Use the information from your project to create a PowerPoint of not more than three to five slides that you would share with the Executive team to summarize the project so far.
Use the information from your project to create a PowerPoint of not more than three to five slides that you would share with the Executive team to summarize the project so far.
Project (Obtain the Data):
There are a lot of different kinds of Airbnb guests in New York City. In this research
project, we want to find out what factors affect the nightly rental price for Airbnb advertisements
and explain those factors. By knowing these factors, homeowners can set the rent at a level that
will carefully seek to maximize occupancy and revenue. This analysis is based on the main
question, “Which factors most greatly affect how much does an Airbnb rental in New York City
cost per night, and how can this information help homeowners come up with the best pricing
strategies?”
Dependent Variable
This study focuses on the cost of renting Airbnb properties for a single night. This is the
dependent variable that I want to explain or predict with the model.
Independent Variables
The following independent factors will be looked at to figure out the nightly rental price:
1.Getting close to important sites like Central Park and the Empire State Building is
called location proximity.
2. Borough: The borough where the offering is based, which shows how prices vary in
Manhattan, Brooklyn, Queens, the Bronx, and Staten Island.
3. Size of the rental unit: This can be shown in square feet or by the number of bedrooms
and bathrooms.
4. Furnishings Quality: How old and in good shape the furniture and decorations are, with
a scale from “new” or “modern” to “old” or “dilapidated.”
5. Level of Privacy: The type of rental (full house or apartment vs. private room vs.
shared area).
6. Guest Reviews: The average rating that past guests gave for cleanliness, service, and
other problems with the facility, considering both the number and quality of reviews.
7. Date of Booking: Prices change depending on the day of the week, especially when
booking on the weekend vs. during the week.
Methodology
The approach is primarily quantitative, with a focus on utilizing regression analysis.
Statistical tools and data analysis software that are capable of supporting regression analysis
would be utilized. Improving the machine learning models would be beneficial, if feasible. The
study will focus on data from the past year to analyze seasonal changes.
The data sets will be utilized to showcase various factors such as the geographical
location, dimensions, furnishings, level of privacy, and customer reviews. Additional
information about the days of the week and special events can be obtained from external sources
if it is not available in the provided datasets. This study aims to develop a predictive tool that can
assist homeowners in making informed decisions when listing their homes on Airbnb.
Understanding the impact of various factors on rental prices empowers homeowners to adapt
their pricing strategies and maximize their earnings.
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