TY - JOUR
T1 - Benchmarking Local Commercial Real Estate Returns
T2 - Statistics Meets Economics
AU - Peng, Liang
N1 - Publisher Copyright:
© 2017 American Real Estate and Urban Economics Association
PY - 2020/12/1
Y1 - 2020/12/1
N2 - It is challenging to estimate granular-level indices of infrequently traded assets because data can be extremely scarce—the degree of freedom can be near zero if the estimation only uses local properties. This article applies a parameter-reduction approach to U.S. commercial real estate data, and estimates metro-level total return indices by property types from 1997 to 2014. This article further evaluates the economic merits of the indices. Test results suggest that the estimated metro indices have significant explanatory power, both in- and out-of-sample, for local property returns.
AB - It is challenging to estimate granular-level indices of infrequently traded assets because data can be extremely scarce—the degree of freedom can be near zero if the estimation only uses local properties. This article applies a parameter-reduction approach to U.S. commercial real estate data, and estimates metro-level total return indices by property types from 1997 to 2014. This article further evaluates the economic merits of the indices. Test results suggest that the estimated metro indices have significant explanatory power, both in- and out-of-sample, for local property returns.
UR - http://www.scopus.com/inward/record.url?scp=85041057004&partnerID=8YFLogxK
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U2 - 10.1111/1540-6229.12229
DO - 10.1111/1540-6229.12229
M3 - Article
AN - SCOPUS:85041057004
SN - 1080-8620
VL - 48
SP - 1004
EP - 1029
JO - Real Estate Economics
JF - Real Estate Economics
IS - 4
ER -