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Has SARS infected the property market? Evidence from Hong Kong☆

Identifieur interne : 001222 ( Pmc/Corpus ); précédent : 001221; suivant : 001223

Has SARS infected the property market? Evidence from Hong Kong☆

Auteurs : Grace Wong

Source :

RBID : PMC:7133336

Abstract

This paper uses the 2003 Hong Kong Severe Acute Respiratory Syndrome (SARS) epidemic as a natural experiment to investigate how housing markets react to extreme events. A panel data set of large-scale housing complexes (estates) is used to exploit the cross-sectional variation in the spread of SARS to estimate the effect of the disease on real estate prices and sales. SARS risk is measured by: (1) the estate-level SARS infection rate, (2) news reports, and (3) government announcements of infections. The average price declines by 1–3 percent if an estate is directly affected by SARS, and by 1.6 percent for all estates as a result of the outbreak of the disease. A back-of-the-envelope calculation of the expected price fall under the rational asset-pricing model implies that the economic value of life consistent with the SARS-related price movement was less than $1 million. This low figure contrasts with the predictions of overreaction from psychological and behavioral economics theories. An analysis of transaction volume suggests that the absence of price overreaction is likely to be related to housing market characteristics, including transaction costs, credit constraints and loss aversion.


Url:
DOI: 10.1016/j.jue.2006.12.007
PubMed: NONE
PubMed Central: 7133336

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PMC:7133336

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<p>This paper uses the 2003 Hong Kong Severe Acute Respiratory Syndrome (SARS) epidemic as a natural experiment to investigate how housing markets react to extreme events. A panel data set of large-scale housing complexes (estates) is used to exploit the cross-sectional variation in the spread of SARS to estimate the effect of the disease on real estate prices and sales. SARS risk is measured by: (1) the estate-level SARS infection rate, (2) news reports, and (3) government announcements of infections. The average price declines by 1–3 percent if an estate is directly affected by SARS, and by 1.6 percent for all estates as a result of the outbreak of the disease. A back-of-the-envelope calculation of the expected price fall under the rational asset-pricing model implies that the economic value of life consistent with the SARS-related price movement was less than $1 million. This low figure contrasts with the predictions of overreaction from psychological and behavioral economics theories. An analysis of transaction volume suggests that the absence of price overreaction is likely to be related to housing market characteristics, including transaction costs, credit constraints and loss aversion.</p>
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<aff>Wharton School of Business, University of Pennsylvania, 1463 Steinberg-Dietrich Hall, The Wharton School, 3620 Locust Walk, Philadelphia, PA 19104-6302, USA</aff>
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<abstract>
<p>This paper uses the 2003 Hong Kong Severe Acute Respiratory Syndrome (SARS) epidemic as a natural experiment to investigate how housing markets react to extreme events. A panel data set of large-scale housing complexes (estates) is used to exploit the cross-sectional variation in the spread of SARS to estimate the effect of the disease on real estate prices and sales. SARS risk is measured by: (1) the estate-level SARS infection rate, (2) news reports, and (3) government announcements of infections. The average price declines by 1–3 percent if an estate is directly affected by SARS, and by 1.6 percent for all estates as a result of the outbreak of the disease. A back-of-the-envelope calculation of the expected price fall under the rational asset-pricing model implies that the economic value of life consistent with the SARS-related price movement was less than $1 million. This low figure contrasts with the predictions of overreaction from psychological and behavioral economics theories. An analysis of transaction volume suggests that the absence of price overreaction is likely to be related to housing market characteristics, including transaction costs, credit constraints and loss aversion.</p>
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<p>This is an update of the first chapter of my PhD thesis at Princeton University.</p>
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</back>
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