The Relationship between Housing Prices and Market Transparency. Evidence from the Metropolitan European Markets

ABSTRACT

This paper develops an empirical analysis quantifying the housing prices reaction to market transparency in 21 European metropolitan areas for the period 2004–2016. Market transparency is measured by the Global Real Estate Transparency Index (JLL) with its five dimensions. Applying a biannual panel EGLS model regression with fixed effects by cities as on data collected from several resources and measuring transparency using JLL index, results quantify the housing price elasticity of responses to changes in transparency. Results indicate that a decrease in transparency level is associated with an increase in house prices. The effect of transparency on the housing prices is heterogeneous in European metropolises, with most visible impacts in the Eastern metropolitan areas (Bratislava, Bucharest, Warsaw and Zagreb) but also in Western areas such as Copenhagen, Dublin and Madrid. London, Paris and Amsterdam also show how large transparency contributes to low house price increase, as the market fundamentals support.

KEYWORDS:

• Transparency
• housing prices
• metropolitan areas
• Europe

Acknowledgments

We thank Jones Lang LaSalle for giving us the whole data of global real estate transparency index (GRETI). We also thank the three anonymous referees for their very positive and deep comments which have contributed to substantially improve the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. The last publication of GRETI 2018 also includes another dimension of market transparency about the real estate environmental sustainability but due to the short coverage of the index, it is not included in this analysis.

2. Housing prices are not available at metropolitan level.

3. The statistics of house prices are based on heterogeneous data sources, methodologies and also areas considering either the city as such or also the surrounding region (EMF 2017, 130). Appendix 1 summarizes the most important aspects of the sources of the house prices data.

4. Hsiao (2003, 41–43) recommend to use panel data when the size of DB is small.

5. Log transformations represent a form of a mathematical modification to the values of a variable to improve the normality of variables (Osborne 2002). The estimated parameters (betas) are a proxy of the fundamental elasticities which capture the reactions of housing prices to market fundamentals changes.

6. Murphy (2009) sustains that regional data vary idiosyncratically and that generates more information about the determinants of housing prices than national data.

7. Cointegration panel tests table is not included here but are available under request.

8. We have to remind that transparency indexes are codified in an inverse sense, so as a value of the index = 1 means higher transparency when the value is = 5 is the lowest transparency score. So as, the positive sign of the parameter means that an increase on the transparency index value of 1% (capturing a reduction on transparency) increase the housing price in the percentage estimated by the parameter.

9. In general, the efficiency of the company management associated with financial disclosure and corporate governance would generate an impact on the prices through a weak governance associated with lack of professional management and data disclosures practices would have an (theoretical) effect increasing housing.

10. They argue that the valuations can generate mispricing, as they are used by the potential buyers or seller to establish the market prices. The availability of data produces more accuracy appraisals, and thus prices reflect more clearly the market fundamentals.