High Frequency House Price Indexes with Scarce Data

Martin E. Hoesli and Steven Bourassa

No unige:84700, Working Papers from University of Geneva, Geneva School of Economics and Management

Abstract: We show how a method that has been applied to commercial real estate markets can be used to produce high frequency house price indexes for a city and for submarkets within a city. Our application of this method involves estimating a set of annual robust repeat sales regressions staggered by start date and then undertaking an annual-to-monthly (ATM) transformation with a generalized inverse estimator. Using transactions data for Louisville, Kentucky, we show that the method substantially reduces the volatility of high frequency indexes at the city and submarket levels. We demonstrate that both volatility and the benefits from using the ATM method are related to sample size.

Keywords: House Prices; High-Frequency Price Indexes; Repeat Sales Method; Scarce Data (search for similar items in EconPapers)
JEL-codes: R31 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://luniarchidoc5.unige.ch/archive-ouverte/arc ... e:84700/ATTACHMENT01

Related works:
Journal Article: High-Frequency House Price Indexes with Scarce Data (2017)
Working Paper: High Frequency House Price Indexes with Scarce Data (2016)
Working Paper: High Frequency House Price Indexes with Scarce Data (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gnv:wpgsem:unige:84700

Access Statistics for this paper

More papers in Working Papers from University of Geneva, Geneva School of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by Jean-Blaise Claivaz ().