JSM 2005 Online Program

Abstract #304569

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	231
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Business and Economics Statistics Section
Abstract - #304569
Title:	Using Quantile Regression for Prediction Purposes
Author(s):	Subhash C. Narula*+ and John F. Wellington
Companies:	Virginia Commonwealth University and Indiana University-Purdue University, Fort Wayne
Address:	School of Business, Richmond, VA, 23284-4000, United States
Keywords:	data analysis ; linear regression ; prediction model
Abstract:	We discuss the use of quantile regression in the development of the single equation linear prediction model of residential property value. Attributes of the property such as age of the home, square feet of living space, and number of rooms serve as the predictor variables. The response variable is the current value of the residential property (home). The descriptive properties of quantile regression provide the analyst and decisionmaker with a variety of alternatives that allow investigation of the consequence of nonzero error of prediction. Overprediction of property values results in additional tax revenue and challenges to the taxing authority. Underprediction results in lower tax revenue. The discussion includes the principle, estimation technique, and loss function implicit to quantile regression. We illustrate the approach with results from a reappraisal study of Midwest residential properties.

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