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The Journal of Real Estate Finance and Economics

Erschienen in:

01.11.2013

Modeling Space Market Dynamics: An Illustration Using Panel Data for US Retail

verfasst von: Patric H. Hendershott, Maarten Jennen, Bryan D. MacGregor

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 4/2013

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Abstract

Real estate research has a long and extensive history of analyzing space market dynamics. Nonetheless, two areas have been under researched. Regional panels of data have been rarely analyzed. Moreover, due to data constraints, the retail market has been studied much less than other market segments. This paper addresses both of these topics through an analysis of Metropolitan Statistical Area (MSA) level panel data. Our study covers almost three decades of annual retail data for 11 of the largest MSAs of the United States. We estimate a long run rent model and use Error Correction Models for short run rent, vacancy and supply adjustments. We test for differences in local market behavior in both the long run equilibrium relationships and in the short run adjustment processes. We identify two groups of similar markets.

Vorheriger Artikel Transaction Frequency and Commercial Property

Nächster Artikel Who Says there is a High Consensus Among Analysts when Market Uncertainty is High? Some New Evidence from the Commercial Real Estate Market

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Research seems to have settled on the error correction model (ECM): see Hendershott et al. (2002a, hereafter HMT) and Englund et al. (2008a, hereafter EGHS).

Application of the ECM in panel estimation of real estate markets is limited. Hendershott et al. (2002b) and Hendershott and MacGregor (2005a, b) estimated panels of regional rents in the UK and of capitalization rates in US MSAs, respectively. Mouzakis and Richards (2007) estimated a panel of office rents in 12 European cities and Brounen and Jennen (2009a, b) estimated panels for European and US city office market rents.

To the best of our knowledge, only Hendershott et al. (2002b) have tested for differences in markets, concluding that the London region was dissimilar from the other regions.

The constancy assumption is standard in the literature on modeling space markets. The actual vacancy rate oscillates around its constant natural level depending on the real estate cycle. Therefore the estimation of vacancy rate trends is affected by the points on the cycle at the start and end of the estimation period. For the MSAs examined in this study, with one exception, the trends in the vacancy rate lie in the narrow range −0.1 % pa to +0.1 % pa.

For simplicity of presentation, we replace SU _t (R _t ) with SU _t but the assumption that supply is a function of rent remains.

Supply cannot adjust within a year as the construction period is too long and demolitions are unlikely. Instead, the adjustment is in occupied space and hence the vacancy rate. To test this assumption, we used the approach advocated by Hilber and Mayer (2009). We estimated a two-stage least squares regression for the response of supply to rent, using retail sales as the instrument. We repeated this for changes in these (logged) variables. The results offer support for our assumption.

The trends in the sales to floorspace ratio range from −1.4 % pa to +1.4 % pa. Of these 11 trends, four are insignificant at 5 %, five are significantly negative and two are significantly positive. The general pattern is a rise for the first 3 years, then a fall for 8 years, a rise for eight and then a leveling off.

This is the general form of the model that allows lags of the dependent and independent variables. In practice, we normally expect no more than one or two lags of the variables. The exception in our estimations is the change in supply. Lags of the rent error were also tested but were never significant.

We use the logs of rent and supply levels, so the log differences approximate the growth rates for these variables, but we use the levels for the vacancy rates and model the change.

Hendershott and Haurin (1988) provide an analysis of the determination of v* and summarize evidence from a number of early empirical studies on variation of office market v* across MSAs.

The input data omit overage rent, but we do not believe that this would have a significant effect on changes in market level rent over time unless the relative importance of base rent has changed over time. At the beginning of a lease a tenant agrees to a base rent and the portion of the rent that can also be driven by sales. What we use in the model is the average market rent at the city level. Rent paid within existing leases will change over time as a result of sales level and indexation; however, at the end of a contract, the rent will be adjusted again to some market level that will be driven by supply of retail space and the level of demand for retail services (sales).

Outliers are those observations whose value does not fall within an interval determined as first quartile minus 1.5 times IQR or third quartile plus 1.5 times IQR, IQR being the Inter Quartile Range or the difference between the third and first quartile observations.

Note that with an average lease length of 5 years (this is the assumed standard length), the availability rate due solely to the rolling over of leases would be 20 %; for length of 10 years, it would be 10 %. The average rates in the data for two of the MSAs is a far lower 4 %.

Estimates by the BOC show that online sales represented about four percent of total retail sales in 2009.

The normal econometric requirements of co-integration and order of integration are met throughout our estimations and are not reported here.

These were the mean and standard deviation of: real rental growth, real retail sales growth, supply growth, the vacancy rate, the real rent level, the supply level, the absorption rate, real sales/ space, and real sales/ space/ real rent. We set this analysis in the context of an option pricing framework. The value of a development, and therefore market responses, should be linked to the level and volatility of these variables.

