Iowa state law requires that all residential properties in the state be assessed every two years to determine property tax charges. This high volume of assessment can be a burden on municipalities. Using predictive modeling as part of the assessment process could ease this burden.
We have a strong lasso regression model available for use. Although further refinement would be beneficial, the model can currently predict within $22,500 of the target price on average. In its current form, human supervision of the model’s performance will be particularly necessary for properties over $275,000.
Documentation of the dataset is available in this directory. Please note that in the provided Jupyter notebooks, code blocks that perform external write commands are mostly been commented out to protect against accidental overwrites.
Our analysis began with a relatively clean dataset of over 2,000 home sales in Ames, IA between 2007 and 2010. We applied a lasso regression model which highlighted the properties’ above-ground square footage as the most heavily-weighted feature in predicting sale price. Basement square footage, the build year, an overall quality rating of “excellent,” and square footage of finished basement rounded out the most influential features. The model currently has an r2 score of .914 and an RMSE of 22,421.
Additional exploration of the data and feature engineering will likely improve the model’s performance, particularly with regards to high-value properties. As home values near $300,000 the model becomes less reliable. In the absence of this additional research, the model is currently ready for production on homes under $275,000.