This is a Real Estate Regression Analysis work.1)only expert in the subject matter must handle.2)only section #6 needs to be answered.(The Limitation) on the whole project.3)the solution should not be more than (1)page.4)deadline of 12:30pm today is crucial. All the completed work is attached, only section 6 needs to be answered. Not more than a page.
Project #1: Regression Analysis – Residential Real Estate. REE 6935 - Fall 2020 – Dr. Beracha This real estate regression analysis project involves compiling and analyzing real world real estate data. You are asked to analyze residential real estate data to determine the effect of school quality on residential real estate. Specifically, you will determine whether and the extent to which purchase prices and rent prices are affected by school quality and whether these affects are dependent on whether the properties are occupied by renters or owners. The goal of this assignment is to examine your ability to analytically answer relevant real estate questions using a linear regression. After you successfully complete this project you will be tooled to analytically answer many other relevant questions, given an appropriate dataset is available. You will be working on this assignment in a group of 4-6 students. A project submitted by a group that doesn’t include 4-6 students will incur a grade penalty of 10%. The assignment is due on or before November 18th at 11:55 p.m. EST. By that time you should submit an electronic copy (via Canvas, not via email) of your assignment. Additionally, you will present your results in class (in person or via Zoom) on November 19th. If you turn your assignment late, 10% will be deducted from your grade for every calendar-day delay. Before you begin working on this project, one representative from each group must email me with the names of the group members and I will assign you the county for your analysis. Your complete assignment should read and look like a professional report and include the following sections: 1. Short introduction: Describe the questions you are attempting to answer; Tell the reader what you expect to find (your hypothesis/hypotheses) and why. 2. Data preparation and description: Before you begin with your analysis, your dataset must be fitted to answer your research questions. To prepare your dataset, you will need to do the following: - Define a range for a few property characteristics and exclude all observations that do not meet your defined ranges. This allows you to work with more a homogeneous set of observations and eliminate some of the errors embedded within the original dataset. You will define ranges for SQFT, Bedrooms, Bathrooms (full and half) and Age based on the segment of the market that you would like to explore (the same characteristic ranges should be applied to your rent and purchase datasets). - Report your defined ranges. - Report the initial number of observations in each dataset (before the characteristic ranges were applied). - Report the final number of observations in each dataset (after the characteristic ranges were applied). - Create a variable that serves as a proxy for school quality and explain how this variable is defined. - For each of the two datasets, create a table that provides some basic statistics (average, min, max and SD) for selected key variables. 3. Regression analysis: Apply each of the models below to each of your two datasets (rent and purchase) and report only the relevant (adjusted R^2, coefficient of each variable and t-stat of each coefficient) regression results within a table. 1) ????? = ?0 + ?1???? + ?2???????? + ?3???ℎ???? + ?4??? + ?5???2012 + ?6??ℎ???_??? 2) ??_????? = ?0 + ?1??_???? + ?2???????? + ?3???ℎ???? + ?4??_??? + ?5???2012 + ?6??ℎ???_??? 4. Interpretation of the regression analysis and estimations: In this section you will provide answers to the following questions: - Are all the coefficients you report in part 3 consistent with your expectations? If not, which coefficients are inconsistent? - Using the 1st regression model, what is the average estimated rent price in 2012 for a 2000 sqft, 3-bedroom, 2-bath, 16-year-old property that is associated with average school quality? Show your work! - Using the 1st regression model, what is the average estimated purchase price in 2012 for a 2000 sqft 3-bedroom, 2-bath, 16-year-old property that is associated with average school quality? Show your work! - According to regression models (1) and (2), what is the effect of school quality on rent prices in dollars and in percentage, respectively? - According to regression models (1) and (2), what is the effect of school quality on purchase prices in dollars and in percentage, respectively?? 5. Discussion: Briefly discuss your results. Are your results consistent with your initial hypotheses? 6. Limitations: Point out some limitations of your analysis and the possible issues or problems that may prove your results to be inaccurate. 7. Conclusion: Conclude your report using two-three paragraphs. Deliverables: - ONE Excel file including your modified data and the outputs of your regressions. - ONE Word file including text and tables. The Word file should be organized as a complete and well flowing report. The total length of the report must not exceed 7 pages (single space, font size 12 and default size margins). - ONE PowerPoint file including your presentation slides. Your assignment files should be named: “SENDER’S NAME – REE6935_Proj1_Excel” “SENDER’S NAME – REE6935_Proj1_PDF” “SENDER’S NAME – REE6935_Proj1_PP” Grading criteria: Following the project guidance: 5% Please make sure that your report follows the description provided above, including the name of file submitted, submission via Blackboard and the material included in each section. Professionalism, clarity and conciseness: 5% Margins, spacing, and fonts have been chosen to make the document attractive and easy to read. Tables, figures and graphs have been used to summarize data and effectively illustrate points. Headings are used judiciously to help reader find key sections in longer reports. It should look like a professional report. Contains few typographical errors and is well-printed. Be brief and clear! The total length of the report must not exceed 7 pages. Section 1: 8% Section 2: 10% Section 3: 14% Section 4: 18% Section 5: 14% Section 6: 8% Section 7: 5% Presentation 13% Introduction When searching for a place to reside one of the most important factors in purchasing or renting is the desired square footage of a potential dwelling. However, there are numerous other factors that present a perceived or a real value relative to the sale price or advertised rent amount. This study endeavors to ask pertinent questions that are addressed conscious or sub-consciously by prospective homeowners, tenants and real estate professionals such as the following. What is the selling price per square foot? · What would the difference be in cost for a home in a neighborhood with a good school? · Will the age of the home or apartment affect the price? · Is the total number of bedrooms a passive variable in comparison to square footage? · To what extent does age matter in terms of price and has a sub-segment does a new home drive a premium? Using a regression analysis as a method to determine the relationship of variables we expect the (1) square footage and price to have a definitive relationship but diminishes in impact with a wider range in pricing. (2) Total number of bedrooms are statistically insignificant given square footage typically dictates the number of bedrooms, (3) Number of bedrooms has only a small impact on pricing, and (4) the quality of school does not drive price. Data Preparation and description Two data sets were used to test our hypothesis, the first a property purchase and second a rental scenario. Both models utilized data obtain for Broward County, Florida region, dated 2012 and earlier. A process of preparing the data for our analysis included removing or modifying data that appeared to be incorrect, incomplete, irrelevant, duplicated or improperly formatted. The purchase property initial data set size is 29,275 houses with 21 variables, 10 of which were utilized with in our analysis. The variables are selling price, square footage, bedrooms, bathrooms, age of property, school grading. Following preparation of the data set 12,158 were houses were remaining. The following characteristics define the range captured per the regression analysis; Variable Range Average Minimum Maximum STD Selling Price $200,000 - $6,000,000 409,371 200,000 6,000,000 354,531 Square Footage 1,000 sq. ft. - 10,000 sq. ft. 2,667 1,018 9,986 999 Bedrooms 1 to 8 4 1 8 0.877 Bathrooms 1 - 10.5 2.8 1.0 10.5 0.946 Age 0 to 50 years 21 0