November 29, 2023

In today’s lecture,

  1. we explore the mechanics of the housing market, paying close attention to how median home values have changed over time and how they relate to larger trends in the economy.
  2. Our study shows the market’s varying highs and lows, just like a roadmap might.
  3. While price dips or plateaus may indicate a cooling market owing to economic developments or shifting buyer attitudes, rising prices frequently imply a solid economy and significant housing demand, signifying buyer confidence.
  4. These patterns are linked to more general economic factors like employment and interest rates.
  5. For example, a robust job market might raise the ability to purchase a property, which in turn drives up prices, while changes in borrowing rates can affect the fervor of potential buyers.
  6. Additionally, we observed possible seasonal patterns in the market.
  7. Time series analysis was performed on ‘economic indicators’ data in order to understand underlying patterns such as trends, seasonality, and cyclic behaviour.
  8. The data was arranged chronologically by time variable (columns ‘Year’ and ‘Month’).
  9. I later combined the ‘Year’ and ‘Month’ columns into a single date time column to indicate time.
  10. The time series data was also visualised using time series plots to observe patterns, trends, and seasonality in each economic indicator over time.
  11. The time series was then decomposed into its components (trend, seasonality, residual) for analysis using techniques such as seasonal decomposition.
  12. Seasonal decomposition is a time series analysis method that separates a time series dataset into three parts: trend, seasonality, and residual or error components.
  13. To predict future values of economic indicators, a forecasting model such as ARIMA (AutoRegressive Integrated Moving Average) was used.
  14. The data was then divided into training and test sets in order to train the model and evaluate its performance.
  15. To assess the accuracy of the forecasting model, a metric such as Mean Absolute Error (MAE) was used, and the predicted values were compared to the actual values in the test dataset to evaluate the model’s performance.

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