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 Data Spreadsheet spreadsheet 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. Note: In the report you prepare for the sales team, the response variable (y) should be the listing price and the predictor variable (x) should be the square feet. Report the mean, median, and standard deviation of the listing price and the square foot variables. Analyze Your Sample Discuss how the regional sample created is or is not reflective of the national market. Compare and contrast your sample with the population using the National Summary Statistics and Graphs Real Estate Data PDF document. Explain how you have made sure that the sample is random. Explain your methods to get a truly random sample. Generate Scatterplot Create a scatterplot of the x and y variables noted above and include a trend line and the regression equation Observe patterns Answer the following questions based on the scatterplot: Define x and y. Which variable is useful for making predictions? Is there an association between x and y? Describe the association you see in the scatter plot. What do you see as the shape (linear or nonlinear)? If you had a 1,800 square foot house, based on the regression equation in the graph, what price would you choose to list at? Do you see any potential outliers in the scatterplot? Why do you think the outliers appeared in the scatterplot you generated? What do they represent?
nalyzing the Relationship Between Property Size and Selling Price in the Real Estate Market
This report delves into an analysis of the relationship between property size, measured in square feet, and selling prices in the real estate market. The objective is to provide valuable insights to D.M. Pan Real Estate Company’s sales team regarding the potential influence of property size on listing prices. To accomplish this, a regional sample of property sales data is analyzed, and a scatterplot with a regression equation is generated. The mean, median, and standard deviation of listing prices and square footage are reported, along with an assessment of how the regional sample reflects the national market. Additionally, the report discusses the methods employed to ensure the randomness of the sample and identifies potential outliers in the scatterplot.
In today’s highly competitive real estate industry, data-driven decision-making is paramount. Smart businesses, regardless of their industry, recognize the power of data in gaining a competitive edge. The real estate sector is no exception, and it relies extensively on data analysis to estimate property values accurately, provide valuable insights to clients, and make informed decisions. As housing costs continue to be a significant expenditure for most households, it becomes imperative for real estate professionals to be well-versed in statistical techniques that can help predict property prices effectively.
This report focuses on the relationship between property size, measured in square feet (x), and selling prices (y) within the real estate market. Specifically, it aims to provide insights into how property size influences listing prices, thus enabling the sales team at D.M. Pan Real Estate Company to better understand this dynamic.