Predictive Analytics

Objective: Answer the following questions. Submit a word document and the R Script you used. This does not have to be a managerial report.

Part One: Time Series

  • Perform time series analysis on the total dollar amount of residential real estate sales on your neighborhood.
  • Use sales beginning in the year 2009 to develop your model. Develop a forecast for the next 8 quarters of sales.
  • Present your findings to include all the below:
    1. A table that shows the forecast numbers, confidence bands for the next 8 quarters.
    2. Define the model type that you used (Additive or Multiplicative) and why you used it.
    3. Determine whether your model factored trend and seasonality and why it did or did not.

Part Two: Regression Forecast

In this part, you are to develop two models. One that includes time only as a predictor. The other includes both time and seasonality. For both models, list and discuss the below points.

    1. The model equation.
    2. The significance of each predictor. What does that value represent and what does it mean for your model?
    3. The R squared and the adjusted R squared. What does each metric mean?

Part Three: Regression Prediction

Use a multiple regression model to determine the sale of a given residential property in your neighborhood. Include:

    • Sale Date
    • Year built
    • Building type (categorical)
    • Gross Square Feet
    • Number of Units

After you build the model, answer the following questions

    1. what are the most and least useful predictors of the amount of a sale?
    2. Are there any redundant independent variables? How can you tell?
    3. According to your model from (3), which properties were the biggest bargains and which were the most overpriced? How might you account for these disparities?

Part Four: Analysis

Write a paragraph to summarize your findings with a focus on the output, interpretation of the output, and what the insights mean for our decision-making process

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