Acting as a consultant for a Real Estate Investment Trust (REIT).The REIT invest in houses and apartments in New York state. Part of the REIT business is try to predict the fair transaction price of a property before it’s sold. They invest in houses, apartments, and condos within a small county in New York state. They do so to calibrate their internal pricing models and keep a pulse on the market.
The REIT currently employs a third-party appraisal service for estimating the price of the property with their own expertise. In practice, the skill level of individual appraisers vary quite large. To estimate the mis-priced range, the REIT run a trial run to compare the actual transaction prices to the estimates from the appraiser. It was found that the estimates given by inexperienced appraisers differs $70,000 on average
The REIT propose that a data-driven approach to valuing properties instead of relying on the personal expertise from the appraiser. The REIT currently have an untapped dataset of transaction prices for previous properties on the market which our model will be using. The target is to predict transaction prices with an average error of under $70,000, then REIT ccould replace inexperienced appraisers with the model.
Deliverable: Trained model file
Machine learning task: Regression
Target variable: Transaction Price
Win condition: Avg. prediction error < $70,000, using Mean Absolute Error (MAE)
Timeline: 1 month