The demand for press brakes and shears with 1/4-inch capacities and less is stable despite an increasing number of private sales and auctions as smaller businesses shrink or close. Although later model equipment equipped with CNC controls is preferred by high production job shops, standard equipment 10- to 20-years old with less sophisticated controls is still very salable.
Methodology of Statistical Analysis
In every industry segment measured by AccuVal, data regarding transactions of similar assets is collected on a global basis, organized by their key attributes, and analyzed to identify trends in the marketplace. These transactions are generated by tracking thousands of sales annually which generate hundreds of thousands of transactions. This information becomes part of the world's largest database of transaction information and is analyzed using a proprietary asset management platform called "Asset Intelligence."
To create the trend line, transactions are grouped by asset type. The sales are further consolidated within asset types based on the frequency of occurrence within a range of sales prices. This refined group of more homogenous sales determines the basket of transactions used to define market trends for a specific asset type. These sales are the most common assets in that category and are within the highest frequency of sales prices occurring in the asset class being studied.
These items are further studied and categorized based on their specific attributes. These attributes may include things such as age and other key factors impacting the specification of an asset such as the weight capacity of a lift truck or the tonnage of a stamping press.
These sales are then run through a regression model to normalize the sales price based on age and the other attributes considered in the data set. Regression analysis in its simplest form finds the best fit for the relationship of all of the variables considered. The standard errors of the regression coefficients are used to then normalize the sales prices and account for differences in the variables or specifications of each asset. The normalized sales prices occurring within each quarter are then converted to a quarterly index based on calendar quarters.
Once the normalized sales price is obtained for each calendar quarter, this information is plotted on a graph over time. A three-period moving average is computed from these data points to reduce the impact of variances in the basket of goods sold in each quarter.
Lastly, the data over the last four periods is further analyzed and used to plot the most current trend line. This analysis produces the estimates of the anticipated percentage of change in value for the asset category in the next two periods.