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The Journal of Real Estate Finance and Economics
Published in: The Journal of Real Estate Finance and Economics 4/2020

12-01-2019

Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models

Authors: Alex van de Minne, Marc Francke, David Geltner, Robert White

Published in: The Journal of Real Estate Finance and Economics | Issue 4/2020

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Abstract

Repeat-sales indexes are the most widely used type of transaction based property price indexes. However, such indexes are particularly prone to revision. When a new period of transaction data becomes available and is used to update the repeat-sales model, all past index values can potentially be revised. These revisions are especially problematical for commercial real estate (as compared to housing), due to the relative scarcity of transaction data and the heterogeneity of the underlying properties. From a methodological perspective, the magnitude of the revisions is a particularly useful measure of the index quality, as it directly reflects both the precision of the index and its practical usefulness in economic and business applications. This paper focuses on index revisions in thin, commercial property markets, the type of market that is most challenging. We present multiple specifications of the repeat-sales model (both existing and new), seeking to reduce revisions. We are able to reduce overall index revisions by more than 50%, compared to more traditional repeat-sales models.
previous article Detecting Bubbles in the US and UK Real Estate Markets
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Appendix
Available only for authorised users
Footnotes
1
Both use a slightly different setup than the classical (Bailey et al. 1963) method. The HPI uses a WLS approach to estimate the repeat-sales model as proposed by Case and Shiller (1987). The Moody’s/RCA CPPI uses frequency conversion as proposed by Bokhari and Geltner (2012). Since September 2017 the RCA CPPIs employ a methodology based on the current paper.
 
2
Note that βi is specified as a pair fixed effect in this paper, as this the most widely accepted form in both academic literature and in industry. However, it could also be specified as a property fixed effect. So if a property is sold 3 times, we ‘break it up’ into 2 pairs, instead of seeing it as 1 property. See Francke (2010) for the (small) difference in specification.
 
3
The metro areas are defined by Real Capital Analytics.
 
4
In fact, we also ran some additional indexes as a robustness check in “Robustness”. As such, the total number of indexes we estimate is closer to 5,000.
 
5
For example, consider Fig. 3. In both markets the crisis is clearly visible. Although the amplitude and the timing for especially the recovery is different. The SFO index started dropping 2 quarters before the LAI index. Subsequently, offices in LAI went down approximately 50%, whereas apartments in SFO ‘only’ decreased 20% in value. However, arguably the biggest difference is that at the end of the sample, the LAI index is still not above its previous peak, whereas the SFO index reached the previous peak already in 2013.
 
6
A partial exception is Gatzlaff and Geltner (1998) which estimated repeat-sales indexes of Florida commercial property based on a ridge regression methodology reflecting an a priori assumption of positively correlated ‘true’ returns.
 
7
Note that Francke and van de Minne (2017) use a similar setup. Their hierarchical repeat-sales (HRS) model has multiple stochastic log price trends with a hierarchical additive structure: One common trend using information from all sectors and markets, and target market specific trends estimated as deviations from the common trend. In the present approach we simply estimate the aggregate indexes separately and use them as explanatory variables in the granular index estimation. This reduces computing time considerably. Moreover, we found that in some cases of data scarcity, the HRS can result in highly correlated indexes.
 
8
We do provide some of the revision statistics for the aggregate indexes in “Robustness”.
 
9
In an earlier version we experimented with transaction volume, regional GDP, and unemployment in the state equation, and allowed the variance of the signal and noise component to be time-varying. For the sake of brevity and readability these results have not been included in the paper. Moreover, the results did not improve much, or not at all.
 
10
The effective sample size (ESS) is computed as follows; \( \text {ESS} = {n \over 1 + 2 {\sum }_{k = 1}^{\infty } \rho (k)} , \) where n is the number of samples and ρ(k) is the correlation at lag k. One gets a different ESS for every variable. Thus, the difference between the effective sample size and the actual sample size, gives one a measure on how independent the draws are.
 
11
The intuition behind the \(\bar {R}\) is that the chains should look alike, if the chains converged. First, the Gelman-Rubin diagnostic is computed, which calculates both the between-chain and the within-chain variance. The \(\hat {R}\) is essentially the fraction between the two, see Gelman and Rubin (1992) and Brooks and Gelman (1998) for more details. A value of 1.1 is usually used as an upper limit.
 
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Metadata
Title
Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models
Authors
Alex van de Minne
Marc Francke
David Geltner
Robert White
Publication date
12-01-2019
Publisher
Springer US
Published in
The Journal of Real Estate Finance and Economics / Issue 4/2020
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-018-9692-x

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