Last Updated: December 30, 2023By

Background: A prominent real estate agency seeks to optimize its property pricing strategy to ensure optimal market rents and minimize time on the market. Leveraging aNumak & Cpmapny’s consulting, the agency aims to uncover complex relationships between various property features and rental prices, providing insights beyond simple correlation analysis.

Data Overview: The agency possesses a comprehensive dataset with over 5000 rows and 7 variables:

  1. number_of_rooms: Total number of rooms in the property
  2. number_of_bathrooms: Total number of bathrooms
  3. sqft: Total area of the property
  4. location: Classified as “great,” “good,” and “poor”
  5. days_on_market: Days from listing to rental
  6. neighborhood: The actual neighborhood
  7. rental_price: The actual rental price

Insights from Correlation Analysis: Upon uploading the dataset, the agency performed a correlation analysis, revealing the following insights:

  • Positive correlations: Number of rooms, area, and number of bathrooms.
  • Negative correlation: Days on the market.

Linear Regression Analysis: The linear regression analysis further delved into these correlations, providing regression coefficients. Notable findings include:

  • Each additional bedroom increases rental prices by $1,079 per month.
  • An extra square foot contributes an average increase of $3.3 per month.
  • Each day on the market correlates with a decrease of $21 per month in rental prices.

Non-linear Regression Analysis with Actable AI: To unveil more intricate relationships, the agency utilized Actable AI’s non-linear regression analysis. This allowed for predicting rental prices by selecting relevant predictors and offering a user-friendly interface.

Key Features of the Non-linear Regression Analysis:

  1. Prediction Model Building: Users can easily choose rental price as the prediction target and other relevant variables as predictors.
  2. Shapley Values Interpretation: The analysis returns predicted results along with Shapley values, indicating the contribution of each predictor to the final prediction.

Example Output: For instance, the predicted rental price of a property is $4,734.6. The analysis highlights that having 3 bedrooms adds $873.4 compared to the average rental price, while the downtown location reduces the price by $1.82.

Model Performance: The analysis assesses the model’s performance on a validation dataset, demonstrating:

  • High predictability (R2 of 0.99, where the max is 1).
  • Low root mean square error (RMSE) of about $45.3.

Key Predictors: The model identifies the most influential predictors for rental prices, ranking number of rooms, number of bathrooms, and locations as the most significant factors.

Conclusion: aNumak’s consulting on regression analysis empowers the real estate agency to refine its pricing strategy effectively. The model captures linear relationships and uncovers nuanced connections, enabling the agency to make data-driven decisions and optimize rental prices for faster market penetration and increased revenue.

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