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House prices at different stages of the buying/selling process

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  • Shimizu, Chihiro
  • Nishimura, Kiyohiko G.
  • Watanabe, Tsutomu

Abstract

In constructing a housing price index, one has to make at least two important choices. The first is the choice among alternative estimation methods. The second is the choice among different data sources of house prices. The choice of the dataset has been regarded as critically important from a practical viewpoint, but has not been discussed much in the literature. This study seeks to fill this gap by comparing the distributions of prices collected at different stages of the house buying/selling process, including (1) asking prices at which properties are initially listed in a magazine, (2) asking prices when an offer for a property is eventually made and the listing is removed from the magazine, (3) contract prices reported by realtors after mortgage approval, and (4) registry prices. These four prices are collected by different parties and recorded in different datasets. We find that there exist substantial differences between the distributions of the four prices, as well as between the distributions of house attributes. However, once quality differences are controlled for, only small differences remain between the different house price distributions. This suggests that prices collected at different stages of the house buying/selling process are still comparable, and therefore useful in constructing a house price index, as long as they are quality adjusted in an appropriate manner.

Suggested Citation

  • Shimizu, Chihiro & Nishimura, Kiyohiko G. & Watanabe, Tsutomu, 2016. "House prices at different stages of the buying/selling process," Regional Science and Urban Economics, Elsevier, vol. 59(C), pages 37-53.
  • Handle: RePEc:eee:regeco:v:59:y:2016:i:c:p:37-53
    DOI: 10.1016/j.regsciurbeco.2016.04.001
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    Cited by:

    1. Diewert, W. Erwin & Shimizu, Chihiro, 2016. "Hedonic regression models for Tokyo condominium sales," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 300-315.
    2. Wang, Xiaodan & Li, Keyang & Wu, Jing, 2020. "House price index based on online listing information: The case of China," Journal of Housing Economics, Elsevier, vol. 50(C).
    3. Robert J. Hill & Norbert Pfeifer & Miriam Steurer & Radoslaw Trojanek, 2021. "Warning: Some Transaction Prices can be Detrimental to your House Price Index," Graz Economics Papers 2021-11, University of Graz, Department of Economics.
    4. Hill, Robert J. & Trojanek, Radoslaw, 2022. "An evaluation of competing methods for constructing house price indexes: The case of Warsaw," Land Use Policy, Elsevier, vol. 120(C).
    5. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2021. "Real estate listings and their usefulness for hedonic regressions," Empirical Economics, Springer, vol. 61(6), pages 3239-3269, December.
    6. Nishi, Hayato & Asami, Yasushi & Shimizu, Chihiro, 2021. "The illusion of a hedonic price function: Nonparametric interpretable segmentation for hedonic inference," Journal of Housing Economics, Elsevier, vol. 52(C).
    7. Robert J. Hill & Norbert Pfeifer & Miriam Steurer, 2020. "The Airbnb Rent-Premium and the Crowding-Out of Long-Term Rentals," Graz Economics Papers 2020-06, University of Graz, Department of Economics.
    8. Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2020. "How useful is listings data for research?," FORLand Working Papers 19 (2020), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    9. Fan, Ying & Fu, Yuqi & Yang, Zan & Chen, Ming, 2023. "Search Frictions in Rental Markets: Evidence from Urban China," Working Paper Series 23/11, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.

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    More about this item

    Keywords

    House price index; Quantile regressions; Hedonic regressions; Quality adjustment; Goodness-of-fit tests;
    All these keywords.

    JEL classification:

    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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