Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach

Abstract

In this paper, the heterogeneity of the Paris apartment market is addressed through assessing the differences in the hedonic price of housing attributes over the 2000-2006 period for various price, hence income, segments of the housing market. For that purpose, quantile regression is applied to the 20 Paris "arrondissements" as well as to the 80 neighborhoods, called "quartiers" -or quarters -(each "arrondissement" is composed of four quarters), with market segmentation being based on price deciles (deciles 1 to 9). The database includes some 159,000 sales spread over a seven year period (2000 -2006). Housing descriptors include, among other things, a price index, building age, apartment size, number of rooms and bathrooms, unit floor level, the presence of a lift and of a garage, the type of street and access to building (boulevard, square, alley, etc.) as well as a series of location dummy variables standing for both the "arrondissements" and the quarters.Findings clearly suggest that hedonic "relative" prices of several housing attributes significantly differ among deciles, although discrepancies tend to vary greatly in magnitude depending on the attribute. Among other findings, the elasticity coefficient of the size variable, which stands at 1.07 for the first price decile (cheapest units), is down to 1.03 for units belonging to the ninth one (dearest units). The number of rooms, of service rooms, of bathrooms, the housing type, the apartment floor level as well as the number of parking slots all exhibit strong implicit price fluctuations among deciles while it is less so for the building period that affect prices in a more uniform way. Finally, the lower the apartment price, the higher the potential for price appreciation over time.

Market heterogeneity, investment risk and portfolio allocation

“…The paper brings further evidence of how quantile regression enhances our understanding of the risk-return behaviour of assets and, more particularly, of how this approach may lead to a finer asset allocation. By using quantile regression, this paper extends the existing literature on hedonic models in the presence of market heterogeneity, in line with Zietz et al (2008), Farmer et al (2010), Mak et al (2010), Liao and Wang (2012), Zahirovich-Herbert and Gibler (2014) and Amédée-Manesme et al (2016).…”

“…Indeed, the evolution of housing prices in terms of return and volatility may follow dissimilar patterns depending not only on location but also on other structural dimensions such as the price quantile to which they belong. Consequently, quantile regression (QR) emerges as an ideal analytical tool for bringing out such divergent patterns (Zietz et al , 2008; Amédée-Manesme et al , 2016).…”

Energy Performance Certificates and Its Capitalization in Housing Values in Sweden

“…For a description of the method, see earlier work by [34]. For examples of recent applications of quantile regression models, see [35][36][37][38][39][40][41][42], as well as two recent papers by [43,44]. All these articles indicate that quantile regression analysis is suitable for a segmented housing market.…”

Provincial Differences on CO2 Emissions in China’s Power Industry: A Quantile Regression Approach

“…The quantile regression both deepens the understanding of the traditional regression methods, and promotes the type and application of the regression model, making the regression model more accurate and detailed when fitting the relevant statistical data. Therefore, it is increasingly widely used in various fields such as mathematics, economics, sociology, medical science, political science, and so on [27][28][29][30][31].…”