Project #1: Regression Analysis – Residential Real Estate.
REE XXXXXXXXXXFall 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
eader 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 e
ors 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
anges were applied).
- Report the final number of observations in each dataset (after the characteristic
anges 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
ent 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
eports. It should look like a professional report. Contains few typographical e
ors and
is well-printed.
Be
ief 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 inco
ect, incomplete, i
elevant, 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
XXXXXXXXXX,371
XXXXXXXXXX,000
XXXXXXXXXX,000,000
XXXXXXXXXX,531
Square Footage
1,000 sq. ft. - 10,000 sq. ft.
XXXXXXXXXX,667
XXXXXXXXXX,018
XXXXXXXXXX,986
XXXXXXXXXX999
Bedrooms
1 to 8
XXXXXXXXXX4
XXXXXXXXXX1
XXXXXXXXXX8
XXXXXXXXXX877
Bathrooms
XXXXXXXXXX
XXXXXXXXXX2.8
XXXXXXXXXX1.0
XXXXXXXXXX10.5
XXXXXXXXXX946
Age
0 to 50 years
XXXXXXXXXX21
XXXXXXXXXX0