Which approach is commonly used in mass appraisal to analyze large data sets?

Multiple regression analysis is commonly used in mass appraisal to analyze large data sets because it allows appraisers to model the relationship between property values and multiple factors simultaneously. In mass appraisal, the goal is to estimate the value of a large number of properties based on various characteristics such as location, size, age, and amenities.

By using multiple regression analysis, appraisers can quantify the impact of each characteristic on property values, providing a systematic and statistically rigorous approach to valuation. This method helps to control for confounding variables and allows for predictions of property values based on the established relationships in the data. As a result, multiple regression is highly effective for processing and analyzing the diverse and extensive data sets encountered in mass appraisal.

Other approaches, while useful in specific contexts, do not provide the same level of analytical power when dealing with large sets of data. Descriptive statistics summarize data without identifying relationships, case study analysis focuses on in-depth examination of individual properties, and qualitative research does not lend itself well to the quantitative valuation required in mass appraisal processes.