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Tracking Owners’ Sentiments: Subjective Home Values, Expectations and House Price Dynamics

Anthony Lepinteur and Sofie Waltl ()

No 2021-02, LISER Working Paper Series from LISER

Abstract: Economic theory predicts that expectations on future house price growth are related to the current price of a house. We test this relationship for the supply side of the secondary housing market using micro data that links individual expectations to a subjective owner estimated value (OEV). We find a strong relationship suggesting that optimistic expectations indeed imply higher OEVs as compared to neutral or pessimistic expectations. We find qualitatively and quantitatively consistent results for Italy and the US as well as for booming and gloomy years. Our results survive ample robustness checks. Since we use subjective data on house prices, we first show that OEVs are indeed a valid source to study house price dynamics by performing three types of convergent validity tests. We find that price dynamics derived by either combining OEVs and dwelling characteristics, or making use of repeatedly provided OEVs by the same owner over time reproduce objectively measured market trends strikingly well – even over decades. In contrast, OEVs and objective data tend to differ in levels – potentially due to psychological bias. These results hold for a large set of countries. We hence conclude that the “wisdom of the home-owner crowd” is sufficient to study house price dynamics but OEVs are less suited for measuring the level of market prices.

Keywords: Housing Markets; Expectations; Heterogenous Beliefs; Subjective Data; Convergent Validity (search for similar items in EconPapers)
JEL-codes: C43 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2020-02
New Economics Papers: this item is included in nep-ure
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Working Paper: Tracking Owners’ Sentiments: Subjective Home Values, Expectations and House Price Dynamics (2020) Downloads
Working Paper: Tracking Owners' Sentiments: Subjective Home Values, Expectations and House Price Dynamics (2020) Downloads
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