Housing Renovations and the Quantile Repeat‐Sales Price Index

Abstract

A median-based quantile estimator suffers less bias from positive outliers, such as unobserved renovations, than a standard mean-based estimator. Quantile repeat-sales estimates for single-family homes in the city of Chicago show nominal price appreciation of 68.9% between 1993 and 2002, substantially smaller than the standard approach's estimate of 77.8%. Omitting observations with building permits reduces the mean and median-based estimates by 4.4 and 1.6 percentage points. The results imply that quality improvements account for much of the rapid rise in house prices, and that a median-based quantile estimator produces a more accurate view of the price performance of a typical house.An ideal house-price index tracks the rate of price appreciation over time for a standard or representative house. 1 Using sample averages to construct the index is generally inappropriate because the house-price distribution is typically asymmetric: the average price is generally higher than the price of a typical house because of the effect of a small number of sales of high-priced homes. Nonacademic estimates of price indexes, such as those reported by the National Association of Realtors or by local newspapers, typically use the sample median sale price during each time period to construct an index. The median reflects the price of a typical house. But the characteristics of the median house in the sample of houses that have sold can change over time. If relatively large or new houses dominate sales during later periods, for example, both mean and median prices may rise faster than the price of a house with a standard set of characteristics.Academic researchers use regression methods to control for the effects of these changes in housing characteristics. One of two common approaches is to estimate a hedonic house price function.

Change in the distribution of house prices across Spanish cities

“…In relation to quantile estimation, in the context of housing, it is also easy to appreciate that the evaluations that individuals make of the physical characteristics of their home differ according to whether homes have higher or lower price per square meter. Quantile regression has also recently been used in the literature on housing economics (see McMillen and Thorsnes (2006), Coulson and McMillen (2007) and Zietz et al (2008).…”

Use of a Gini index to examine housing price heterogeneity: A quantile approach

“…Knowing the full PPSM distribution would greatly improve housing price indexes to give a complete picture of the variation in appreciation rates (McMillen and Thorsnes, 2006). As far as heterogeneity is concerned, knowing the full PPSM distribution could enhance our knowledge of the Gini index and thus facilitate testing of heterogeneity trends when house prices are either rising or falling.…”

Construção de índices de preços de imóveis para o Distrito Federal por meio de vendas repetidas e GWR

“…Sugere-se em estudos futuros que os pesquisadores interessados estimem os índices de preços via método de vendas repetidas, utilizando para isso Regressão Quantílica como McMillen & Thorsnes (2006) e Salvati et al (2012) para se corrigir quaisquer desvios devido à existência de valores extremos que poderiam afetar a equação média de regressão, por meio da estimativa da mediana.…”