Repeat Sales Regression on Heterogeneous Properties
Published online: 2 October 2010
Springer Science+Business Media, LLC 2010
Abstract This paper proposes a generalized repeat sales regression (GRSR) that
uses repeat sales from the entire market, in which properties may have
heterogeneous value appreciation processes, to estimate price indices for not only
the entire market, but also submarkets or customized portfolios of properties that
only have small numbers of value observations. Monte Carlo simulations provide
strong evidence that the GRSR indices more accurately measure the index for the
entire market as well as individual property value appreciation than conventional
RSR indices. This paper also proposes a Chi-square test to detect the heterogeneity
in property value appreciation across submarkets/portfolios, and use simulations to
show that the test is powerful in small samples. This paper finally illustrates the
application of the GRSR using a historical dataset of the Chicago housing market
from 1970 to 1986.
Keywords Repeat sales regression
Monte Carlo simulation
JEL classification C12
Real estate plays an important role in the United States economy. It generates over
28% of United States gross domestic product,
and constitutes the largest asset class
in the United States with the total value being about $20 trillion.
Due to the large
J Real Estate Finan Econ (2012) 45:804–827
National Policy Agenda 2005 (Washington, DC: Real Estate Roundtable, 2005).
Flow of Funds Accounts of the United States, Federal Reserve (various tables).
L. Peng (*)
Leeds School of Business, University of Colorado at Boulder, 419 UCB,
Boulder, CO 80309-0419, USA