Month: December 2023

Report - Project 3

Report - Project 1

Today, we’re focusing to detect anomaly within the economic indicator’s dataset. Anomaly detection is a powerful statistical technique used to identify unusual patterns that do not conform to expected behavior. These outliers can often provide critical insights.

In Essence, think of anomaly detection as the process of finding the needles in the haystack. In the context of our economic data, these ‘needles’ could be unusual spikes or dips in indicators like unemployment rates or hotel occupancy. Identifying these anomalies is crucial because they could signal significant economic events, shifts, or even errors in data collection.

We’re trying to employ the Isolation Forest method, a sophisticated algorithm well-suited for pinpointing anomalies in complex datasets. This technique is especially effective in handling large, multidimensional data, making it ideal for our purpose.

Today, we’re planning to do clustering analysis to discover distinct phases within the economy. This is hard yet accessible approach will allow us to group similar periods in economic indicators, painting a clearer picture of our economic cycles.

Clustering is like grouping different moments in time based on their economic characteristics. Imagine categorizing years or months when the economy showed similar trends in unemployment, hotel occupancy, and housing prices. This method can help us identify periods of growth, stability, or recession in a more structured manner.

By applying clustering techniques to our dataset, we can uncover patterns that might not be immediately evident. It’s like finding hidden stories in the data – stories about when and how our economy thrived or faced challenges. This method goes beyond looking at individual indicators by revealing how they interact over time.

We plan to use a selection of key economic indicators for this analysis. The idea is to see how these indicators cluster together at different times, giving us insight into the economic conditions of those periods. We’ll be looking for correlations and patterns that define distinct economic phases.å

In this blog, we take a closer look at the seasonal rhythms of the housing market and tourism sector, founded through an analysis of median housing prices and hotel occupancy rates.

The first graph gives a picture of how hotel occupancy rates ebb and flow throughout the year. These patterns are a window into the tourism industry’s seasonal heartbeat, where certain months show higher occupancy, possibly due to holidays or favorable weather, while others dip, reflecting off-peak times.

The second graph showcases the average monthly trends in median housing prices. Unlike hotel occupancy, the housing market’s seasonality is subtler but still telling. We might observe higher activity and prices during specific times of the year, aligned with general buying patterns or economic cycles.

Understanding these seasonal trends is more than an academic exercise. For businesses in the tourism industry, these insights are crucial for planning and strategizing. Similarly, for real estate professionals and homebuyers, knowing when the market tends to peak or cool can inform smarter decisions.

This analysis reminds us that both the housing market and tourism sector dance to the rhythm of seasonal patterns. It highlights the importance of timing in both industries and offers a nuanced view of how different times of the year can shape economic activity.

In our latest analysis, we’re focusing on foreclosure trends, a critical yet often overlooked aspect of the economy. By examining the changes in foreclosure petitions and deeds, we gain insights into the housing market’s stability and the broader economic situation.

The graph of foreclosure trends shows the ups and downs in petitions and deeds over time. An increase in foreclosures typically points to economic stress, like job losses, affecting homeowners’ ability to pay mortgages. On the flip side, a decrease suggests a healthier economy and stable housing market.

These trends are closely linked to other economic factors. For instance, higher unemployment can lead to more foreclosures, and shifts in housing prices can influence homeowners’ financial decisions.

High foreclosure rates impact more than just numbers; they affect community stability and reflect real challenges faced by individuals and families.

This analysis not only sheds light on the housing market but also offers a unique perspective on the economy’s overall health. Stay tuned as we continue to explore and decipher these economic patterns.