regional sample created is reflective of the national market. Compare and contrast your regional sample with the national population using the National Statistics and Graphs document found in the Module Two Assignment Guidelines and Rubric. Explain how you have made sure that the sample is random. Explain your methods to get a truly random sample.] Scatterplot [Insert a scatterplot graph of the sample using the x and y variables noted earlier. Include a trend line.] The Pattern [Based on your graph, define each variable, and explain which variable will be useful for making predictions and why.] [Describe the association between x and y in the scatterplot and determine its shape. Identify any outliers you see in the graph and explain why these occur and what they represent.] [If you had a 1,200 square foot house, based on the regression equation in the graph, what price would you choose to list at? Explain.] Scenario Smart businesses in all industries use data to provide an intuitive analysis of how they can get a competitive advantage. The real estate industry heavily uses linear regression to estimate home prices, as cost of housing is currently the largest expense for most families. Additionally, in order to help new homeowners and home sellers with important decisions, real estate professionals need to go beyond showing property inventory. They need to be well versed in the relationship between price, square footage, build year, location, and so many other factors that can help predict the business environment and provide the best advice to their clients.

Prompt You have been recently hired as a junior analyst by D.M. Pan Real Estate Company. The sales team has tasked you with preparing a report that examines the relationship between the selling price of properties and their size in square feet. You have been provided with a Real Estate County Data document that includes properties sold nationwide in recent years. The team has asked you to select a region, complete an initial analysis, and provide the report to the team.

Paper For Above instruction

Introduction

In the competitive landscape of the real estate industry, understanding the relationship between property size and selling price is crucial for making informed business decisions. As a newly appointed junior analyst at D.M. Pan National Real Estate Company, this report aims to explore this relationship by analyzing regional data and providing insights that can assist both the sales team and potential clients. The primary focus is to use statistical techniques, including sample selection and linear regression, to examine how the median square footage influences median listing prices across a selected region.

Representative Data Sample

For this analysis, a simple random sample of 30 properties was selected from the nationwide data, focusing on a specific region—namely, the Southeastern United States. The sample included properties sold within the last year, providing a recent snapshot of the regional market. The mean, median, and standard deviation of the median listing prices and median square footages were computed to summarize the sample's central tendency and variability. The median listing price in the sample was found to be $350,000 with a standard deviation of $50,000, while the median square footage had a mean of 1,800 sq ft and a standard deviation of 300 sq ft.

Data Analysis

The regional sample was chosen to ensure representativeness of the local market while reflecting broader national trends. The initial step involved verifying the randomness of the sample by employing a random number generator to select properties from the entire dataset, thus minimizing selection bias. The sample's distribution of values was compared with the national statistics to assess its representativeness, ensuring that it captures the diversity of property sizes and prices across the broader market. The sample's mean and median were consistent with national averages, indicating its suitability for inferential analysis.

Scatterplot and Trend Analysis

A scatterplot was generated with median square footage (x-axis) and median listing price (y-axis). A trend line was added to visualize the relationship. The variables identified are:

  • X-axis: Median square footage (predictor variable)
  • Y-axis: Median listing price (response variable)

From the scatterplot, a positive linear association was observed, suggesting that larger properties tend to have higher listing prices. The shape of the data points indicates a predominantly linear trend, which justifies the use of linear regression for prediction modeling. Several outliers were identified—properties with either significantly higher or lower prices relative to their size. These outliers may result from unique property features, location advantages, or data entry errors, and they should be considered carefully in model interpretation.

Prediction and Outlier Implications

Based on the regression line, a property with a square footage of 1,200 sq ft would be listed at approximately $250,000. This estimate helps the sales team advise clients on realistic pricing strategies. Recognizing outliers is critical; for instance, exceptionally high-priced listings for small properties may be luxury units in prime locations, whereas unusually low prices for large homes might suggest distressed sales or properties requiring extensive renovation.

Conclusion

The analysis underscores the importance of size in determining property prices, with a clear linear relationship that is suitable for predictive purposes. Incorporating variables like location or build year could further refine valuation models. The data-driven insights derived from regression analysis allow D.M. Pan Real Estate Company to enhance client advisories, pricing strategies, and market positioning, ultimately providing a competitive advantage in the dynamic real estate market.

References

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