Difference between revisions of "Business Mafia Proposal"
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== Project Motivation == | == Project Motivation == | ||
| + | The overwhelming majority of Airbnb hosts are individual home owners who are renting out parts of their apartment to earn additional side income. Most hosts do not have a robust approach to setting prices. More often then not, prices are determined intuitively – through gut feeling. | ||
| + | Our group hopes to provide these homeowners with another alternative approach to pricing, through robust understanding of their listing’s geographical location and its relationship with Downtown Seattle. However, the main challenge in doing so is to simplify and summarise technical, complex analytics techniques into layman terms that every host can easily understand. | ||
| + | |||
| + | To effectively do so, we have decided to create an RShiny Application that will guide them step-by-step through the thought process. We hope that they will take away with them not only the final listing price from the model, but also our thought process and methodology in determining them. | ||
<br/> | <br/> | ||
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# Analyse the spatial relationships between various key locations and Airbnb listings in Downtown Seattle to determine if the listing's location to key places affect its listing price | # Analyse the spatial relationships between various key locations and Airbnb listings in Downtown Seattle to determine if the listing's location to key places affect its listing price | ||
# Through the use of Local Geographical Weighted Regression (GWR) Model, we hope to help Airbnb owner(s) determine the better pricing for their listing(s). | # Through the use of Local Geographical Weighted Regression (GWR) Model, we hope to help Airbnb owner(s) determine the better pricing for their listing(s). | ||
| − | <br | + | </br> |
== Our Datasets == | == Our Datasets == | ||
Revision as of 13:08, 20 March 2019
Contents
Project Motivation
The overwhelming majority of Airbnb hosts are individual home owners who are renting out parts of their apartment to earn additional side income. Most hosts do not have a robust approach to setting prices. More often then not, prices are determined intuitively – through gut feeling.
Our group hopes to provide these homeowners with another alternative approach to pricing, through robust understanding of their listing’s geographical location and its relationship with Downtown Seattle. However, the main challenge in doing so is to simplify and summarise technical, complex analytics techniques into layman terms that every host can easily understand.
To effectively do so, we have decided to create an RShiny Application that will guide them step-by-step through the thought process. We hope that they will take away with them not only the final listing price from the model, but also our thought process and methodology in determining them.
Project Objective
Through our project, we aim to:
- Derive individual walking distance between various key attractions and Airbnb listings in Downtown Seattle
- Analyse the spatial relationships between various key locations and Airbnb listings in Downtown Seattle to determine if the listing's location to key places affect its listing price
- Through the use of Local Geographical Weighted Regression (GWR) Model, we hope to help Airbnb owner(s) determine the better pricing for their listing(s).
Our Datasets
| Data | Source | Data Description | Source URL | Data Type |
|---|---|---|---|---|
Literature Review
Sources:
- https://towardsdatascience.com/airbnb-rental-listings-dataset-mining-f972ed08ddec
- https://www.airbnbcitizen.com/the-airbnb-community-in-seattle/
Our Methodology
Project Storyboard
Application Overview
Our Findings
Reflecting on our project
Project Timeline
