Page 5 - Regression Guideline

""Regression"Process"""
The&SAVVI&AnalyDcs&Regression&Modeling&Approach&
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The"SAVVI"modeling"approach"is"called"“Hedonic"Regression”"because"it"uses"a"set"of" property"characterisJcs"to"esJmate"the"value"of"a"marketed"good;"in"this"instance"the"value" of"a"property."There"are"5"steps"in"the"process:"
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To"begin,"a"group"of"properJes"in"geographical"proximity"to"the"property"whose"value"is"to" be"appraised"is"selected"from"the"MLS"database."The"appraiser"can"use"their"own"MLS"data" or"data"supplied"by"SAVVI"AnalyJcs."The"number"of"properJes"selected"for"analysis"should" be"large"enough"(no"fewer"than"200"properJes)"to"allow"for"variaJon"on"the"property" characterisJcs"to"be"examined"as"well"as"on"the"sales"prices."At"this"point,"we"are"a^er" variaJon"and"not"strict"comparability,"because"having"variaJon"to"staJsJcally"model"actually" improves"the"esJmates"of"the"value"of"each"characterisJc"as"well"as"sales"price"of"the" appraised"property."This"step"includes"calculaJng"a"standard"deviaJon"score"for"each" property"to"idenJfy"which"have"extremely"high"or"low"sales"prices"relaJve"to"the"average"of" other"selected"properJes"and"removing"those"with"extreme"prices"from"the"database.""
" The"second"step"involves"calculaJng"a"Jme"adjustment"for"each"property"in"the"data"base"to" account"for"monthly"fluctuaJons"in"sales"price"conJngent"upon"when"each"selected"MLS"
property"was"sold"over"the"preceding"few"years."A"smoothing"algorithm"that"uses"5"month" """"""""""
averages"is"used"to"reduce"extreme"price"fluctuaJons"on"the"trend"line.""
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Third,"a"set"of"bivariate®ression"(i.e.,"twoPvariable)"models"is"run"using"the"selected" properJes."Bivariate"regression"assesses"the"associaJon"between"a"single"dependent" variable"such"as"sale"price"and"a"single"independent"or"predictor"variable"such"as"square" footage."A"separate"bivariate®ression"is"run"for"each"property"characterisJc"that"is"being" examined."Each"simple"regression"is"run"on"the"full"data"set"containing"informaJon"on"the" 200+"properJes."The"purpose"of"this"step"is"to"screen"for"those"property"characterisJcs"that" are"reliably"associated"with"variaJons"in"the"sales"prices"of"the"properJes"in"the"data"set.""
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In"the"fourth"step,"those"property"characterisJcs"found"to"be"reliably"associated"(i.e.," staJsJcally"significant)"with"the"sales"prices"of"the"examined"properJes"are"included"in"a" stepwise"mulDvariable®ression"(i.e.,"many"variables)"model."Using"the"full"data"set,"the" mulJvariable"regression"assesses"the"associaJon"between"sales"price,"the"single"dependent" variable,"and"a"set"of"mulJple"predictors"such"as"square"footage,"lot"size,"and"age"of"the" property.""
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