Part 2
Do these
Read 3-5 posts AND read the corresponding article
Download and read the Five Obstacles faced in Linear Regression Download Five Obstacles faced in Linear Regressionarticle for use in this part of the assignment.
Select one of your classmates’ posts for your comments (it cannot be the same article that you selected) and explain why you think the Linear Regression did or did not support the article’s findings using the following criterion:
Variables that were included or excluded
Validity of the technique that was used (i.e., was Linear Regression an appropriate analytical tool for what the article was trying to claim)
Discuss at least three of the points from the “Five Obstacles” document as they relate to the article you are reading for this post.
Do not do these
Do not comment about the author’s work on their post – comment about the article and the characteristics noted above
Do not repeat what the article is about or the business, product, or service being addressed.
Link to classmate article https://core.ac.uk/download/pdf/303864121.pdf
Grading Rubric attached. Please keep discussion between 250-275 words. Discussion cannot go over 275 words.
Introduction
Since there are many variables that affect the real estate market, pricing is a difficult but important decision for both buyers and sellers. Dr. Ceyhun Ozgur, Zachariah Hughes, Grace Rogers, and Sufia Parveen constructed a multiple regression model to forecast housing prices for individual homes sold in Northwestern Indiana in 2014 in an effort to clarify this intricacy. Their study looks for important factors that influence housing prices and their effects by using data from realtor.com. With its practical insights for improving pricing tactics and supporting demand forecasting, this predictive model has important ramifications for companies operating in the real estate industry.
Regression Model
A variety of independent variables, such as square footage, the number of bathrooms, the presence of a finished basement, the presence of a brick front, and the type of home (Tudor, Colonial, or Contemporary), are incorporated into the regression model that Dr. Ozgur and his team built. The price of individual homes can be predicted using these variables. The researchers hope to provide a quantitative understanding of the ways in which different factors influence pricing dynamics in the real estate market by capturing the relationships between these predictors and housing prices using the framework of multiple linear regression analysis.
Goodness of Fit
Metrics like the adjusted R-squared value and the F-test(ANOVA) are used to evaluate the regression model’s goodness of fit . The model’s ability to account for pricing variability is highlighted by the adjusted R-squared value of 0.0745, which shows that the regression model explains approximately 7.45% of the variance in housing prices. The significance of the explanatory variables in predicting housing prices is further indicated by the F-test results, which highlight the importance of elements like Homeowner Association (HOA) fees in influencing housing prices.
Benefits
Businesses in the real estate industry can benefit from the developed regression model in a number of ways. Businesses can adjust their pricing strategies to match the dynamics of the market and the preferences of their customers by taking into account variables like square footage, the number of bathrooms, and the presence of amenities. Improved market competitiveness and higher sales potential are possible outcomes of this optimization. Buyers can also use the model’s insights to evaluate the value proposition of various properties and determine whether housing prices are reasonable in order to make well-informed decisions.
Lessons Learned
The study emphasizes the value of using regression analysis in real estate pricing to identify important trends and insights from a professional perspective. In order to improve predictive accuracy, it highlights the iterative nature of data analysis and the necessity of careful data exploration, model improvement, and outlier consideration. Furthermore, the research highlights the importance of elements like HOA fees in shaping property values, providing industry professionals with insightful advice on how to successfully negotiate the intricacies of the real estate market.
To sum everything up, Dr. Ozgur and his colleagues’ research offers a thorough framework for comprehending and forecasting real estate market housing prices. Businesses can obtain important insights into pricing dynamics by utilizing multiple regression analysis, which helps them to make well-informed decisions and maximize their success strategies.
https://core.ac.uk/download/pdf/303864121.pdf
