Hello,
For your project, I will adopt a thorough Python-based data analysis approach. My first step will involve the collection of data, where I will either scrape or import the Denver County delinquent tax liens database, depending on its availability and format. This database should contain the necessary data points, such as property value, tax amount, and the owner's contact details.
Once I have the data, I'll employ Python libraries like pandas to clean and preprocess it. I'll take care of missing values, rectify any inconsistencies, and format the data for analysis. I'll also use geocoding libraries like Geopy to enrich the dataset with additional geographical information, including zip codes and school districts.
In the analysis phase, I'll apply statistical methods and data aggregation to pinpoint areas with the highest home values and examine the relationship between property values and tax amounts. I'll use clustering algorithms from Python’s Scikit-learn library to detect patterns and group similar properties, aiding in the identification of prime investment areas.
Based on your specified criteria, I'll filter out the top 100-500 properties. This will involve setting thresholds for property values, tax amounts, and other relevant parameters. I'll present the results in a clear and accessible format, such as a CSV file or an Excel spreadsheet, which will include a list of potential investment properties with all necessary details.