Determinants of House Prices: A Quantile Regression Approach

Abstract

OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance, but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices. To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.

Corporate governance, incentives, and tax avoidance

“…In the quantile regressions, the coefficient is … insignificant at the 0.25 quantile and higher, but is relatively large and significant for the bottom tail of the distribution suggesting that these expenditures may increase math scores for the lower part of the conditional distribution." Quantile regression has also been applied in finance and economics to assess the effects of 401(K) participation on wealth (Chernozhukov and Hansen, 2004); the determinants of house prices (Zietz et al, 2008); the determinants of gender wage differences (Garcia et al, 2001); the effect of education on women's labor market value (Buchinsky, 2002); and to evaluate value-atrisk models (Gaglianone et al, 2011).…”

Hedonic Modeling for a Growing Housing Market: Valuation of Apartments in Complexes

“…commodity, is not a simple task. The hedonic pricing method, which is frequently used in the literature on real estate evaluation, views home prices as a composition of its characteristics and tries to measure the marginal effect of the characteristics on the price (Zietz et al, 2008). The hedonic price function usually takes the form of     X P Where P is the vector of sale prices, X the matrix of explanatory variables, β the vector of regression coefficients and ε the error term.…”

Spatial Quantile Regression

“…The spatial AR version of the quantile model relies on approaches developed by Chernozhukov and Hansen (2006) and Kim and Muller (2004). The approaches have been applied to studies of house prices by Kostov (2009), Liao and Wang (2012) and Zeitz et al (2008). The studies rely on the IV approach for estimating the spatial AR model.…”