I explored the “economic-indicators.csv” dataset to understand various aspects of the region’s economic landscape. Here’s a rundown of what I discovered:
I looked at the historical trends of hotel occupancy rates, trying to discern patterns or seasonal variations in the hospitality industry.
By calculating the average monthly passenger numbers at Logan Airport, I got a sense of the ebb and flow of travel, which speaks volumes about economic activity related to tourism and business.
The trend of new housing construction permits gave me insights into the region’s real estate development. It’s like watching the evolution of the area through the lens of construction permits.
I dove into the relationship between hotel occupancy rates and the average daily rates, unraveling the intricate dynamics that influence pricing strategies in the hotel industry.
Analyzing the seasonality of international flights at Logan Airport provided a glimpse into peak and off-peak travel periods, affecting various stakeholders like airlines and tourism authorities.
Calculating the average monthly new housing construction permits quantified the growth in the housing sector, a key indicator of economic health.
The trend of foreclosure deeds over time told a story about the financial health of the region’s residents and the stability of the local real estate market.
Examining the correlation between median housing prices and housing sales volume revealed insights into market dynamics, including supply and demand, affordability, and broader economic conditions.
In essence, each analysis contributed to understanding the region’s economic well-being and trends, painting a comprehensive picture of its economic landscape.