As a professional in the Real estate industry, we all know the primary sources for valuation derivation. But given the evolution of technology and GSE demands there is now a “4th approach to value”.
A multivariate approach to adjustments, using big-data and market trend analysis, you can now include the “4th approach” into your final analysis, substantiating your final value.
How Savvi Analytics Works
Preparing your data for analysis
Regression analysis is a process that begins with selecting a group of properties on which the regression analysis will be done. An important step in the Savvi Analytics regression process is gathering data on a set of properties that will be used to estimate how sales prices are affected by property characteristics. While it is important that the selected properties are similar to the property being appraised in terms of characteristics such as square footage, regression analysis require sufficient variation to adequately and accurately estimation the final model.
Savvi data management system Cleans, Organizes and Stores your data automatically. No other software helps you prepare your data for analysis like Savvi. With our automatic processing system your MLS data is checked for missing or inaccurate data. Any data that may give false results is removed or corrected before analysis begins.
Data Quality Tested
MAPE is a non-scaled error metric. MAPE is used as a figure of merit to identify whether a data mining method is performing well or not. The lower the MAPE, the better the performance of the data mining method. The mean or average of the absolute percentage errors of forecasts is a measure of accuracy of a method for constructing fitted time series values in statistics, specifically in trend estimation. This measure is easy to understand because it provides the error in terms of percentages.
Savvi's Analytic Engine
As a real estate appraiser, you are interested in determining how a set of characteristics about a given property such as number of baths and bedrooms, total square feet etc., affects the sale price of the property.
Although you might not have thought of them in this way, in statistical terms, the property characteristics that affect the sale price (up or down) are independent or predictor variables because they (help you the appraiser) estimate or predict what the property is worth. Statistically speaking, the estimated sale price is called the dependent variable because the estimated price depends on the set of property characteristic predictors that were considered when arriving at an appraised value. The statistical tool used to estimate a sale price is called regression analysis.