Overall, I think this is a very clean and thorough report. It was especially informative given this is the first time I've been exposed to this project, and I was able to easily follow the commentary. The team is analyzing how airbnb prices have been affected due to the COVID 19 pandemic, analyzing pricing data in NYC in 2020 and comparing this data to 2019 pricing information.
First thing I liked was that the data cleaning all made sense, and I don't think any information was lost or misinterpreted, which is always an important concern when cleaning a dataset. The two pairs of histograms were also very informative and make it easy to visualize the initial findings regarding the team's hypothesis. Lastly, I also think the geographic map comparing September 2019 to 2020 does a good job of framing the issue of volume for airbnb as well, and will give great context when this is further analyzed.
One thing I think would have been helpful would be a histogram for the volume of each type of rental. You have the geographic map visualizing this, but that does a better job of showing the overall trend rather than how each subtype is changing. The histograms you have for pricing are solid, I think doing the same for volume would provide some more clarity on a more granular level. One potential suggestion as far as evaluating the impact of the COVID 19 pandemic on price would be a brief comparison to a different virus outbreak (think H1N1 is the only other one that's happened since airbnb was founded), and see if there was a substantial difference there. Could be that airbnb learned from that smaller outbreak and that's why prices haven't changed so much. Last suggestion I have would be to analyze the affect of the pandemic on volume. Revenue issues for a company stem from pricing and/or volume decreases. It very well may be that prices aren't affected by COVID, but I bet there might be a more telling trend from March through October on the volume of listings. Given the wealth of COVID data as well, you may be able to formulate a model that predicts how the volume of rentals changes given a change in COVID infection levels.
Overall I think this is great progress thus far. Everything makes complete sense, and you have some really interesting data you have found.
Overall, I think this is a very clean and thorough report. It was especially informative given this is the first time I've been exposed to this project, and I was able to easily follow the commentary. The team is analyzing how airbnb prices have been affected due to the COVID 19 pandemic, analyzing pricing data in NYC in 2020 and comparing this data to 2019 pricing information.
First thing I liked was that the data cleaning all made sense, and I don't think any information was lost or misinterpreted, which is always an important concern when cleaning a dataset. The two pairs of histograms were also very informative and make it easy to visualize the initial findings regarding the team's hypothesis. Lastly, I also think the geographic map comparing September 2019 to 2020 does a good job of framing the issue of volume for airbnb as well, and will give great context when this is further analyzed.
One thing I think would have been helpful would be a histogram for the volume of each type of rental. You have the geographic map visualizing this, but that does a better job of showing the overall trend rather than how each subtype is changing. The histograms you have for pricing are solid, I think doing the same for volume would provide some more clarity on a more granular level. One potential suggestion as far as evaluating the impact of the COVID 19 pandemic on price would be a brief comparison to a different virus outbreak (think H1N1 is the only other one that's happened since airbnb was founded), and see if there was a substantial difference there. Could be that airbnb learned from that smaller outbreak and that's why prices haven't changed so much. Last suggestion I have would be to analyze the affect of the pandemic on volume. Revenue issues for a company stem from pricing and/or volume decreases. It very well may be that prices aren't affected by COVID, but I bet there might be a more telling trend from March through October on the volume of listings. Given the wealth of COVID data as well, you may be able to formulate a model that predicts how the volume of rentals changes given a change in COVID infection levels.
Overall I think this is great progress thus far. Everything makes complete sense, and you have some really interesting data you have found.