Incomplete Housing Listings

Requirements Clarification & Assessment

1. Understanding the Importance of Square Footage:

   • Primary Variable: Square footage is a critical variable in predicting housing prices, as it directly influences property value.
   • Correlation: Investigate how square footage correlates with other features like the number of bedrooms, bathrooms, and location.

2. Missing Data Analysis:

   • Distribution Analysis: Determine if missing square footage data is spread across all listings or concentrated in specific categories (e.g., location, property type).
   • Missing Data Type: Identify if the missing data is "Missing Completely at Random" (MCAR), "Missing at Random" (MAR), or "Missing Not at Random" (MNAR).

3. Data Source Reliability:

   • Consistency Check: Verify the reliability of data sources. Are there any patterns or anomalies in data collection that might explain the missing values?

4. Impact on Model Performance:

   • Sensitivity Analysis: Evaluate how sensitive the predictive model is to the absence of square footage data. Determine the threshold at which missing data significantly affects model accuracy.

5. Potential External Data Sources:

   • Supplementary Data: Consider other datasets or sources that might provide the missing square footage data.

6. Domain Expertise Consultation:

   • Real Estate Insights: Engage with real estate experts to gain insights into why square footage might be missing and how it impacts property valuation.