There are two measures of development constraints (the percentage of undeveloped land and the Wharton Restriction Index) and his estimated housing supply elasticities.

Our thinking here is as follows: (1) the less undeveloped land is available, the greater would be the impact of a change in demand on rent, so the higher would be the retail sales coefficient (γ₁), so the Saiz correlation would be negative; (2) the more undeveloped land is available, the lower will be the magnitude of the impact on rent of an increase in supply so, with a negative impact, the correlation with the supply coefficient (γ₂) should be positive; (3) as the price elasticity is 1/γ₂, we expect the correlation to be negative; and (4) as the income elasticity is − γ₁/γ₂, we expect a positive correlation.

As explained at Eqs. (6)–(8), we can extract separate estimates of natural vacancy rate from the three equations. In each equation, the estimate is the negative of the ratio of the constant to the coefficient (or sum or coefficients) on the lagged vacancy rate. In the constrained system, we impose a constraint on the lagged vacancy rate coefficient in two of the equations. This ensures that the ratio of the lagged coefficient to the constant in that equation is the same as in the third equation. Similar constraints are required on each of the dummy variables representing the fixed effects. The exact formulation is available from the authors.

Both the first and second lags of the vacancy were (barely) significant in early estimations with a single long run equilibrium and without the supply coefficients allowed to vary between groups.

The importance of asymmetries in supply adjustment has been emphasized, in the context of a model of urban growth and residential housing, by Glaeser and Gyourko (2005). They show empirically across U.S. metropolitan areas that positive shocks to the local economy tend to increase population and employment more than they increase prices, whereas the opposite holds for negative shocks: prices fall more than population and employment. A key driving force in their model is a kinked supply curve with a high upward elasticity but with downward elasticity limited by depreciation.

Recall that these estimates are based on low, statistically insignificant, vacancy rate coefficients.

A striking feature of all of the shocks is how the system returns to a stable equilibrium. This underlines the robustness of the models.

The office studies are of single cities (Stockholm and London) with employment as the demand variable.

The data for the national analysis were supplied by CBRE EA.

Brounen, D., & Jennen, M. (2009a). Asymmetric properties of office rent adjustment. Journal of Real Estate Finance and Economics, 39, 336–358.CrossRef

Brounen, D., & Jennen, M. (2009b). Local office rent dynamics, a tale of ten cities. Journal of Real Estate Finance and Economics, 39, 385–402.CrossRef

Englund, P., Gunnelin, A., Hendershott, P. H., & Soderberg, B. (2008a). Adjustment in commercial property space markets: taking long-term leases and transaction costs seriously. Real Estate Economics, 36, 81–109.CrossRef

Englund, P., Gunnelin, A., Hendershott, P. H., & Soderberg, B. (2008b) Asymmetries in property space market adjustment, Paper presented at the Rotterdam School of Management real estate symposium.

Glaeser, E. L., & Gyourko, J. (2005). Urban decline and durable housing. Journal of Political Economy, 113(2), 345–375.CrossRef

Hendershott, P. H., & Haurin, D. R. (1988). Adjustments in real estate markets. AREUEA Journal, 1988–89, 343–353.CrossRef

Hendershott, P. H., & Kane, E. J. (1992). Causes and consequences of the 1980s commercial construction boom. Journal of Applied Corporate Finance, 5, 61–70.CrossRef

Hendershott, P. H., & MacGregor, B. D. (2005a). Investor rationality: evidence from UK property capitalization rates. Real Estate Economics, 30(2), 165–184.CrossRef

Hendershott, P. H., & MacGregor, B. D. (2005b). Investor rationality: an analysis of NCREIF commercial property data. Journal of Real Estate Research, 27(4), 445–475.

Hendershott, P. H., MacGregor, B. D., & Tse, R. (2002a). Estimating the rental adjustment process. Real Estate Economics, 30(2), 165–184.CrossRef

Hendershott, P. H., MacGregor, B. D., & White, M. (2002b). Explaining real commercial rents using an error correction model with panel data. Journal of Real Estate Finance and Economics, 24(1/2), 59–88.CrossRef

Hendershott, P. H., Lizieri, C. M., & MacGregor, B. D. (2010). Asymmetric adjustment in the London office market. Journal of Real Estate Finance and Economics, 41(1), 80–101.CrossRef

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Titel: Modeling Space Market Dynamics: An Illustration Using Panel Data for US Retail
verfasst von: Patric H. Hendershott
Maarten Jennen
Bryan D. MacGregor
Publikationsdatum: 01.11.2013
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 4/2013
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-013-9426-z

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