Open dchoe21 opened 3 years ago
katcorr
commented
3 years ago @dchoe21 @jokim21 @ethangilman
Sounds like a good new direction for the blog post, team! Do you have any set plans to meet as a group on a weekly or biweekly basis?
Update 1: 10/10
dchoe21
commented
3 years ago Hi Professor Correia,
I'm not sure if I should keep writing in this issue, or start a new one, but I'm here to give Update 2.
We were unable to meet our original deadline of 5/4 for our data wrangling. Some of our members had other commitments and assignments which used up the time they scheduled to code. We were also unable to schedule our meeting yesterday, in which we planned to go over our plan for members who missed last Thursday's class. We now plan to complete both of these objectives by our meeting tomorrow afternoon.
To answer your question, yes, our objectives outside of Updates 2 and 3 entail a Zoom meeting to work together and update each other on our progress, ahead of each official Update. That's 5/3, 5/8, 5/18, and 5/19.
I'll be back tomorrow to update you on our progress after our meeting tomorrow!
katcorr
commented
3 years ago @dchoe21 @jokim21 @ethangilman
Okay, sounds good -- yes, please do update me after your meeting so we can make sure the group is back on track with your original schedule, or if the plan needs to be readjusted to account for less time.
Update 2: 5/5
dchoe21
commented
3 years ago @katcorr
We've each completed our data wrangling and are in the process of individually pushing our files to the GitHub. Our next meeting has been tweaked a bit - 5/9 at 3 pm, when we plan to be close to completion on our visualizations. Otherwise, our schedule won't change. Great job team!
katcorr
commented
3 years ago Great, thanks for the update!
ethangilman
commented
3 years ago Update 3: We were able to meet up on May 9th in order to help each other out with creating our visualizations and keeping in touch to make sure we are on track to complete the project within our timeline. Joshua was able to finish his visualizations and is now starting on writing for the blog and preparing for presentations. Ethan finished his visualizations as well but is still working to make them more visually aesthetic. Daniel is still working on one of his visualizations due to R errors and technical difficulties but should be done by Thursday. We are looking to record our presentation after we meet on Thursday in class and complete the blog by the 18th.
katcorr
commented
3 years ago @dchoe21 @ethangilman @jokim21
update 3: 5/5
Main Questions How was the real estate market impacted by the pandemic? How well has the real estate market performed compared to other markets? What explains the real estate market behavior?
Visualizations
Project Proposal
For the Blog Project, the Funemployed Statisticians decided to explore the effect of the pandemic on a specific industry, real estate, rather than focusing only on employment in various industries. Despite flashbacks of the 2008 financial crisis and recessionary concerns due to the pandemic, the real estate frenzy has reignited after a short downturn. Many houses are selling well over asking price due to heavy competition and single family home inventory is at ⅔ of last year’s based on Aug 2019 and 2020. Demand is growing while supply is decreasing, snowballing the real estate market into a hot, competitive market. In order to examine such phenomena mid-pandemic, we seek to answer, ‘How did the COVID-19 pandemic impact the real estate industry?’ ‘How well has the real estate market performed compared to other markets?’ and ‘What explains this real estate market behavior?’
In order to accomplish this, we will create 6 visualizations:
1. National Sale Volume Difference Bar Graph This bar graph will show the overall volume of sales in the US and allow us to observe how the pandemic affected real estate sales by each month and year on a macro-level. This will have the months on the x-axis and dollars on the y-axis, and the different colors will be 4 separate years to compare pre and post-pandemic sales. The data will be downloaded from Zillow’s Database using their Zillow Sales Count data.
2. US Average Home Price Area Graph This will be a line graph which will illustrate the overall US house price trends and also provide a macro-level understanding of the US real estate market. This is important to realize the effect the pandemic had on the overall US market, despite regional differences. This will have years on the x-axis and Dollars on the y-axis. The data will be downloaded from Zillow’s Database using their Zillow Home Value data.
3. Home Price by State, Interactive Spatial Map in Shiny This is an interactive geospatial map that will allow us to observe median sale price and for-sale inventory from each state/city. This will include selecting month and year from a drop-down menu for an understanding of how the real estate prices vary within the US. The data will be downloaded from Zillow’s Database using their Zillow Home Value data.
4. Housing Price Index vs CPI, Case-Schiller vs S&P 500 Line Graphs This will be 2 adjacent line graphs that portrays how the housing market performed compared to other performance indexes. For instance, the Housing Pricing Index and Consumer Price Index, and the Case-Shiller U.S. National Home Price Index and S&P 500 use the same scale to make comparisons possible. This will allow us to understand how the real estate market performed vs the overall and stocks. The x-axis will be years while the y-axis will be the index number. The data will be downloaded from Federal Reserve Economic Data, St. Louis Fed (FRED).
5. Interest vs Home Price Scatterplot This will be a scatter plot with a regression line to see if there is a correlation between interest and home price. We hypothesize that there will be an increase in price when interest rates are low because lower interest rates encourage spending and investing. This will also allow us to examine the influence of lower interest rates in regards to higher home prices, but also point us to various effects of the pandemic. The x-axis will be interest rates and the y-axis will be home prices with individual dots representing years. This data will be combined by year from the FRED for mortgage interest rates, and Zillow Home Values for home prices.
6. Construction Material Costs vs Building Permits Percentage Change Line Graph This will be a percentage change line graph that will compare the construction material costs index percentage changes, and directly compare it to the percentage change of building permits approved by the government. This is to confirm the claim that high construction material costs play a role in inhibiting developers from building new homes, thus driving down supply and increasing prices. The x axis will be month and they-axis will be percent change, separated by color. The data will be downloaded from Federal Reserve Economic Data, St. Louis Fed (FRED).
Data Source Links: https://fred.stlouisfed.org/ https://www.zillow.com/research/data/
This is our schedule in order to complete our project on time. 5/3: Finalize our data scraping and wrangling 5/4: Update 2 Have our data scraped and wrangled, prepared for creating our visualizations 5/8: Complete most visualizations and provide help to each other 5/11: Update 3 Have visualizations and shiny application completed; prepare for the presentation and blog 5/18: Complete presentation 5/19: Submit blog post