NAME: Modeling home prices using realtor dataTYPE: Random sampleSIZE: 76 observations, 19 variablesDESCRIPTIVE ABSTRACT:The data file contains information on 76 single-family homes inEugene, Oregon during 2005.  This dataset is suitable for a completemultiple linear regression analysis of home price data that coversmany of the usual regression topics, including interaction andpredictor transformations.  Whereas realtors use experience and localknowledge to subjectively value a house based on its characteristics(size, amenities, location, etc.) and the prices of similar housesnearby, regression analysis can provide an alternative that moreobjectively models local house prices using these same data.SOURCES:The data were provided by Victoria Whitman, a realtor in Eugene, in2005.  The data were used in a case study in Pardoe (2006).VARIABLE DESCRIPTIONS:id = ID numberPrice = sale price (thousands of dollars)Size = floor size (thousands of square feet) Lot = lot size category (from 1 to 11)Bath = number of bathrooms (with half-bathrooms counting as 0.1)Bed = number of bedrooms (between 2 and 6)BathBed = interaction of Bath times BedYear = year builtAge = age (standardized: (Year-1970)/10)Agesq = Age squaredGarage = garage size (0, 1, 2, or 3 cars)Status = act (active listing), pen (pending sale), or sld (sold)Active = indicator for active listing (reference: pending or sold)Elem = nearest elementary school (edgewood, edison, harris, adams,crest, or parker)Edison = indicator for Edison Elementary (reference: EdgewoodElementary)Harris = indicator for Harris Elementary (reference: EdgewoodElementary)Adams = indicator for Adams Elementary (reference: EdgewoodElementary)Crest = indicator for Crest Elementary (reference: EdgewoodElementary)Parker = indicator for Parker Elementary (reference: EdgewoodElementary)SPECIAL NOTES:None.STORY BEHIND THE DATA:The data file contains information on 76 single-family homes inEugene, Oregon during 2005.  At the time the data were collected, thedata submitter was preparing to place his house on the market and itwas important to come up with a reasonable asking price.  Whereasrealtors use experience and local knowledge to subjectively value ahouse based on its characteristics (size, amenities, location, etc.)and the prices of similar houses nearby, regression analysis providesan alternative that more objectively models local house prices usingthese same data.  Better still, realtor experience can help guide themodeling process to fine-tune a final predictive model.  For example,both realtor experience and regression modeling results suggest theneed for a BathBed interaction term and an Age-squared transformationin the model.PEDAGOGICAL NOTES:It can be challenging when teaching regression concepts to findinteresting real-life datasets that allow analyses that put all theconcepts together in one large example.  For example, concepts likeinteraction and predictor transformations are often illustratedthrough small-scale, unrealistic examples with just one or twopredictor variables that make it difficult for students to appreciatehow these concepts might be applied in more realistic multi-variableproblems.  This dataset addresses this challenge by allowing for acomplete multiple linear regression analysis of home price data thatcovers many of the usual regression topics, including interaction andpredictor transformations.  The statistical ideas discussed range fromthose suitable for a second college statistics course to thosetypically found in more advanced linear regression courses.REFERENCES:Pardoe, I. (2006). Applied Regression Modeling: A Business Approach.Hoboken, NJ: Wiley.SUBMITTED BY:Iain PardoeUniversity of OregonLundquist College of Business, 1208 University of Oregon, Eugene, OR97403, USA.ipardoe@lcbmail.uoregon.edu