The data we had was for the commercial real estate data for the year of 2017.
1st Approach: The Standard Deviation Approach with regard to the price per square feet
- Trying to look for zip codes with potential for commercial real estate valuation expansion.
Issues: Many zip codes had very few data points, with quite a few have less or equal to three which yielded highly biased standard deviation values. Because of that, the value of standard deviation could not give an accurate representation of the real estate market.
https://public.tableau.com/profile/daqi.chen#!/vizhome/HackNC2019/Dashboard1
2nd Approach: Given a city, the total sales in commercial real estate in the year of 2017.
- Identifying the market with the most buying power, or getting sold out.
- With the limited data we have on broker firms, who are the largest players in the year 2017.
- Issue: Only addressed the volume of real estate transaction, but doesn't necessarily take into account the quality or the total square footage sold in 2017. With more time and resources, maybe using data extracting from web pages.
https://public.tableau.com/profile/daqi.chen#!/vizhome/2017RecordsSoldHackNC2019/Broker?publish=yes
But Not Yet,
Bringing in population data from Government Census:
- Took the top 10 most populated metropolitan cities.
- Calculated the revenue of 2017 for each city.
- Purpose: wanted to see if there is a correlation between population and total transaction values of commercial real estate in 2017
https://public.tableau.com/profile/daqi.chen#!/vizhome/2017RecordsSoldHackNC2019/Broker?publish=yes
Conclusion:
- Who's got the hottest market? - Las Vegas
- There is a somewhat weak correlation between the population and the market for commercial real estate.
- It's EXPENSIVE to live in New York.
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