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dc.contributor.authorMonson, Matt
dc.date.accessioned2020-09-04T17:19:18Z
dc.date.available2020-09-04T17:19:18Z
dc.date.issued2009-07-01
dc.identifier.other5469289
dc.identifier.urihttps://hdl.handle.net/1813/70656
dc.description.abstractBuildings can be compared to a bundle of goods sold in a market, where each of the building characteristics combined equate to the expected overall transaction value. By collecting data on many different buildings a regression analysis can be used to determine the correlation (relationship) of each characteristic to the transaction price —e.g. physical characteristics and other external influencing elements that may add or subtract from the building value. Each of these correlations can be measured to determine a degree of confidence (i.e. significance) and then subsequently be used to build a hedonic pricing model. Hedonic pricing models can be useful to determine the intrinsic value of each attribute, as well as to predict transaction prices. This can be particularly useful when traditional discounted cash flow models fall short because of the absence of a market, when no comparable buildings exist, and for nonincome generating buildings.
dc.language.isoen_US
dc.relation.ispartofseriesCornell Real Estate Review
dc.rightsRequired Publisher Statement: © Cornell University. Reprinted with permission. All rights reserved.
dc.subjectvaluation
dc.subjecthedonic; regression analysis
dc.subjectmarket price
dc.subjectpricing analysis
dc.subjectacquisitions
dc.subjectconsumer price index
dc.subjectBoston
dc.subjectresidential development
dc.subjectIllinois
dc.subjectoffice space
dc.subjectmulti-family
dc.subjectVirginia
dc.subjectCornell
dc.subjectreal estate
dc.titleValuation Using Hedonic Pricing Models
dc.typearticle
schema.issueNumberVol. 7
dc.description.legacydownloads2009_62_73_Monson.pdf: 32615 downloads, before Aug. 1, 2020.
local.authorAffiliationMonson, Matt: Cornell University


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