Page 24 - Regression Guideline

Frequently"Asked"QuesJons"con’t..."
SAVVI’s"mulJvariable"regression"models"do"“examine”"a"large"set"of"variables"in"relaJon"to" predicJng"a"sale"price"and"they"do"so"by"comparing"fluctuaJons"in"sales"price"for"a"large"(200+)" number"of"properJes."The"influence"of"each"variable"on"sale"price"is"assessed"relaJve"to"all"of" the"other"variables"being"considered"and"for"all"of"the"properJes"being"analyzed."Consequently," some"variables"will"be"found"to"be"very"good"predictors"of"sales"price"while"others"will"not"be" good"predictors;"they"add"nothing"to"the"mix."Poor"predictors"are"dropped"from"the"final"model." Although"it’s"possible"to"include"all"of"the"predictors"in"the"final"regression"model,"the"poor" predictors"add"lisle"to"determining"the"sale"price"and"unnecessarily"complicate"the"model."By" focusing"on"only"those"variables"that"influence"price,"the"SAVVI"regression"model"gives"you"the" best"of"both"worlds:""
"
1) It"produces"the"most"accurate"model"for"esJmaJng"a"sales"price"based"solely"on"the"most"
relevant"predictors;"and"
2) It"simplifies"the"regression"model"by"excluding"predictors"that"add"no"or"lisle"informaJon"
for"esJmaJng"a"sale"price."" "
The"final"equaJon"most"accurately"reproduces"“predicts”"the"actual"sales"prices"of"the"200+" properJes"that"were"used"in"the"analysis."AdjusJng"that"equaJon"up"or"down"to"account"for" property"characterisJcs"that"the"appraiser"believes"are"undervalued"or"overvalued"by"the"model" will"consequently"make"the"predicJon"less"accurate.""
"
The"exclusion"of"variables"or"property"characterisJcs"that"do"not"uniquely"predict"price"is"the" explanaJon"for"quesJons"like"why"there"is"no"adjustment"for"basement"square"feet"or"for"bed" or"bath."These"factors"ARE"considered"and"implicitly"included"in"the"model."The"variaJon"they" add"to"the"final"esJmated"price"is"mathemaJcally"accounted"for"by"the"variables"that"remain"in" the"model.""
"
Some"of"the"other"quesJons"are"due"to"the"same"misunderstanding"that"the"weights"assigned" to"each"property"characterisJc"(and"hence"their"contribuJon"to"the"final"esJmated"sales"price)" adjust"for"other"variables"in"the"model"as"well"as"those"excluded"from"the"model."For"example,"a" higher"value"might"be"assigned"for"basement"square"feet"because"the"effects"of"square"feet"are" already"accounted"for"by"other"variables"in"the"model"such"as"number"of"bedrooms"and"baths."" Last,"with"respect"to"the"quesJon"as"to"why"no"adjustment"was"made"for"a"property"that"has"a" 1,500"square"foot"basement"while"the"selected"comparables"have"none"and"no"adjustment"has" been"made,"the"answer"is"that"the"esJmated"price"for"the"property"is"based"on"analyses"of"the" full"data"set"containing"200"or"mode"properJes."It"is"not"based"solely"on"those"few"selected" properJes"that"end"up"as"comparables."In"effect,"the"model"has"already"mathemaJcally" accounted"for"the"added"value"of"the"basement"square"footage.""
23"