Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/73354
DC FieldValueLanguage
dc.contributor地政系-
dc.creatorLin, Yu-Ju;Chang, Chin-Oh;Chen, Chien-Liang-
dc.date2014-07-
dc.date.accessioned2015-02-09T02:05:34Z-
dc.date.available2015-02-09T02:05:34Z-
dc.date.issued2015-02-09T02:05:34Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/73354-
dc.description.abstractPrevious researchers discussing housing affordability issues have primarily focused on the housing pressure of the whole society, and most papers on this topic have discussed to a degree on the housing affordability situation of individual households. However, housing affordability involves many problems, and cannot be analyzed using only the average or median housing price. To clearly identify the housing affordability situation of individual households, the individual household housing price-to-income ratio (i.e., the micro PIR) is used in this paper.\r\n\r\nWe used the ordinary least squares model and quantile regression to analyze the micro PIR. The empirical results of this study show that the micro PIR has a right-skewed long-tail distribution. The empirical results revealed that general homebuyers with higher budgets and lower permanent incomes, who have purchased new houses with large amounts of space, located in downtown areas, tend to exhibit relatively higher micro PIR. Moreover, the results suggested that increasing search times or viewing additional houses cannot resolve the housing affordability problem.\r\n\r\nThe 90th quantile result indicated that homebuyers with high micro PIRs may have high budgets and low incomes, and may be purchasing houses to invest. Thus, high housing PIRs may not indicate housing affordability.-
dc.format.extent899775 bytes-
dc.format.mimetypeapplication/pdf-
dc.relationHabitat International, Vol.43, pp.41-47-
dc.subjectHousing affordability; \r\nHousing price-to-income ratio; \r\nQuantile regression-
dc.titleWhy homebuyers have a high housing affordability problem: Quantile regression analysis-
dc.typearticleen
dc.identifier.doi10.1016/j.habitatint.2014.01.013en_US
dc.doi.urihttp://dx.doi.org/10.1016/j.habitatint.2014.01.013en_US
item.grantfulltextrestricted-
item.openairetypearticle-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:期刊論文
Files in This Item:
File Description SizeFormat
41-47.pdf878.69 kBAdobe PDF2View/Open
Show simple item record

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.