Housing match quality and demand: What can we learn from comparing buyer characteristics?
Journal of Housing Economics, 2018, vol. 41, issue C, 184-199
Match quality, the part of housing value to the buyer which is unique for each buyer-house match, is important in several housing market matching models, but measuring it is difficult. I suggest that similarity between buyers and sellers (at the time they bought) may be used to measure a part of match quality, which is correlated with buyer characteristics. If observable characteristics of a buyer are correlated with the buyer’s preferences for housing, successive owners of houses should share characteristics. A buyer could be expected to have higher match quality if similar to the previous buyer. I use a simple matching model to show this mechanism, and test this prediction using unique data with information on two “generations” of buyers. Buyers who resemble past buyers are paying more, also when a large number of observable housing, buyer and seller characteristics are controlled for. This supports the use of buyer similarity as a proxy for match quality. I use the match quality measure to indicate how search frictions affect the estimation of housing preferences in a structural housing demand model.
JEL-codes: D83 R21 R31 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Match quality in housing transactions. What can we learn from comparing buyers and sellers? (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:jhouse:v:41:y:2018:i:c:p:184-199
Access Statistics for this article
Journal of Housing Economics is currently edited by H. O. Pollakowski
More articles in Journal of Housing Economics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().