Price Optimization of Condominium Units

Project Description

We are a real estate developer that would like to develop a Proof of Concept (POC) predictive model to identify which factors influence the pricing and sales of a home and/or condominium units. The POC will utilize all available historical data for condominium projects. After the POC has demonstrated value with predictive power and an actionable implementation within the project, the scope may be expanded across a broad array of projects.

The POC will utilize the full history of condominium sales data, including (but not limited to):

• Unit available for sale date
• Unit list price
• Unit sale date
• Unit sale price
• Agent incentive fee and characteristics
• Unit characteristics
• Building characteristics
• Accessibility measures
• Expected building transfer date
• Advertising expenses
• Brand
• Project
• Room Type
• Room Size
• Floor
• Tower
• Project Transfer Duration
• Transfer Duration
• Selling Duration
• Price/Sqm
• Price Increase/Decrease
• Selling Discount
• Transfer Discount
• Resale Count
• Resale Amount
• Transfer Amount
• SPA Amount
• More to be determined

With the data organized into a structured format and validated for accuracy and completeness, the data scientist will build models to predict the optimal price for a unit given a collection of data points.

Restrictions, such as the sum of the revenue of all individual units must equal the set building revenue, will be incorporated as modeling options as required by the client.

For new model predictions, the heads of business units will enter building attributes, unit attributes, and all other required characteristics into a simple-to-use front end tool. The output generated will be a numeric suggested price. Additionally, work will be done to derive estimates for the effects of changes in each variable. Depending on the complexity desired of the model developed, and variation in the data across buildings, these by-variable impacts may or may not be attainable individually at a high accuracy level.

The statistical model will be developed in R, accompanied with a spreadsheet and/or PowerPoint deliverable that gives details on the functional form of model, examples of how historical sales patterns fit the function, and measures of model accuracy.

The user tool for the POC will be published in Excel or other front end alternative (Shiny app) and serve for testing purposes to evaluate the quality of results and overall value. Recommendations for the expansion of the tool beyond the POC will be given for future implementation.

The client will have full access to all code and tools used in this project, both for model development and model deployment.

Note: This project is being awarded to a data scientist which whom we have an existing relationship.